- •Pain is a subjective experience that results from the complex modulation of nociception conveyed to the brain via the nervous system.
- •Psychological factors such as cognitions (eg, pain catastrophizing), emotions (eg, depression), and pain-related behaviors (eg, avoidance) can influence perceived pain intensity, physical function, and treatment outcomes.
- •Several evidence-based interventions to address pain-related psychological risk factors are available and can be integrated into hand therapy.
Posts Tagged upper limb
[ARTICLE] Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training – Full Text
Stroke is a leading cause of severe long-term disability. In the US alone, nearly 800,000 people suffer a stroke each year . The number of individuals who suffer a stroke each year is expected to rise in the coming years because the prevalence of stroke increases with age and the world population is aging . Approximately 85% of individuals who have a stroke survive, but they often experience significant motor impairments. Upper-limb paresis is the most common impairment following a stroke. It affects 75% of stroke survivors and leads to limitations in the performance of Activities of Daily Living (ADL) .
Inability to use the stroke-affected upper limb for ADL often leads to a phenomenon that is referred to as learned non-use . As patients rely more and more on the unaffected (or less impaired) upper limb  they progressively lose motor abilities of the stroke-affected upper limb that they may have recovered as a result of a rehabilitation intervention .
A high dosage of motor practice using the stroke-affected upper limb during the performance of ADL, despite considerable difficulty, stimulates neuroplasticity and motor function recovery –. Thus, it is clinically important to encourage stroke survivors to continue making appropriate use of the affected upper limb –, in addition to engaging in rehabilitation exercises that focus on range-of-motion and functional abilities –.
The use of wearable sensors has recently emerged as an efficient way to monitor the amount of upper-limb use after a stroke –. However, despite growing evidence of the clinical potential of these devices , their widespread clinical deployment has been hindered by technical limitations. A shortcoming of currently available wrist-worn devices is that they cannot distinguish between Goal-Directed (GD) movements (i.e., movements performed for a specific purposeful task) and non-Goal-Directed (non-GD) movements (e.g., the arm swinging during gait). Instead, these sensors focus on recording the number and/or intensity of any type of arm movements . Consequently, non-GD movements are reflected as part of the measurements with equal importance as GD movements. This results in an overestimation of the amount of actual arm use . Furthermore, monitoring the aggregate number of stroke-affected upper limb movements is not sufficient for the purpose of providing timely feedback to encourage the use of the affected limb during the performance of ADL. To promote the use of the stroke-affected limb, it is critical that feedback reflects the relative use of the affected upper limb compared to the contralateral one.
Wrist-worn movement sensors have also been applied to monitoring rehabilitation exercises in the home setting –. However, existing systems primarily focus on quantifying the dosage/intensity of the exercises (e.g., the duration of the exercises and the number of movement repetitions) and do not monitor if the quality of the performed exercise is appropriate. Ensuring good quality of movement during the performance of rehabilitation exercises is critical for maximizing functional recovery after a stroke . Moreover, providing customized feedback regarding the quality of exercise movements can increase motivation, promote long-term adherence to a prescribed exercise regimen, and ultimately maximize clinical outcomes . One of the reasons for limited exercise participation by stroke survivors is the lack of access to resources to support exercise including performance feedback from rehabilitation specialists . There are no technical solutions that provide feedback regarding the quality of exercise performance for upper-limb rehabilitation after stroke.
