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[ARTICLE] Haptic-Enabled Hand Rehabilitation in Stroke Patients: A Scoping Review – Full Text

Abstract

There is a plethora of technology-assisted interventions for hand therapy, however, less is known about the effectiveness of these interventions. This scoping review aims to explore studies about technology-assisted interventions targeting hand rehabilitation to identify the most effective interventions. It is expected that multifaceted interventions targeting hand rehabilitation are more efficient therapeutic approaches than mono-interventions. The scoping review will aim to map the existing haptic-enabled interventions for upper limb rehabilitation and investigates their effects on motor and functional recovery in patients with stroke. The methodology used in this review is based on the Arksey and O’Malley framework, which includes the following stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating, summarizing, and reporting the results. Results show that using three or four different technologies was more positive than using two technologies (one technology + haptics). In particular, when standardized as a percentage of outcomes, the combination of three technologies showed better results than the combination of haptics with one technology or with three other technologies. To conclude, this study portrayed haptic-enabled rehabilitation approaches that could help therapists decide which technology-enabled hand therapy approach is best suited to their needs. Those seeking to undertake research and development anticipate further opportunities to develop haptic-enabled hand telerehabilitation platforms.

1. Introduction

Strokes are the second leading cause of death and the third leading cause of disability globally [1]. In 2010, there were 16.9 million new strokes, 33 million stroke survivors, 5.9 million stroke-related deaths, and 102 million disability-adjusted life years lost due to strokes [2]. In Canada, the prevalence of stroke is 1.2% with approximately 405,000 Canadians experiencing a stroke in 2013. This number is expected to increase from 405, 000 to between 654,000 and 726,000 in 2038 [3]. The most common post-stroke deficiency is hemiparesis of the upper contralateral limb. This condition affects the functional independence and satisfaction among 50 to 70% of patients with stroke. Approximately 80% of patients experience acute hemiparesis while 40% experience this condition chronically [4]. Recovery of functional outcomes post-stroke is heterogeneous. About 71% of patients with mild to moderate upper extremity paresis achieved some dexterity after 6 months post-stroke, while the same was true for only 60% of severely affected patients. Only 5% of people who have undergone total paralysis have achieved functional use of their arm [4].Patients who have had a stroke are often faced with permanent movement impairments that limit their ability to engage in meaningful occupations such as self-care, leisure activities, or work. Impaired hand function is among the most common effects of stroke [5]. Hand or upper limb weakness happens severely in up to 87% of patients with stroke [6,7]. The hand rehabilitation process aims to reduce spasticity, increase neuroplasticity enhance functional outcomes. Spasticity was defined by Lance et al. as “a motor disorder characterized by a velocity-dependent increase in tonic stretch reflexes with exaggerated tendon jerks, resulting from hyperexcitability of the stretch reflex” [8] (p. 485). Neuroplasticity, also known as neural plasticity, or brain plasticity, is the “capacity of neurons and neural networks in the brain to change their connections and behaviour in response to new information, sensory stimulation, development, damage, or dysfunction” [9]. While clinicians tend to profit from a substantial amount of time spent in treating spasticity and neuroplasticity after the stroke [10,11], studies show that they may not be having enough care. Compared with other patient populations, patients who have had a stroke spend more time inactive and alone or less active on rehabilitation units, more likely because of reduced sensorimotor capacity [12,13]. Hence, there seems to be a difference in practice between how much training stroke patients need and how much they receive. Therefore, it is beneficial to investigate ways to increase both the efficacy of training upper limb and hand movement. Robotic-assisted therapies are increasingly becoming available for stroke rehabilitation. The basic components of robotic-assisted therapy are (1) motorized mechanical component; (2) performance-related visual feedback; and (3) an interactive computer program that monitors progress. The ability to provide high-dosage and high-intensity interventions is a significant advantage of robotic-assisted devices [4]. A lack of devices targeting hand rehabilitation exists as most current devices target elbow and shoulder movements. Evidence shows that robotic-assisted therapy combined with virtual reality appears to be a valuable intervention for stroke rehabilitation [4].Therapists dealing with this population use strategies to improve motor behaviour to regain occupational performance. Treatment interventions such as materials-based training [14], task-related [15,16] or task-specific training [17,18] are common training methods for restoring function in the upper limb. Such training methods emphasize the patient’s active participation, the use of goal-oriented tasks or environmental features to drive motor activity, and the execution of the entire task or components of the task under different conditions. Several studies have failed to demonstrate the superiority of one type of conventional stroke training over another [19,20,21,22]. Our understanding of brain function and brain trainability is becoming more evident with identifying mirror neurons and the recent development of neuroimaging techniques. This training modality has traditionally been used in athletics in an intuitive manner [23,24] to review or reinforce the sequence of movements that make up the action to be taken. Mental practice has been shown to be effective in reducing impairment and improving functional recovery [25]. Literature shows that mental practice is an effective intervention when it is added to physical practice [25]. Although functional imaging has shown that mental practice induces similar cortical activation patterns, such interventions’ clinical efficacy in the treatment and functional recovery has yet to be demonstrated [25].Retraining a motor task can be controlled more precisely than conventional treatment approaches by using a variety of technologies such as robots (e.g., [26,27,28,29,30,31,32]), virtual reality (e.g., [33,34,35,36]) and sensor-based devices (e.g., [37,38,39]). The complex nature of the human hands and arms and the various daily activities will contribute to an approach in which specific approaches were integrated to address the diverse needs of upper limb/hand rehabilitation. Further research is needed to determine the most effective technology-assisted intervention or combination of interventions. This paper aims (i) to draw a portrait of existing haptic-enabled hand rehabilitation in stroke patients, (ii) to map the use of haptic technology to support technology-assisted therapeutic interventions, and (iii) to investigate the effects of haptic-enabled interventions on the motor and functional recovery in patients with stroke. One of the common locations to apply haptic technology to provide biofeedback is the hand (e.g., [37,38,39,40,41]). Integration of haptic technology in hand therapy plays a significant role in the interaction between the body and the objects. A better sense of touch determines the efficacy of daily life movements. Haptics can be defined as “the perception of combined tactile and kinesthetic inputs during object manipulation and exploration” [42]. It is expected that the haptic feedback increases motor and functional recovery. It is hypothesized that the more different technologies are combined with haptic technology, the better the therapeutic outcomes.[…]

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[Abstract + References] Novel Technologies in Upper Extremity Rehabilitation

Abstract

Structured and sufficient training is a key factor for successful fitting of an upper limb prosthesis. This is especially true for more advanced myoelectric control strategies, or for individuals with comorbidities that require additional treatment. With advances in technology, not only have the control strategies become more complex, but also possibilities for more tailored rehabilitation have increased. Novel rehabilitation technologies include virtual and augmented reality systems, as well as training systems relying on computers and smartphone apps. These technologies can be used within the clinical setting, enable telerehabilitation, and/or can support unsupervised home training. While most experts agree that novel rehabilitation technologies can be a good supplement for conventional therapy, one of the greatest challenges is to transfer the progress achieved in the technology-assisted realm into real-world situations and actual prosthetic function.

References

  1. 1.Grigore C. Burdea, “Virtual reality technology for the clinician,” 2015 International Conference on Virtual Rehabilitation (ICVR). Valencia. 2015: p. 1–1.  https://doi.org/10.1109/ICVR.2015.7358629.
  2. 2.Adamse C, Dekker-Van Weering MGH, van Etten-Jamaludin FS, Stuiver MM. The effectiveness of exercise-based telemedicine on pain, physical activity and quality of life in the treatment of chronic pain: a systematic review. J Telemed Telecare. 2018;24(8):511–26.CrossRefGoogle Scholar
  3. 3.Al-Jumaily A, Olivares RA. Electromyogram (EMG) driven system based virtual reality for prosthetic and rehabilitation devices. In: Proceedings of the 11th international conference on information integration and web-based applications & services. New York: ACM; 2009. p. 582–6.Google Scholar
  4. 4.Alcañiz M, Perpiña C, Baños R, Lozano JA, Montesa J, Botella C, Palacios AG, Villa H, Alozano J. A new realistic 3D body representation in virtual environments for the treatment of disturbed body image in eating disorders. CyberPsychology Behav. 2000;3:433–439.  https://doi.org/10.1089/10949310050078896.
  5. 5.Anderson F, Bischof WF. Augmented reality improves myoelectric prosthesis training. Int J Disabil Hum Dev. 2014;13:349–54.  https://doi.org/10.1515/ijdhd-2014-0327.CrossRefGoogle Scholar
  6. 6.Annett M, Anderson F, Bischof WF. Activities and evaluations for technology-based upper extremity rehabilitation. Virtual Real Enhanc Robot Syst Disabil Rehabil. 2016;307.  https://doi.org/10.4018/978-1-4666-9740-9.ch015.
  7. 7.Aprile I, Cruciani A, Germanotta M, Gower V, Pecchioli C, Cattaneo D, Vannetti F, Padua L, Gramatica F. Upper limb robotics in rehabilitation: an approach to select the devices, based on rehabilitation aims, and their evaluation in a feasibility study. Appl Sci. 2019;9(18):3920.  https://doi.org/10.3390/app9183920.CrossRefGoogle Scholar
  8. 8.Armiger RS, Vogelstein RJ. Air-Guitar Hero: a real-time video game interface for training and evaluation of dexterous upper-extremity neuroprosthetic control algorithms. Biomed Circuits Syst Conf. 2008;121–4.  https://doi.org/10.1109/biocas.2008.4696889.
  9. 9.Baker L. Neuro muscular electrical stimulation: a practical guide. 4th ed. Downey, CA: Los Amigos Research Institute; 2000.Google Scholar
  10. 10.Biddiss E, Irwin J. Active video games to promote physical activity in children and youth: a systematic review. Arch Pediatr Adolesc Med. 2010;164:664–72.  https://doi.org/10.1001/archpediatrics.2010.104.CrossRefPubMedGoogle Scholar
  11. 11.Boudreau SA, Badsberg S, Christensen SW, Egsgaard LL. Digital pain drawings: assessing touch-screen technology and 3D body schemas. Clin J Pain. 2016;32(2):139–45.  https://doi.org/10.1097/AJP.0000000000000230.CrossRefPubMedGoogle Scholar
  12. 12.Bouwsema H, van der Sluis CK, Bongers RM. The role of order of practice in learning to handle an upper-limb prosthesis. Arch Phys Med Rehabil. 2008;89:1759–64.  https://doi.org/10.1016/j.apmr.2007.12.046.CrossRefPubMedGoogle Scholar
  13. 13.Bouwsema H, van der Sluis CK, Bongers RM. Effect of feedback during virtual training of grip force control with a myoelectric prosthesis. PLoS One. 2014;9:e98301.  https://doi.org/10.1371/journal.pone.0098301.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.Burke JW, McNeill M, Charles D, Morrow P, Crosbie J, McDonough SM. Serious games for upper limb rehabilitation following stroke. In: Conference in games and virtual worlds for serious applications, 2009. VS-GAMES’09; 2009. p. 103–10.Google Scholar
  15. 15.Cleeland CS, Ryan KM. Pain assessment: global use of the Brief Pain Inventory. Ann Acad Med Singap. 1994;23(2):129–38.PubMedGoogle Scholar
  16. 16.Cottrell MA, Galea OA, O’Leary SP, Hill AJ, Russell TG. Real-time telerehabilitation for the treatment of musculoskeletal conditions is effective and comparable to standard practice: a systematic review and meta-analysis. Clin Rehabil. 2017;31(5):625–38.CrossRefGoogle Scholar
  17. 17.Csikszentmihalyi M. Toward a psychology of optimal experience. In: Flow and the foundations of positive psychology: the collected works of Mihaly Csikszentmihalyi; 2014. p. 209–26.Google Scholar
  18. 18.Dawson MR, Carey JP, Fahimi F. Myoelectric training systems. Expert Rev Med Devices. 2011;8:581–9.  https://doi.org/10.1586/erd.11.23.CrossRefPubMedGoogle Scholar
  19. 19.Deci EL, Ryan RM, Koestner R. A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol Bull. 1999;125:627–68.  https://doi.org/10.1037/0033-2909.125.6.627.CrossRefPubMedGoogle Scholar
  20. 20.Deterding S, Dixon D, Khaled R, Nacke L, Sicart M, O’Hara K. Gamification: using game design elements in non-game contexts. In: Proc 2011 Annu Conf Ext Abstr Hum Factors Comput Syst (CHI 2011); 2011. p. 2425–8.  https://doi.org/10.1145/1979742.1979575.
  21. 21.Van Dijk L, Van Der Sluis CK, Van Dijk HW, Bongers RM. Task-oriented gaming for transfer to prosthesis use. IEEE Trans Neural Syst Rehabil Eng. 2015;24(12):1384–94.  https://doi.org/10.1109/TNSRE.2015.2502424.CrossRefPubMedGoogle Scholar
  22. 22.Donaghy E, Atherton H, Hammersley V, McNeilly H, Bikker A, Robbins L, Campbell J, McKinstry B. Acceptability, benefits, and challenges of video consulting: a qualitative study in primary care. Br J Gen Pract. 2019;69(686):e586–94.  https://doi.org/10.3399/bjgp19X704141.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.Dudkiewicz I, Gabrielov R, Seiv-Ner I, Zelig G, Heim M. Evaluation of prosthetic usage in upper limb amputees. Disabil Rehabil. 2004;26:60–3.  https://doi.org/10.1080/09638280410001645094.CrossRefPubMedGoogle Scholar
  24. 24.Ferrer-Garcia M, Gutiérrez-Maldonado J, Riva G. Virtual reality based treatments in eating disorders and obesity: a review. J Contemp Psychother. 2013;43(4):207–21.  https://doi.org/10.1007/s10879-013-9240-1.CrossRefGoogle Scholar
  25. 25.Flores E, Tobon G, Cavallaro E, Cavallaro FI, Perry JC, Keller T. Improving patient motivation in game development for motor deficit rehabilitation. In: Proceedings of the 2008 international conference on advances in computer entertainment technology; 2008. p. 381–4.Google Scholar
  26. 26.Gusman J, Mastinu E, Ortiz-Catalan M. Evaluation of computer-based target achievement tests for myoelectric control. IEEE J Transl Eng Heal Med. 2017;5:1–10.  https://doi.org/10.1109/JTEHM.2017.2776925.CrossRefGoogle Scholar
  27. 27.Halton J. Virtual rehabilitation with video games: a new frontier for occupational therapy. Occup Ther Now. 2008;10:12–4.Google Scholar
  28. 28.Handelzalts JE, Ben-Artzy-Cohen Y. The draw-a-person test and body image. Rorschachiana. 2014;35(1):3–22.  https://doi.org/10.1027/1192-5604/a000042.CrossRefGoogle Scholar
  29. 29.Hargrove L, Losier Y, Lock B, Englehart K, Hudgins B. A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment. In: Annu Int Conf IEEE Eng Med Biol—Proc; 2007. p. 4842–5.  https://doi.org/10.1109/IEMBS.2007.4353424.
  30. 30.Herz NB, Mehta SH, Sethi KD, Jackson P, Hall P, Morgan JC. Nintendo Wii rehabilitation (“Wii-hab”) provides benefits in Parkinson’s disease. Park Relat Disord. 2013;19:1039–42.  https://doi.org/10.1016/j.parkreldis.2013.07.014.CrossRefGoogle Scholar