We propose a system for aiding in functional recovery after a stroke that consists of two wearable sensors, one worn on the stroke-affected upper limb and the other on the contralateral upper limb  (Fig. 1). The proposed system can be used to provide timely feedback when ADL are performed. If the system detects that the patient consistently performs GD movements with the unaffected upper limb, and rarely uses the stroke-affected upper limb, then a visual or vibrotactile reminder can be triggered to encourage the patient to attempt GD movements with the stroke-affected limb. A benefit of this approach is that if a movement is critical (e.g., signing a check), patients can use the unaffected upper limb without receiving negative feedback as long as they have performed a sufficient number of movements with the affected upper limb throughout the day. Furthermore, the system promotes high-dosage motor practice with appropriate feedback to extend components of rehabilitation interventions into the home environment.[…]
[Abstract+References] The Use of Image Processing Methods to Improve the Detection of User’s Hand in Vision Based Games Used in Neurological Rehabilitation
Vision based games is a type of software that can become a promising, modern neurorehabilitation tool. This paper presents the possibilities offered for the implementation of this kind of software by the open source vision library. The methods and functions related to the aspect of image processing and analysis are presented in terms of their usefulness in creating programs based on the analysis of the images acquired from the camera. On the basis of the issues contained in the paper, the functionality of the library is presented in terms of the possibilities related primarily to the processing of video sequences, detection, tracking and analysis of the movement of objects.
As part of the work, the software that meets the requirements for modern neurorehablitation games has been implemented. Its main part is responsible for the identification of the current position of the user’s hand and is based on the image captured from the webcam. Whereas the tasks set for the user used among others supporting visual-motor coordination.
The main subject of the research was the analysis of the impact of the applied methods of initial image processing on the correctness of the chosen tracking algorithm. It was proposed and experimentally examined the impact of operations such as morphological transformations or apply an additional mask on a functioning of the CamShift algorithm. And hence on the functioning of the whole game which analyzing the user’s hand movement.
Allen G. J., Richard Xu Y. D., Jin J. S. (2004). Object Tracking Using CAMShift Algorithm and Multiple Quantized Feature Spaces, Proceedings of the Pan-Sydney area workshop on Visual information processing , Sydney, 3-7.
Bradski G., Kaehler A. (2008). Learning OpenCV. Computer Vision with the OpenCV Library, Sebastopol, CA: O’Reilly Media.
Buczyński P. (2005). Optymalna reprezentacja kolorów w analizie i przetwarzaniu obrazów komputerowych, Praca doktorska. Warszawa: Politechnika Warszawska.
Burke J. W., Morrow P.J., et al. (2008). Vision Based Games for Upper-Limb Stroke Rehabilitation, Machine Vision and Image Processing Conference, 159 – 164.
Burke J. W. McNeill M. D. J., et al. (2010). Designing engaging, playable games for rehabilitation”, International Conference Series On Disability, Virtual Reality and Associated Technologies (ICDVRAT), 195-202.
Cameirão M.S. , et al. (2010). Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation, Journal of NeuroEngineering and Rehabilitation, 7, 48.
Comaniciu D., Ramesh V., Meer P. (2003). Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions 2003, p. 564-577.
Derpanis K. G. (2005). Mean Shift Clustering, http://www.cse.yorku.ca/~kosta/ Comp-Vis_Notes/mean_shift.pdf
Di Loreto I., Gouaich A., Hocine N., (2011). Mixed reality serious games for post-stroke rehabilitation, Pervasive Computing Technologies for Healthcare , 5th International Conference on, 530-537.
Garcia-Marin J., Felix-Navarro K., Law-rence E. (2011). Serious games to Improve the Physical Health of the Elderly: A Categorization Scheme, Fourth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services (CENTRIC 2011), 64-71.
Jog A., Halbe S. (2013). Multiple Objects Tracking Using CAMShift Algorithm and Implementation of Trip Wire, International Journal of Image, Graphics and Signal Processing, 43-48.
Joshi S., Gujarathi S., Mirgemoving A. (2014). Moving object tracking method using improved camshift with surf algorithm. International Journal of Advances in Science Engineering and Technology, 2(2), 14-19.
Laganière R. (2011). “OpenCV 2 Computer Vision Application Programming Cookbook”, Packt Publishing, 2011.
Lange B., Flynn S.M., Rizzo A. A., (2009). Game-based telerehabilitation, European Journal of Physical and Rehabilitation Medicine, 45(1), 143-151.
Rafajłowicz E, Rafajłowicz W. (2010). Wstęp do przetwarzania obrazów przemysłowych, Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej.