  31. 31.Hussaini A, Kyberd P. Refined clothespin relocation test and assessment of motion. Prosthet Orthot Int. 2016;41(3):294–302.  https://doi.org/10.1177/0309364616660250.CrossRefPubMedGoogle Scholar
  32. 32.Intrinsic Motivation Inventory. Intrinsic Motivation Inventory (IMI). Intrinsic Motiv Invent Scale Descr. 1994;1–3. www.selfdeterminationtheory.org.
  33. 33.Jensen TS, Krebs B, Nielsen J, Rasmussen P. Phantom limb, phantom pain and stump pain in amputees during the first 6 months following limb amputation. Pain. 1983;17(3):243–56.  https://doi.org/10.1007/BF01402796.CrossRefPubMedGoogle Scholar
  34. 34.Jensen TS, Krebs B, Nielsen J, Rasmussen P. Non-painful phantom limb phenomena in amputees: incidence, clinical characteristics and temporal course. Acta Neurol Scand. 1984;70:407–14.  https://doi.org/10.1111/j.1600-0404.1984.tb00845.x.CrossRefPubMedGoogle Scholar
  35. 35.Johnson SS, Mansfield E. Prosthetic training: upper limb. Phys Med Rehabil Clin N Am. 2014;25(1):133–51.CrossRefGoogle Scholar
  36. 36.Kuiken TA, Miller LA, Turner K, Hargrove LJ. A comparison of pattern recognition control and direct control of a multiple degree-of-freedom transradial prosthesis. IEEE J Transl Eng Heal Med. 2016;4:2100508.  https://doi.org/10.1109/JTEHM.2016.2616123.CrossRefGoogle Scholar
  37. 37.Lang CE, Macdonald JR, Reisman DS, Boyd L, Kimberley TJ, Schindler-ivens SM, Hornby TG, Ross SA, Scheets PL. Observation of amounts of movement practice provided during stroke rehabilitation. Arch Phys Med Rehabil. 2010;90:1692–8.  https://doi.org/10.1016/j.apmr.2009.04.005.Observation.CrossRefGoogle Scholar
  38. 38.Letosa-Porta A, Ferrer-Garcia M, Gutiérrez-Maldonado J. A program for assessing body image disturbance using adjustable partial image distortion. Behav Res Methods. 2005;37(4):638–43.  https://doi.org/10.3758/BF03192734.CrossRefPubMedGoogle Scholar
  39. 39.Levin MF, Weiss PL, Keshner EA. Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles. Phys Ther. 2015;95:415–25.CrossRefGoogle Scholar
  40. 40.Lloréns R, Alcañiz M, Colomer C, Gil-Gomez J-A, Llorens R, Alcaniz M, Colomer C. Effectiveness of a Wii balance board-based system (eBaViR) for balance rehabilitation: a pilot randomized clinical trial in patients with acquired brain injury. J Neuroeng Rehabil. 2011;8:30.  https://doi.org/10.1186/1743-0003-8-30.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.Lohse K, Shirzad N, Verster A, Hodges N. Video games and rehabilitation: using design principles to enhance engagement in physical therapy. J Neurol Phys Ther. 2013;37(4):166–75.  https://doi.org/10.1097/NPT.0000000000000017.CrossRefPubMedGoogle Scholar
  42. 42.Lohse K, Shirzad N, Verster A, Hodges N, der Loos HFM V. Video Games and Rehabilitation. J Neurol Phys Ther. 2013;37:166–75.  https://doi.org/10.1097/NPT.0000000000000017.CrossRefPubMedGoogle Scholar
  43. 43.Mathiowetz V, Volland G, Kashman N, Weber K. Adult norms for the box and block test of manual dexterity. Am J Occup Ther. 1985;39:386–91.  https://doi.org/10.5014/ajot.39.6.386.CrossRefPubMedGoogle Scholar
  44. 44.Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health. 2011;26:1479–98.  https://doi.org/10.1080/08870446.2010.540664.CrossRefPubMedGoogle Scholar
  45. 45.Murray CD, Pettifer S, Howard T, Patchick EL, Caillette F, Kulkarni J, Bamford C. The treatment of phantom limb pain using immersive virtual reality: three case studies. Disabil Rehabil. 2007;29:1465–9.  https://doi.org/10.1080/09638280601107385.CrossRefPubMedGoogle Scholar
  46. 46.Noble D, Price DB, Gilder R. Psychiatric disturbances following amputation. Am J Psychiatry. 1954;110:609–13.  https://doi.org/10.1176/ajp.110.8.609.CrossRefPubMedGoogle Scholar
  47. 47.Oppenheim H, Armiger RS, Vogelstein RJ. WiiEMG: a real-time environment for control of the Wii with surface electromyography. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS); 2010. p. 957–60.Google Scholar
  48. 48.Ortiz-Catalan M, Gudmundsdottir RA, Kristoffersen MB, Zepeda-Echavarria A, Caine-Winterberger K, Kulbacka-Ortiz K, Widehammar C, Eriksson K, Stockselius A, Ragnö C, Pihlar Z, Burger H, Hermansson L. Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain: a single group, clinical trial in patients with chronic intractable phantom limb pain. Lancet. 2016;388:2885–94.  https://doi.org/10.1016/S0140-6736(16)31598-7.CrossRefPubMedGoogle Scholar
  49. 49.Ottobock. MyoBoy upgrade/exchange | Myo Software. 2016. https://professionals.ottobockus.com/Prosthetics/Upper-Limb-Prosthetics/Myo-Hands-and-Components/Myo-Software/MyoBoy-Upgrade-Exchange/p/757M11~5X-CHANGE. Accessed 1 Sept 2017.
  50. 50.Ottobock. PAULA 1.2 | Myo Software. 2016. https://professionals.ottobockus.com/Prosthetics/Upper-Limb-Prosthetics/Myo-Hands-and-Components/Myo-Software/PAULA-1-2/p/646C52~5V1~82. Accessed 1 Sept 2017.
  51. 51.Ottobock. Myo Plus App. 2020. https://www.ottobock.com/en/apps/myoplusapp/myo-plus-app-de.html. Accessed 20 May 2020.
  52. 52.Prahm C, Bauer K, Sturma A, Hruby L, Pittermann A, Aszmann O. 3D body image perception and pain visualization tool for upper limb amputees. In: IEEE proceedings on serious games and applications for health, Kyoto; 2019. p. 1–5.Google Scholar
  53. 53.Prahm C, Kayali F, Aszmann O. MyoBeatz: using music and rhythm to improve prosthetic control in a mobile game for health. In: IEEE proceedings on serious games and applications for health, Kyoto; 2019. p. 1–6.Google Scholar
  54. 54.Prahm C, Kayali F, Sturma A, Aszmann O. Recommendations for games to increase patient motivation during upper limb amputee rehabilitation. In: Converging clinical and engineering research on neurorehabilitation {II}. Cham: Springer; 2017. p. 1157–61.CrossRefGoogle Scholar
  55. 55.Prahm C, Kayali F, Sturma A, Aszmann O. PlayBionic: game-based interventions to encourage patient engagement and performance in prosthetic motor rehabilitation. PM&R. 2018;10:1252–60.  https://doi.org/10.1016/j.pmrj.2018.09.027.CrossRefGoogle Scholar
  56. 56.Prahm C, Kayali F, Vujaklija I, Sturma A, Aszmann O. Increasing motivation, effort and performance through game-based rehabilitation for upper limb myoelectric prosthesis control. In: 2017 International conference on virtual rehabilitation (ICVR). Montreal, CA: IEEE; 2017. p. 1–6.Google Scholar
  57. 57.Prahm C, Schulz A, Paaben B, Schoisswohl J, Kaniusas E, Dorffner G, Hammer B, Aszmann O. Counteracting electrode shifts in upper-limb prosthesis control via transfer learning. IEEE Trans Neural Syst Rehabil Eng. 2019;27(5):956–62.  https://doi.org/10.1109/TNSRE.2019.2907200.CrossRefPubMedGoogle Scholar
  58. 58.Prahm C, Sturma A, Kayali F, Mörth E, Aszmann O. Smart Rehab: app-based rehabilitation training for upper extremity amputees—case report. Handchir Mikrochir Plast Chir. 2018;50:1–8.  https://doi.org/10.1055/a-0747-6037.CrossRefGoogle Scholar
  59. 59.Prahm C, Sturma A, Mörth E, Aszmann O. Interactive mobile training app after nerve transfer or amputation of the upper extremity. In: Journal of Hand Surgery, Federation of European Societies for Surgery of the Hand (FESSH), vol. 43. Copenhagen: Sage; 2018. p. 198–9.Google Scholar