Rayavel P., Appasami G., Nakeeran R. (2011). Noise removal for object tracking based on HSV color space parameter using CAMSHIFT. International Journal of Computational Intelligence & Telecommunication Systems, 2(1), 39–45.
Yilmaz A., Javed O., Shah M. (2006). Object tracking: A survey, ACM Computing Surveys, 38(4), Article 13, 1-45.
[ARTICLE] BCI-Based Strategies on Stroke Rehabilitation with Avatar and FES Feedback – Full Text PDF
Stroke is the leading cause of serious and long-term disability worldwide. Some studies have shown that motor imagery (MI) based BCI has a positive effect in poststroke rehabilitation. It could help patients promote the reorganization processes in the damaged brain regions. However, offline motor imagery and conventional online motor imagery with feedback (such as rewarding sounds and movements of an avatar) could not reflect the true intention of the patients. In this study, both virtual limbs and functional electrical stimulation (FES) were used as feedback to provide patients a closed-loop sensorimotor integration for motor rehabilitation. The FES system would activate if the user was imagining hand movement of instructed side. Ten stroke patients (7 male, aged 22-70 years, mean 49.5+-15.1) were involved in this study. All of them participated in BCI-FES rehabilitation training for 4 weeks.The average motor imagery accuracies of the ten patients in the last week were 71.3%, which has improved 3% than that in the first week. Five patients’ Fugl-Meyer Assessment (FMA) scores have been raised. Patient 6, who has have suffered from stroke over two years, achieved the greatest improvement after rehabilitation training (pre FMA: 20, post FMA: 35). In the aspect of brain patterns, the active patterns of the five patients gradually became centralized and shifted to sensorimotor areas (channel C3 and C4) and premotor area (channel FC3 and FC4).In this study, motor imagery based BCI and FES system were combined to provided stoke patients with a closed-loop sensorimotor integration for motor rehabilitation. Result showed evidences that the BCI-FES system is effective in restoring upper extremities motor function in stroke. In future work, more cases are needed to demonstrate its superiority over conventional therapy and explore the potential role of MI in poststroke rehabilitation.
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[Abstract+References] Self-directed therapy programmes for arm rehabilitation after stroke: a systematic review
A systematic review of Medline, EMBASE, CINAHL, SCOPUS and IEEE Xplore up to February 2018 was carried out. Studies of stroke arm interventions were included where more than 50% of the time spent in therapy was initiated and carried out by the participant. Quality of the evidence was assessed using the Cochrane risk of bias tool.
A total of 40 studies (n = 1172 participants) were included (19 randomized controlled trials (RCTs) and 21 before–after studies). Studies were grouped according to no technology or the main additional technology used (no technology n = 5; interactive gaming n = 6; electrical stimulation n= 11; constraint-induced movement therapy n = 6; robotic and dynamic orthotic devices n = 8; mirror therapy n = 1; telerehabilitation n = 2; wearable devices n = 1). A beneficial effect on arm function was found for self-directed interventions using constraint-induced movement therapy (n = 105; standardized mean difference (SMD) 0.39, 95% confidence interval (CI) −0.00 to 0.78) and electrical stimulation (n = 94; SMD 0.50, 95% CI 0.08–0.91). Constraint-induced movement therapy and therapy programmes without technology improved independence in activities of daily living. Sensitivity analysis demonstrated arm function benefit for patients >12 months poststroke (n = 145; SMD 0.52, 95% CI 0.21–0.82) but not at 0–3, 3–6 or 6–12 months.