  60. 60.Prahm C, Vujaklija I, Kayali F, Purgathofer P, Aszmann OC. Game-based rehabilitation for myoelectric prosthesis control. JMIR Ser Games. 2017;5:13.  https://doi.org/10.2196/games.6026.CrossRefGoogle Scholar
  61. 61.Price DD, McGrath PA, Rafii A, Buckingham B. The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain. 1983;17(1):45–56.  https://doi.org/10.1016/0304-3959(83)90126-4.CrossRefPubMedGoogle Scholar
  62. 62.Rand D, Yacoby A, Weiss R, Reif S, Malka R, Weingarden H, Zeilig G. Home-based self-training using video-games: preliminary data from a randomised controlled trial. In: Virtual Rehabil Proc (ICVR), 2015 Int Conf; 2015. p. 86–91.  https://doi.org/10.1109/ICVR.2015.7358588.
  63. 63.Raymer R. Gamification: using game mechanics to enhance eLearning. 2011. https://elearnmag.acm.org/featured.cfm?aid=2031772. Accessed 30 Aug 2019.
  64. 64.Reinkensmeyer DJ, Housman SJ. “If I can’t do it once, why do it a hundred times?”: connecting volition to movement success in a virtual environment motivates people to exercise the arm after stroke. In: 2007 virtual rehabilitation. IEEE; 2007. p. 44–8.Google Scholar
  65. 65.Resnik L, Etter K, Klinger SL, Kambe C. Using virtual reality environment to facilitate training with advanced upper-limb prosthesis. J Rehabil Res Dev. 2011;48(6):707–18.CrossRefGoogle Scholar
  66. 66.Roche AD, Vujaklija I, Amsüss S, Sturma A, Göbel P, Farina D, Aszmann OC. A structured rehabilitation protocol for improved multifunctional prosthetic control: a case study. J Vis Exp. 2015;(105):e52968.  https://doi.org/10.3791/52968.
  67. 67.la Rosa R, Alonso A, de la Rosa S, Abasolo D. Myo-Pong: a neuromuscular game for the UVa-Neuromuscular training system platform. In: 2008 virtual rehabilitation; 2008. p. 61.Google Scholar
  68. 68.Rush KL, Hatt L, Janke R, Burton L, Ferrier M, Tetrault M. The efficacy of telehealth delivered educational approaches for patients with chronic diseases: a systematic review. Patient Educ Couns. 2018;101(8):1310–21.CrossRefGoogle Scholar
  69. 69.Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55:68–78.  https://doi.org/10.1037/0003-066X.55.1.68.CrossRefPubMedGoogle Scholar
  70. 70.Seagal I, Morin E. A virtual training environment for prosthetic control. In: CMBES proceedings 39, Calgary, AB; 2016. p. 1–4.Google Scholar
  71. 71.Shani M, Feldman Y, Chared M. ReAbility online. http://www.reabilityonline.com/. Accessed 15 May 2020.
  72. 72.Simon AM, Hargrove LJ, Lock BA, Kuiken TA. Target achievement control test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. J Rehabil Res Dev. 2011;48:619.  https://doi.org/10.1682/JRRD.2010.08.0149.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.Smurr LM, Gulick K, Yancosek K, Ganz O. Managing the upper extremity amputee: a protocol for success. J Hand Ther. 2008;21:160–75.; ; quiz 176.  https://doi.org/10.1197/j.jht.2007.09.006.CrossRefPubMedGoogle Scholar
  74. 74.Solutions Ag. Navigate pain. 2017. https://aglancesolutions.com/. Accessed 4 Apr 2017.
  75. 75.Spyridonis F, Ghinea G. 2D vs. 3D pain visualization: User preferences in a spinal cord injury cohort. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics); 2011Google Scholar
  76. 76.Spyridonis F, Ghinea G. 3-D pain drawings and seating pressure maps: relationships and challenges. IEEE Trans Inf Technol Biomed. 2011;15(3):409–15.  https://doi.org/10.1109/TITB.2011.2107578.CrossRefPubMedGoogle Scholar
  77. 77.Stubblefield KA, Miller LA, Lipschutz RD, Kuiken TA. Occupational therapy protocol for amputees with targeted muscle reinnervation. J Rehabil Res Dev. 2009;46:481.  https://doi.org/10.1682/JRRD.2008.10.0138.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.Sturma A, Goebel P, Herceg M, Gee N, Roche A, Fialka-Moser V, Aszmann O. Advanced rehabilitation for amputees after selective nerve transfers: EMG-guided training and testing. In: Jensen W, Andersen OK, Akay M, editors. Replace, repair, restore, relieve and bridging clinical and engineering solutions in neurorehabilitation. Cham: Springer; 2014. p. 169–77.Google Scholar
  79. 79.Sturma A, Hruby LA, Prahm C, Mayer JA, Aszmann OC. Rehabilitation of upper extremity nerve injuries using surface EMG biofeedback: protocols for clinical application. Front Neurosci. 2018;12:906.  https://doi.org/10.3389/fnins.2018.00906.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.Sturma A, Roche AD, Goebel P, Herceg M, Ge N, Fialka-Moser V, Aszmann O. A surface EMG test tool to measure proportional prosthetic control. Biomed Tech. 2015;60:207–13.  https://doi.org/10.1515/bmt-2014-0022.CrossRefGoogle Scholar
  81. 81.Tabor A, Bateman S, Scheme E, Flatla DR, Gerling K. Designing game-based myoelectric prosthesis training. In: CHI 2017, Denver, CO; 2017. p. 1–12.Google Scholar
  82. 82.Tatla SK, Shirzad N, Lohse KR, Virji-Babul N, Hoens AM, Holsti L, Li LC, Miller KJ, Lam MY, Van der Loos HFM. Therapists’ perceptions of social media and video game technologies in upper limb rehabilitation. JMIR Ser Games. 2015;3:e2.  https://doi.org/10.2196/games.3401.CrossRefGoogle Scholar
  83. 83.Touch Bionics. Biosim-i. In: MA01178, Issue 2. 2014. http://www.touchbionics.com/sites/default/files/files/biosim-i_my i-limb datasheet June 2014.pdf. Accessed 1 Sept 2017.
  84. 84.Össur, i-limb Mobile Apps by Össur. http://www.ossur.com/en-gb/professionals/apps/i-limb-mobile-apps. Accessed Nov. 2020.
  85. 85.Touch Bionics Inc. my i-limb—Android Apps on Google Play. 2020. https://play.google.com/store/apps/details?id=com.touchbionics.myilimb.app. Accessed 22 May 2020.
  86. 86.Treleaven J, Battershill J, Cole D, Fadelli C, Freestone S, Lang K, Sarig-Bahat H. Simulator sickness incidence and susceptibility during neck motion-controlled virtual reality tasks. Virtual Real. 2015;19:267–75.  https://doi.org/10.1007/s10055-015-0266-4.CrossRefGoogle Scholar
  87. 87.Vujaklija I, Roche AD, Hasenoehrl T, Sturma A, Amsuess S, Farina D, Aszmann OC. Translating research on myoelectric control into clinics—are the performance assessment methods adequate? Front Neurorobot. 2017;11:1–7.  https://doi.org/10.3389/fnbot.2017.00007.CrossRefGoogle Scholar
  88. 88.Wheaton LA. Neurorehabilitation in upper limb amputation: understanding how neurophysiological changes can affect functional rehabilitation. J Neuroeng Rehabil. 2017;14(1):41.CrossRefGoogle Scholar
  89. 89.Williamson A, Hoggart B. Pain: a review of three commonly used pain rating scales. J Clin Nurs. 2005;14(7):798–804.CrossRefGoogle Scholar
  90. 90.Winslow BD, Ruble M, Huber Z. Mobile, game-based training for myoelectric prosthesis control. Front Bioeng Biotechnol. 2018;6:1–8.  https://doi.org/10.3389/fbioe.2018.00094.CrossRefGoogle Scholar
  91. 91.Woodward RB, Hargrove LJ. Adapting myoelectric control in real-time using a virtual environment. J Neuroeng Rehabil. 2019;16:11.  https://doi.org/10.1186/s12984-019-0480-5.CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.American Occupational Therapy Association. Am J Occup Ther. 2018;72:7212410059p1.  https://doi.org/10.5014/ajot.2018.72S219.
  93. 93.Alan L, Karen F, Lesley H, Diane M, Chris P. World confederation of physical therapy. In: Report, WCPT/INPTRA Digit. Pract. Final; 2019.Google Scholar