Self-directed interventions can enhance arm recovery after stroke but the effect varies according to the approach used and timing. There were benefits identified from self-directed delivery of constraint-induced movement therapy, electrical stimulation and therapy programmes that increase practice without using additional technology.
|1.||Han, C, Wang, Q, Meng, PP. Effects of intensity of arm training on hemiplegic upper extremity motor recovery in stroke patients: a randomized controlled trial. Clin Rehabil 2013; 27: 75–81. Google Scholar, SAGE Journals, ISI|
|2.||Hayward, KS, Brauer, SG. Dose of arm activity training during acute and subacute rehabilitation post stroke: a systematic review of the literature. Clin Rehabil 2015; 29: 1234–1243. Google Scholar, SAGE Journals, ISI|
|3.||Pollock, A, Farmer, SE, Brady, MC. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev 2014; 11: CD010820. Google Scholar|
|4.||Bernhardt, J, Chan, J, Nicola, I. Little therapy, little physical activity: rehabilitation within the first 14 days of organized stroke unit care. J Rehabil Med 2007; 39: 43–48. Google Scholar, Crossref, Medline, ISI|
|5.||Clarke, DJ, Burton, LJ, Tyson, SF. Why do stroke survivors not receive recommended amounts of active therapy? Findings from the ReAcT study, a mixed-methods case-study evaluation in eight stroke units. Clin Rehabil. Epub ahead of print 27 March 2018. DOI: 10.1177/0269215518765329. Google Scholar, SAGE Journals|
|6.||Demain, S, Burridge, J, Ellis-Hill, C. Assistive technologies after stroke: self-management or fending for yourself? A focus group study. BMC Health Serv Res 2013; 13: 334. Google Scholar, Crossref, Medline, ISI|
|7.||Higgins, JPT, Green, S. Cochrane handbook for systematic reviews of interventions. Hoboken, NJ: John Wiley & Sons, 2011. Google Scholar|
|8.||Da-Silva, R, Price, CI, Moore, S. A systematic review of self-directed therapy interventions with and without technology for upper limb rehabilitation after stroke, 2016, http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016038619 Google Scholar|
|9.||Review manager (Rev Man) version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014. Google Scholar|
|10.||Moher, D, Liberati, A, Tetzlaff, J. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol 2009; 62: 1006–1012. Google Scholar, Crossref, Medline, ISI|
|11.||Adie, K, Schofield, C, Berrow, M. Does the use of Nintendo Wii sportsTM improve arm function? Trial of WiiTM in stroke: a randomized controlled trial and economics analysis. Clin Rehabil 2017; 31: 173–185. Google Scholar, SAGE Journals, ISI|
|12.||Brkic, L, Shaw, L, van Wijck, F. Repetitive arm functional tasks after stroke (RAFTAS): a pilot randomised controlled trial. Pilot Feasibil Stud 2016; 2: 50. Google Scholar, Crossref, Medline|
|13.||Brunner, IC, Skouen, JS, Strand, LI. Is modified constraint-induced movement therapy more effective than bimanual training in improving arm motor function in the subacute phase post stroke? A randomized controlled trial. Clin Rehabil 2012; 26: 1078–1086. Google Scholar, SAGE Journals, ISI|
|14.||Dos Santos-Fontes, RL, De Andrade, KN, Sterr, A. Home-based nerve stimulation to enhance effects of motor training in patients in the chronic phase after stroke: a proof-of-principle study. Neurorehabil Neural Repair 2013; 27: 483–490. Google Scholar, SAGE Journals, ISI|
|15.||Gabr, U, Levine, P, Page, SJ. Home-based electromyography-triggered stimulation in chronic stroke. Clin Rehabil 2005; 19: 737–745. Google Scholar, SAGE Journals, ISI|
|16.||Hara, Y, Ogawa, S, Tsujiuchi, K. A home-based rehabilitation program for the hemiplegic upper extremity by power-assisted functional electrical stimulation. Disabil Rehabil 2008; 30: 296–304. Google Scholar, Crossref, Medline, ISI|