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[Abstract] Application of AR and VR in Hand Rehabilitation: A Systematic Review

Highlights

•The human hand accounts for a third of all work-related accidents.

•Feedback, challenge and increased difficulty are motivators of patients’ adherence.

•Leap Motion Controller and haptic gloves can be integrated into the home setting.

•AR/VR technologies can be used as a complement to conventional therapies.

•Patients can benefit from the use of AR or VR interventions for hand rehabilitation.

Abstract

Background

The human hand is the part of the body most frequently injured in work related accidents, accounting for a third of all accidents at work and often involving surgery and long periods of rehabilitation. Several applications of Augmented Reality (AR) and Virtual Reality (VR) have been used to improve the rehabilitation process. However, there is no sound evidence about the effectiveness of such applications nor the main drivers of therapeutic success.

Objectives

The objective of this study was to review the efficacy of AR and VR interventions for hand rehabilitation.

Methods

A systematic search of publications was conducted in October 2019 in IEEE Xplore, Web of Science, Cochrane library, and PubMed databases. Search terms were: (1) video game or videogame, (2) hand, (3) rehabilitation or therapy and (4) VR or AR. Articles were included if (1) were written in English, (2) were about VR or AR applications, (3) were for hand rehabilitation, (4) the intervention had tests on at least ten patients with injuries or diseases which affected hand function and (5) the intervention had baseline or intergroup comparisons (AR or VR intervention group versus conventional physical therapy group). PRISMA protocol guidelines were followed to filter and assess the articles.

Results

From the eight selected works, six showed improvements in the intervention group, and two no statistical differences between groups. We were able to identify motivators of patients’ adherence, namely real-time feedback to the patients, challenge, and increased individualized difficulty. Automated tracking, easy integration in the home setting and the recording of accurate metrics may increase the scalability and facilitate healthcare professionals’ assessments.

Conclusions

This systematic review provided advantages and drivers for the success of AR/VR application for hand rehabilitation. The available evidence suggests that patients can benefit from the use of AR or VR interventions for hand rehabilitation.

Graphical abstract

Source: https://www.sciencedirect.com/science/article/abs/pii/S1532046420302136

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[Abstract + References] Virtual and Augmented Reality Platform for Cognitive Tele-Rehabilitation Based System – Conference paper

Abstract

Virtual and Augmented Reality systems have been increasingly studied, becoming an important complement to traditional therapy as they can provide high-intensity, repetitive and interactive treatments. Several systems have been developed in research projects and some of these have become products mainly for being used at hospitals and care centers. After the initial cognitive rehabilitation performed at rehabilitation centers, patients are obliged to go to the centers, with many consequences, as costs, loss of time, discomfort and demotivation. However, it has been demonstrated that patients recovering at home heal faster because surrounded by the love of their relatives and with the community support.

References

  1. 1.Aruanno, B., Garzotto, F., Rodriguez, M.C.: HoloLens-based mixed reality experiences for subjects with alzheimer’s disease. In: Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter (CHItaly 2017), Article 15, 9 p. (2017)Google Scholar
  2. 2.Bozgeyikli, L., Raij, A., Katkoori, S., Alqasemi, R.: A survey on virtual reality for individuals with autism spectrum disorder: design considerations. IEEE Trans. Learn. Technol. 11, 133–151 (2018)CrossRefGoogle Scholar
  3. 3.Cameron, C., et al.: Hand tracking and visualization in a virtual reality simulation, pp. 127–132, April 2011Google Scholar
  4. 4.American Psychiatric Association Diagnostic: Statistical manual of mental disorders. American psychiatric pub. (2013)Google Scholar
  5. 5.Gelsomini, M., Garzotto, F., Matarazzo, V., Messina, N., Occhiuto, D.: Creating social stories as wearable hyper-immersive virtual reality experiences for children with neurodevelopmental disorders. In: Proceedings of the 2017 Conference on Interaction Design and Children (IDC 2017), pp. 431–437 (2017)Google Scholar
  6. 6.Gelsomini, M., Garzotto, F., Montesano, D., Occhiuto, D.: Wildcard: a wearable virtual reality storytelling tool for children with intellectual developmental disability. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Orlando, FL, pp. 5188–5191 (2016)Google Scholar
  7. 7.Guna, J., Jakus, G., Pogacnik, M., Tomazic, S., Sodnik, J.: An analisis of the precision and reliability of the leap motion sensor and its suitability for static and diynamic tracking. Sensors 14, 3702–3720 (2014)CrossRefGoogle Scholar
  8. 8.Josman, N., Ben-Chaim, H.M., Friedrich, S., Weiss, P.L.: Effectiveness of virtual reality for teaching street-crossing skills to children and adolescents with autism. Int. J. Disabil. Hum. Dev. 49–56 (2011)Google Scholar
  9. 9.Aspoc Onlus (2020). http://www.aspoc.it//. Accessed 04 Apr 2020

Source: https://link.springer.com/chapter/10.1007/978-3-030-58796-3_17

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[ARTICLE] Acceptability of a Mobile Phone–Based Augmented Reality Game for Rehabilitation of Patients With Upper Limb Deficits from Stroke: Case Study – Full Text

ABSTRACT

Background: Upper limb functional deficits are common after stroke and result from motor weakness, ataxia, spasticity, spatial neglect, and poor stamina. Past studies employing a range of commercial gaming systems to deliver rehabilitation to stroke patients provided short-term efficacy but have not yet demonstrated whether or not those games are acceptable, that is, motivational, comfortable, and engaging, which are all necessary for potential adoption and use by patients.

Objective: The goal of the study was to assess the acceptability of a smartphone-based augmented reality game as a means of delivering stroke rehabilitation for patients with upper limb motor function loss.

Methods: Patients aged 50 to 70 years, all of whom experienced motor deficits after acute ischemic stroke, participated in 3 optional therapy sessions using augmented reality therapeutic gaming over the course of 1 week, targeting deficits in upper extremity strength and range of motion. After completion of the game, we administered a 16-item questionnaire to the patients to assess the game’s acceptability; 8 questions were answered by rating on a scale from 1 (very negative experience) to 5 (very positive experience); 8 questions were qualitative.

Results: Patients (n=5) completed a total of 23 out of 45 scheduled augmented reality game sessions, with patient fatigue as the primary factor for uncompleted sessions. Each patient consented to 9 potential game sessions and completed a mean of 4.6 (SE 1.3) games. Of the 5 patients, 4 (80%) completed the questionnaire at the end of their final gaming session. Of note, patients were motivated to continue to the end of a given gaming session (mean 4.25, 95% CI 3.31-5.19), to try other game-based therapies (mean 3.75, 95% CI 2.81-4.69), to do another session (mean 3.50, 95% CI 2.93-4.07), and to perform other daily rehabilitation exercises (mean 3.25, 95% CI 2.76-3.74). In addition, participants gave mean scores of 4.00 (95% CI 2.87-5.13) for overall experience; 4.25 (95% CI 3.31-5.19) for comfort; 3.25 (95% CI 2.31-4.19) for finding the study fun, enjoyable, and engaging; and 3.50 (95% CI 2.52-4.48) for believing the technology could help them reach their rehabilitation goals. For each of the 4 patients, their reported scores were statistically significantly higher than those generated by a random sampling of values (patient 1: P=.04; patient 2: P=.04; patient 4: P=.004; patient 5: P=.04).

Conclusions: Based on the questionnaire scores, the patients with upper limb motor deficits following stroke who participated in our case study found our augmented reality game motivating, comfortable, engaging, and tolerable. Improvements in augmented reality technology motivated by this case study may one day allow patients to work with improved versions of this therapy independently in their own home. We therefore anticipate that smartphone-based augmented reality gaming systems may eventually provide useful postdischarge self-treatment as a supplement to professional therapy for patients with upper limb deficiencies from stroke.

Introduction

Background

Stroke induces a variety of functional impairments, as well as pain and other ailments, depending on its type and location [1]. Common deficits associated with ischemic stroke include motor function, spatial neglect, and psychological changes [1]. Motor function deficits after stroke often include partial or total loss of function of the upper or lower limbs on a given side, with associated muscle weakness, poor stamina, lack of muscle control, and even paralysis [2]. These deficits impact the patient’s independent lifestyle and decrease their performance of activities of daily living [1]. According to the National Institute of Neurological Disorders and Stroke, the most important part of rehabilitation programs is “carefully directed, well-focused, repetitive practice [3].”

Prior Work

Patients who engage in rigorous, time-intensive, and challenging therapeutic exercises after ischemic stroke tend to experience greater functional recovery, while if ignored or insufficiently treated, impairments may remain [4,5]. The dosage of motor skill practice correlates to the extent of motor recovery following a stroke [4]. In addition, the type of therapy delivered relative to patient’s impairment determines outcomes after therapy. For example, for those who have upper limb motor impairment, best therapeutic practice modifies the prescribed exercises as the patient’s symptoms evolve [5,6]. Regrettably, patients report their experiences of conventional repetitive stroke rehabilitation therapies as tedious and difficult to hold their interest, which conflicts with the fact that patient motivation is often required to obtain good clinical outcomes [710].

Rehabilitation doctors and medical staff, therefore, face a significant problem: how can they provide high intensity therapy in large quantities for upper limb impairments with this seemingly intrinsic motivational deficit? Especially problematic are patient’s therapeutic needs after their discharge from the hospital—their therapeutic needs still exist, but medical staff have substantially reduced access to the patient to provide targeted care. Given the difficulty of this problem, an insufficient percentage of patients regain the full functional potential of their upper limb after ischemic stroke [11]. This regrettable outcome motivates an ongoing search for new therapeutic approaches that provide acceptable (motivational, comfortable, and engaging) experiences, hence, effective therapy, especially at the patient’s home. 

Use of commercial augmented reality devices has found recent application in stroke rehabilitation using existing expensive commercial headsets [4,617]. However, there are few studies that assay the acceptability of augmented reality gaming system–based patient rehabilitation after stroke [10,12,1719], and then, only in a cursory fashion. For example, 30 patients recovering from stroke were surveyed for their opinions on game-based rehabilitation, and the researchers concluded that though games for patients recovering from stroke existed, they were primarily designed for efficacy, not entertainment [10]; they suggest investing in a single, affordable gaming platform for patient rehabilitation after stroke that also focuses on entertainment and provides diverse gaming content [10]. Augmented reality technology and an upper-limb assistive device were tested on 3 individuals recovering from stroke for 6 weeks, and the study reported that both the user and therapist believed that their augmented reality environment was user friendly due to the lightness of the assistive devices and the simplicity of set-up [18]. Finally, a study of 4 patients recovering from stroke who were exposed to several gaming platforms reported that manually adjusting the difficulty of games to provide a challenge and creating games with deeper story lines helped the patients stay motivated to perform their gaming exercises [17]. To the best of our knowledge, our case study is the first of its kind that analyzes the opinions of patients recovering from stroke regarding the problems of current augmented reality–specific game-based rehabilitation systems to provides insight into future designs of augmented reality game-based stroke rehabilitation systems. Augmented reality, provided by one of a variety of device designs, represents one such approach. Augmented reality projects a live camera view of a user’s environment and computer-generated objects with a variety of properties—movement and sound, typically. As an example, Pokémon Go, a smartphone-based augmented reality game, has had documented success sustaining the interest of users for extended periods of time while consistently increasing their physical activity [13], making augmented reality a prime candidate for facilitating otherwise tedious therapy.

Hypothesis

Since patient motivation often drives a larger dosage of rehabilitation therapy, hence, improved clinical outcomes [20,21], we hypothesized that augmented reality deployed on a relatively inexpensive and readily available platform—a smartphone—could provide a motivational, comfortable, and engaging rehabilitation experience. To test this hypothesis, we first developed a candidate rehabilitation game on a smartphone that could encourage a patient’s hand motions through use of simple visual cues with a custom-made app. We then asked patients with acute upper-motor stroke to use this system and report their experiences via a questionnaire that assayed the acceptability of the game in terms of motivation to continue to play, comfort, and engagement.

Methods

Overview

This acceptability study was conducted at Harborview Medical Center in Seattle, Washington from November 2018 to March 2019. Inpatients who were recovering from an acute ischemic stroke participated and provided consent. These patients had impaired strength as determined by physical and occupational therapists. To be included in the study, they had to have at least antigravity strength in deltoid or biceps muscles as well as the ability to perform internal and external shoulder rotations. All patients in this study had a Medical Research Council manual muscle score of 3 or 4 in the affected limb.

Intervention

We designed and built an augmented reality game using Unity (Unity Technologies) that is deployable on any modern smartphone with a camera (Table 1 and Figure 1). The game presents users with a view of an augmented reality dolphin swimming under the ocean with the task of capturing fish and feeding turtles, worn on the hand associated with the upper-limb deficit (Multimedia Appendix 1). To experience the game, patients wore an augmented reality headset, which did not obscure the camera mounted on the phone, and a custom device on their hand. We used two headsets—the Google Daydream headset, which required us to remove the front panel that held the phone in place, and the Merge augmented reality/virtual reality headset, which did not require any modification (Figure 1). The game also required users to place the hand associated with their motor deficits within a padded box that replaced their hand as seen in augmented reality with a dolphin (Figure 1). Finally, we required the user to look at a complex landscape through their headset while wearing the padded box and while playing the game. Instead of holding the phone, the headset supported the phone for the user. We built customized controllers with different interior sizes that changed the effective grip strength of the controller; this was important because our patients’ ability to hold the controllers varied. Viewing the complex landscape through the augmented reality system caused our software to create a seascape that contained a turtle, fish, and other underwater flora and fauna (Multimedia Appendix 1). Successful placement of the dolphin over a fish allowed the dolphin to capture the fish. Placement of the dolphin plus fish over the turtle allowed the user to feed the turtle, thereby winning points.

Notably, we used the TeamViewer (TeamViewer AG) app to project the screen view of the patient from the phone to a laptop, so we could see the patient’s view with, however, the complex landscape was also projected in the background, so we could check the viewer’s alignment with the landscape while they played (Figure 1).

Set-up of the game, to ensure that system function was verified, occurred prior to patients using the system. Patients followed verbal directions and instructions from study staff on how to use the system, facilitated by demonstration of the game using the TeamViewer app. Examples of directions included how to start the game, the actions required to pick up the fish, and how to colocate the dolphin plus fish with the turtle for point accumulation. Some patients required physical assistance to adjust the view of the environment. Examples of physical assistance included moving the patient’s chair or wheelchair closer or farther away from the images recognized by the camera (Figure 1).

Table 1. Vuforia compatible mobile devices.
Figure 1. (A) phone: Asus Zenfone 2, phone operating system: Android 7 Nougat, Unity version: 2018.2.10, developer operating system: Windows 10; (B) headsets: Google Daydream (left) Merge augmented reality/virtual reality goggles (right); (C) controllers with various grip sizes consisting of soft foam inserts; (D) virtual dolphin avatar; (E) image target; (F) study staff during game play with (1) smartphone (2) headset (3) controller (4) image target; (G) user experience.

[…]

Source: https://rehab.jmir.org/2020/2/e17822/

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[Abstract] The use of augmented reality for rehabilitation after stroke: a narrative review

Purpose

To explore research relating to the use of Augmented Reality (AR) technology for rehabilitation after stroke in order to better understand the current, and potential future application of this technology to enhance stroke rehabilitation.

Methods

Database searches and reference list screening were conducted to identify studies relating to the use of AR for stroke rehabilitation. These studies were then reviewed and summarised.

Results

Eighteen studies were identified where AR was used for upper or lower limb rehabilitation following stroke. The findings of these studies indicate the technology is in the early stages of development and application. No clear definition of AR was established, with some confusion between virtual and augmented reality identified. Most AR systems engaged users in rote exercises which lacked an occupational focus and contextual relevance. User experience was mostly positive, however the poor quality of the studies limits generalisability of these findings to the greater stroke survivor population.