|17.||Harris, JE, Eng, JJ, Miller, WC. A self-administered Graded Repetitive Arm Supplementary Program (GRASP) improves arm function during inpatient stroke rehabilitation: a multi-site randomized controlled trial. Stroke 2009; 40: 2123–2128. Google Scholar, Crossref, Medline, ISI|
|18.||Kimberley, TJ, Lewis, SM, Auerbach, EJ. Electrical stimulation driving functional improvements and cortical changes in subjects with stroke. Exp Brain Res 2004; 154: 450–460. Google Scholar, Crossref, Medline, ISI|
|19.||Michielsen, ME, Selles, RW, Van Der Geest, JN. Motor recovery and cortical reorganization after mirror therapy in chronic stroke patients: a phase II randomized controlled trial. Neurorehabil Neural Repair 2011; 25: 223–233. Google Scholar, SAGE Journals, ISI|
|20.||Nijenhuis, SM, Prange-Lasonder, GB, Stienen, AH. Effects of training with a passive hand orthosis and games at home in chronic stroke: a pilot randomised controlled trial. Clin Rehabil 2017; 31: 207–216. Google Scholar, SAGE Journals, ISI|
|21.||Smania, N, Gandolfi, M, Paolucci, S. Reduced-intensity modified constraint-induced movement therapy versus conventional therapy for upper extremity rehabilitation after stroke: a multicenter trial. Neurorehabil Neural Repair 2012; 26: 1035–1045. Google Scholar, SAGE Journals, ISI|
|22.||Standen, PJ, Threapleton, K, Richardson, A. A low cost virtual reality system for home based rehabilitation of the arm following stroke: a randomised controlled feasibility trial. Clin Rehabil 2017; 31: 340–350. Google Scholar, SAGE Journals, ISI|
|23.||Stinear, CM, Barber, PA, Coxon, JP. Priming the motor system enhances the effects of upper limb therapy in chronic stroke. Brain 2008; 131: 1381–1390. Google Scholar, Crossref, Medline, ISI|
|24.||Sullivan, JE, Hurley, D, Hedman, LD. Afferent stimulation provided by glove electrode during task-specific arm exercise following stroke. Clin Rehabil 2012; 26: 1010–1020. Google Scholar, SAGE Journals, ISI|
|25.||Tariah, HA, Almalty, A, Sbeih, Z. Constraint induced movement therapy for stroke survivors in Jordon: a home-based model. Int J Ther Rehabil 2010; 17: 638–646. Google Scholar, Crossref|
|26.||Turton, AJ, Cunningham, P, van Wijck, F. Home-based reach-to-grasp training for people after stroke is feasible: a pilot randomised controlled trial. Clin Rehabil 2017; 31: 891–903. Google Scholar, SAGE Journals, ISI|
|27.||Wolf, SL, Sahu, K, Bay, RC. The HAAPI (Home Arm Assistance Progression Initiative) trial: a novel robotics delivery approach in stroke rehabilitation. Neurorehabil Neural Repair 2015; 29: 958–968. Google Scholar, SAGE Journals, ISI|
|28.||Zondervan, DK, Augsburger, R, Bodenhoefer, B. Machine-based, self-guided home therapy for individuals with severe arm impairment after stroke: a randomized controlled trial. Neurorehabil Neural Repair 2015; 29: 395–406. Google Scholar, SAGE Journals, ISI|
|29.||Burridge, JH, Lee, ACW, Turk, R. Telehealth, wearable sensors, and the internet: will they improve stroke outcomes through increased intensity of therapy, motivation, and adherence to rehabilitation programs? J Neurol Phys Ther 2017; 41(suppl. 3): S32–S38. Google Scholar, Crossref, Medline|
|30.||Alon, G, McBride, K, Ring, H. Improving selected hand functions using a noninvasive neuroprosthesis in persons with chronic stroke. J Stroke Cerebrovas Dis 2002; 11: 99–106. Google Scholar, Crossref, Medline|
|31.||Alon, G, Sunnerhagen, KS, Geurts, ACH. A home-based, self-administered stimulation program to improve selected hand functions of chronic stroke. Neurorehabilitation 2003; 18: 215–225. Google Scholar, Medline, ISI|