Conclusion

AR systems are currently being used for stroke rehabilitation in a variety of ways however the technology is in its infancy and warrants further investigation. A consistent definition of AR must be developed and further research is required to determine the possibilities of using AR to promote practice of occupations in a more contextually relevant environment to enhance motor learning and generalisation to other tasks. This could include using AR to bring the home environment into the hospital setting to enhance practice of prioritised occupations before returning home.

  • IMPLICATIONS FOR REHABILITATION

  • There is a developing body of evidence evaluating the use of various forms of AR technology for stroke rehabilitation.

  • User motivation and engagement in rehabilitation may improve with the use of AR.

  • A clear and consistent definition for AR must be developed.

  • Ongoing work could explore how AR systems support engagement in, and promote motor learning that links to, meaningful occupations.

via The use of augmented reality for rehabilitation after stroke: a narrative review: Disability and Rehabilitation: Assistive Technology: Vol 0, No 0

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[Abstract] Enhancing visual performance of hemianopia patients using overview window

Highlights

 

  • Proposal of a computational glasses for visual field defect
  • Design of a whac-a-mole task for empirical performance evaluation
  • Optimal combinations of size, position, and opacity for overlaid window

Abstract

Visual field defect (VFD) is a type of ophthalmic disease that causes the loss of part of the patient’s field of view (FoV). In this paper, we propose a method to enlarge the restricted FoV with an optical see-through head-mounted display (OST-HMD) equipped with a camera that captures an overview and overlays it on the persisting FoV. Because the overview window occludes the real background scene, it is important to create a balance between the augmented contextual information and the unscreened local information. We recruited twelve participants and conducted an experiment to seek the best size, position, and opacity for the overview window through a Whac-A-Mole task (a touchscreen game). We found that the performance was better when the overview window was of medium size (FoV of 9.148 × 5.153, nearly one third of FoV of the used OST-HMD) and placed lower in the visual field. Either too large or too small a size decreases the performance. The performance increases with increased opacity. The obtained results can legitimate the default setting for the overview window.

Graphical abstract

via Enhancing visual performance of hemianopia patients using overview window – ScienceDirect

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[ARTICLE] Walking with head-mounted virtual and augmented reality devices: Effects on position control and gait biomechanics – Full Text PDF

Abstract

What was once a science fiction fantasy, virtual reality (VR) technology has evolved and come a long way. Together with augmented reality (AR) technology, these simulations of an alternative environment have been incorporated into rehabilitation treatments. The introduction of head-mounted displays has made VR/AR devices more intuitive and compact, and no longer limited to upper-limb rehabilitation. However, there is still limited evidence supporting the use of VR and AR technology during locomotion, especially regarding the safety and efficacy relating to walking biomechanics. Therefore, the objective of this study is to explore the limitations of such technology through gait analysis. In this study, thirteen participants walked on a treadmill in normal, virtual and augmented versions of the laboratory environment. A series of spatiotemporal parameters and lower-limb joint angles were compared between conditions. The center of pressure (CoP) ellipse area (95% confidence ellipse) was significantly different between conditions (p = 0.002). Pairwise comparisons indicated a significantly greater CoP ellipse area for both the AR (p = 0.002) and VR (p = 0.005) conditions when compared to the normal laboratory condition. Furthermore, there was a significant difference in stride length (p<0.001) and cadence (p<0.001) between conditions. No statistically significant difference was found in the hip, knee and ankle joint kinematics between the three conditions (p>0.082), except for maximum ankle plantarflexion (p = 0.001). These differences in CoP ellipse area indicate that users of head-mounted VR/AR devices had difficulty maintaining a stable position on the treadmill. Also, differences in the gait parameters suggest that users walked with an unusual gait pattern which could potentially affect the effectiveness of gait rehabilitation treatments. Based on these results, position guidance in the form of feedback and the use of specialized treadmills should be considered when using head-mounted VR/AR devices.

Introduction

Over the past two decades, the application of virtual reality (VR) technology in a healthcare setting has become increasingly popular. It has been incorporated into clinical practices such as in the rehabilitation of stroke survivors, as well as patients with cerebral palsy and multiple sclerosis []. There is ample evidence suggesting that VR-based rehabilitation facilitates upper limb motion [] and dynamic balance [] among stroke survivors. More recently, research groups have also investigated the use of VR in dynamic situations (i.e. treadmill walking), aiming to improve balance and facilitate gait recovery [].

In current clinical practice, gait retraining often includes treadmill training under the supervision of practitioners or through provision of real-time biofeedback. It is a widely adopted technique that aims to permanently correct faulty gait patterns and has been found to be effective in both walking and running gait modifications []. For example, a recently published randomized controlled trial showed that gait retraining was an effective intervention for reduction of knee loading and also improved symptoms among patients with early knee osteoarthritis []. Incorporation of VR technology into conventional gait retraining has the potential to further enhance training outcomes. VR allows users to actively interact with a simulated environment in real-time and offers the opportunity to practice skills acquired in the virtual environments to everyday life []. VR-based gait retraining has the potential to facilitate implicit learning, enhance variety, and actively engage the patient during training. These attributes are crucial in the optimization of motor learning and could maximize the training effect [].

Walking is normally an automatic process. It has been suggested that conscious modification to walking patterns could affect gait retraining adaptations []. A previous study found that subjects who trained with distraction were able to retain the training effect longer than the group who focused on correction []. VR-based retraining could include different tasks and games while the patients modify their gait pattern as it could help patients to maintain focus and promote implicit motor learning. Moreover, the training environment, feedback type and level of difficulty of tasks can be manipulated within the VR environment relatively effortlessly for the clinician, as compared to conventional gait retraining. Variation in training has been shown to promote a more robust motor pattern and favor adaptation [,]. Moreover, motivation and adherence among patients can also be improved with more variation and an adjustable level of difficulty provided in the VR-based training []. Stroke survivors were previously found to be more actively engaged in a VR-based training than a conventional task-oriented intervention to improve motor function []. The training environment can be designed to simulate real-life activities and include task-specific training and a natural experience can be achieved through immersive VR devices, such as using a head-mounted display (HMD) []. Studies have supported task-specific motor skill training with VR in helping to drive neuroplasticity in individuals with progressive neurodegenerative disorder [,].

Although multiple studies have reported positive results of gait retraining using VR among various patient groups within the lab [,,,], there is still little understanding of the limitations and challenges for using VR technology clinically. One overriding concern for using VR technology in clinical applications, especially an HMD, is safety. The user may not be able to recognize his/her own body position when using an immersive VR device, which could result in physical injuries, particularly if the user fails to stay within the boundaries of the treadmill. Suspension devices (i.e. an over-head harness) have been used for protection during VR-based gait rehabilitation [], and a recent study showed that both young and older adults were able to use HMD during walking without adverse effects []. However, the limit of VR technology on safety was not quantified or discussed. Recent technological advances in both the hardware and software of HMD might allow for safer use. However, there is still a need for evidence-based support and quantifiable data, which could help with practical considerations among VR applications in a clinical setting.

Another concern for gait rehabilitation would be the regularity and quality of gait. Through studying spatiotemporal gait parameters, some studies have reported that walking in a projected VR environment can induce gait instability even in healthy participants [,]. Nowadays, VR-based gait retraining using HMD focuses primarily on gait restoration after stroke []; the changes in natural gait due to the use of HMD may not be clinically significant. However, it is crucial for particular patient groups undergoing gait modification to maintain a certain level of regularity in their gait pattern. For instance, knee loading can be affected by spatiotemporal parameters such as cadence and step length [] and VR was previously found to alter such parameters in an over-ground setting []. The treatment effect of gait retraining in reducing knee loading would likely be affected if the patient’s baseline walking gait was already altered by the use of HMD or other VR devices. The aforementioned studies did not quantify the changes in walking biomechanics when using a HMD, therefore, this study aimed to identify gait parameters that were affected by the use of HMD.

An alternative to VR is Augmented Reality (AR), which does not fully immerse the user in a simulated environment but includes virtual elements that are superimposed on a real-world view []. For example, external cues on foot placement could be overlaid on to the walking surface in order to facilitate gait adjustments [,]. The addition of feedback in AR-based gait retraining allows for variations in training and could enhance the gait retraining effect. Yet, there is also a lack of understanding of the limitation of using AR devices. Therefore, this study also aimed to examine the biomechanical changes induced by the HMD within an AR setting.

This study was designed to assess whether the use of commercially available HMD in VR and AR settings were suitable for clinical gait retraining. Specifically, the aim was to quantify the limitations of current VR and AR technology based on two practical concerns for clinical applications: 1) safety: the ability of the user to maintain a relatively stable position within the treadmill and 2) natural gait patterns: deviation of walking biomechanics from that of normal-treadmill walking. We hypothesized that there would be variations in the control of body position relative to the treadmill between both VR and AR conditions when compared with normal-treadmill walking. Also, based on altered gait biomechanics reported with the use of HMD in an over-ground setting [], we hypothesized there would be variation in the spatiotemporal and joint kinematic measures while walking in VR and AR conditions, when compared with normal-treadmill walking.

Materials and methods

Participants

A total of 13 participants (7 females, 6 males; age = 24.6 ± 4.5 years; weight = 63.1 ± 14.5 kg; height = 1.68 ± 0.11 m) were recruited for this study through convenient sampling, which is a comparable sample size to previous studies []. Participants were free of any musculoskeletal, neurological, neuromuscular or cardiovascular pathology that might hinder walking. The experimental procedures were reviewed and approved by the Departmental Research Committee of the department of Rehabilitation Sciences, The Hong Kong Polytechnic University (Ref.: HSEARS20161018001) and written informed consent was obtained from all participants prior to the experiment.

Experimental procedures

Participants were asked to walk at a self-selected pace for four minutes to allow for treadmill adaptation prior to data collection []. Anthropometric data, including leg length, knee width and ankle width [], were recorded and 39 reflective markers were affixed to specific bony landmarks based on the Vicon Plug-in-Gait® full body model []. The marker model was previously established for the measurement of lower-limb kinematics []. This study was designed to assess HMD in VR and AR settings using a commercially available model within a typical clinical setting. Thus, the conditions were designed to be simple and without the use of additional lab equipment. All walking trials were conducted on a dual-belt instrumented treadmill (Force-sensing tandem treadmill, AMTI, Watertown, MA, USA; length x width = 1.2 x 0.6 m). Participants wore their own usual shoes and walked under different conditions at 3.0 km/h (0.83 m/s) for three minutes each. The three conditions were Control, VR and AR, details were as follows:

Control: Treadmill walking without the HMD;

Virtual reality (VR): Immersive 360° panoramic image of the laboratory captured by the Samsung Gear 360 Cam (Samsung, Seoul, South Korea), set up instructions and image file used are provided in the supporting information (S1 File and S1 Fig).

Augmented reality (AR): Real-time display through the rear camera of the HMD, set up instructions are provided in the supporting information (S2 File).

For the AR and VR conditions, participants wore a head-mounted VR device (Samsung Gear VR SM-R322 and Samsung Galaxy S7, Samsung, Seoul, South Korea; width x height x depth: 201.93 x 92.71 x 116.33 mm). The immersive VR/AR environment within this study refers to the panoramic display in a first-person perspective with complete visual obstruction to the real-world environment. The HMD used in this study weighs a total of 470 g, which is comparable to typical commercial HMD models (HTC VIVE Pro: 555 g [] and Oculus Rift DK2: 440 g []). Adjustments to the device were made for fit, focus, and orientation for each participant. Participant’s comfort was confirmed through subjective reporting before the beginning of each walking trial.

The test sequence was randomized using a web-based software (www.randomizer.org). To ensure safety, participants were supported by an overhead safety harness providing 0% bodyweight support. The experimental setup is indicated in Fig 1. The individual in Fig 1 of this manuscript has given written informed consent (as outlined in PLOS consent form) to publish the photograph.

An external file that holds a picture, illustration, etc.Object name is pone.0225972.g001.jpg

Fig 1
A photograph to illustrate the experimental setup.For condition AR and VR, the participant wore a head-mounted VR device. The participant was protected by an overhead safety harness system. Reflective markers and motion cameras were employed to collect gait biomechanics during the walking trials.

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Continue —-> Walking with head-mounted virtual and augmented reality devices: Effects on position control and gait biomechanics

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[NEWS] Ocutrx Vision Technologies promotes AR for macular degeneration

The Oculenz for macular degeneration

A California-based technology startup has developed an augmented reality headset meant to help patients cope with macular degeneration.

Mitchael Freeman, COO of Ocutrx Vision Technologies, LLC, presented the products and discussed how wearable devices, smartphones and artificial intelligence are changing healthcare at the Medical Design & Manufacturing West Conference.

Freeman highlighted the Oculenz Advanced Macular Degeneration ARwear, which has patented technology that uses complex algorithms to reposition video pixels from blurred vision areas to adjacent areas that still have viable vision.

“The speed at which wearable technology is developing and proving its utility in the healthcare space is raising a lot of eyebrows internationally,” Freeman said. “The Ocutrx technology is aimed at both improving surgery protocols and outcomes as well as assisting patients with low vision conditions such as age-related macular degeneration, amblyopia and hemianopsia. But the reality is that our tech — and other wearable tech currently in our development — can and will be used for everything from general healthcare to fitness; from remote disease monitoring to in-home pharma testing; and into advanced surgical telemedicine — and the list goes on.”

Oculenz is available for pre-order now, with shipping expected by summer.

 

via Ocutrx Vision Technologies promotes AR for macular degeneration – Products – McKnight’s Long Term Care News

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[WEB SITE] Neofect Debuts Smart Balance, Designed to Rehab the Lower Body by Playing Games – Rehab Managment

Neofect Debuts Smart Balance, Designed to Rehab the Lower Body by Playing Games

Neofect unveils Neofect Smart Balance, a lower-body rehabilitation device designed to help patients recovering from stroke, ambulatory injuries, and other lower body disabilities regain function in their legs via augmented reality.

Recognized as a 2020 CES Innovation Award honoree, Neofect Smart Balance features 16 rehabilitation games that emphasize core strength, restabilization, and balance, all with the goal of helping patients walk unassisted.

The rehab device features a 2.5-foot by 2.5-foot “Dance Dance Revolution”-esque board designed to evaluate a patient’s posture and gait, then track and analyze motions, providing feedback when it senses an imbalance. Optional handlebars provide additional stability as needed. As patients advance, Neofect Smart Balance games increase speed of movement and coordination as patients step on and off the pad, according to the company, US-based in San Francisco, in a media release.

“For the past decade we’ve focused on hand and upper arm rehabilitation, but we’ve always wanted to create more engaging and measurable therapy for patients who need to recover leg function — whether that’s relearning how to walk or regaining range of motion and confidence,” Scott Kim, co-founder and CEO of Neofect USA, says in the release.

“With Neofect Smart Balance, games like ‘Rock Band’ prompt users to move their feet, in this case to the beat of a song. Patients are physically and cognitively challenged and can also have fun while rehabilitating.”

Neofect Smart Balance is designed for use in healthcare clinics and at home, increasing accessibility of treatment for patients with limited mobility. It securely and remotely shares progress reports with therapists, so they can monitor and adjust patients’ recovery regimen as needed.

Neofect announces it is also showcasing Neofect Connect, a new coaching and companion app, at CES 2020. Designed as an extension of therapy in a clinical setting to support and inspire stroke survivors through recovery at home, Neofect Connect will recommend customized daily exercises and educational materials based on patient ability.

The app, which will be available for any stroke survivor regardless if they use Neofect’s solutions, will include a digital telehealth program where physical and occupational therapists will connect with users remotely to guide their rehabilitation.

Neofect Connect is available on the Apple App Store and on the Google Play Store for homeNeofect users and will be open to any stroke survivor in spring 2020, per the release.

[Source(s): Neofect, Business Wire]

 

via Neofect Debuts Smart Balance, Designed to Rehab the Lower Body by Playing Games – Rehab Managment

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