|32.||Burridge, JH, Turk, R, Merrill, D. A personalized sensor-controlled microstimulator system for arm rehabilitation poststroke. Part 2: objective outcomes and patients’ perspectives. Neuromodulation 2011; 14: 80–88. Google Scholar, Crossref, Medline|
|33.||Brown, EV, McCoy, SW, Fechko, AS. Preliminary investigation of an electromyography-controlled video game as a home program for persons in the chronic phase of stroke recovery. Arch Phys Med Rehabil 2014; 95: 1461–1469. Google Scholar, Crossref, Medline|
|34.||Langan, J, Delave, K, Phillips, L. Home-based telerehabilitation shows improved upper limb function in adults with chronic stroke: a pilot study. J Rehabil Med 2013; 45: 217–220. Google Scholar, Crossref, Medline, ISI|
|35.||Lee, HS, Kim, JU. The effect of self-directed exercise using a task board on pain and function in the upper extremities of stroke patients. J Phys Ther Sci 2013; 25: 963–967. Google Scholar, Crossref, Medline|
|36.||Mawson, S. The SMART rehabilitation system for stroke self-management: issues and challenges for evidence-based health technology research. J Phys Ther Educ 2011; 25: 48–53. Google Scholar, Crossref|
|37.||Mouawad, MR, Doust, CG, Max, MD. Wii-based movement therapy to promote improved upper extremity function post-stroke: a pilot study. J Rehabil Med 2011; 43: 527–533. Google Scholar, Crossref, Medline, ISI|
|38.||Niama Natta, DD, Alagnide, E, Kpadonou, GT. Feasibility of a self-rehabilitation program for the upper limb for stroke patients in Benin. Ann Phys Rehabil Med 2015; 58: 322–325. Google Scholar, Crossref, Medline|
|39.||Nijenhuis, SM, Prange, GB, Amirabdollahian, F. Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke. J Neuroeng Rehabil 2015; 12: 89. Google Scholar, Crossref, Medline, ISI|
|40.||Page, SJ, Levine, P. Modified constraint-induced therapy extension: using remote technologies to improve function. Arch Phys Med Rehabil 2007; 88: 922–927. Google Scholar, Crossref, Medline, ISI|
|41.||Page, SJ, Levine, P, Hill, V. Mental practice–triggered electrical stimulation in chronic, moderate, upper-extremity hemiparesis after stroke. Am J Occup Ther 2015; 69: 1–88. Google Scholar|
|42.||Pickett, TC, Fritz, SL, Ketterson, TU. Telehealth and constraint-induced movement therapy (CIMT): an intensive case study approach. Clin Gerontol 2007; 31: 5–20. Google Scholar, Crossref|
|43.||Sivan, M, Gallagher, J, Makower, S. Home-based computer assisted arm rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting. J Neuroeng Rehabil 2014; 11: 163. Google Scholar, Crossref, Medline, ISI|
|44.||Sullivan, JE, Hedman, LD. Effects of home-based sensory and motor amplitude electrical stimulation on arm dysfunction in chronic stroke. Clin Rehabil 2007; 21: 142–150. Google Scholar, SAGE Journals, ISI|
|45.||Turk, R, Burridge, JH, Davis, R. Therapeutic effectiveness of electric stimulation of the upper-limb poststroke using implanted microstimulators. Arch Phys Med Rehabil 2008; 89: 1913–1922. Google Scholar, Crossref, Medline|
|46.||Wittmann, F, Held, JP, Lambercy, O. Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J Neuroeng Rehabil 2016; 13: 75. Google Scholar, Crossref, Medline|
|47.||Wittmann, F, Lambercy, O, Gonzenbach, RR. Assessment-driven arm therapy at home using an IMU-based virtual reality system. In: Proceedings of the 2015 IEEE international conference on rehabilitation robotics (ICORR), Singapore, 11–14 August 2015, pp.707–712. New York: IEEE. Google Scholar|
|48.||Zhang, H, Austin, H, Buchanan, S. Feasibility studies of robot-assisted stroke rehabilitation at clinic and home settings using RUPERT. In: Proceedings of the IEEE international conference on rehabilitation robotics, Zurich, 29 June–1 July 2011. Google Scholar, Crossref|
|49.||Da-Silva, RH, Frederike van, W, Shaw, F. Prompting arm activity after stroke: a clinical proof of concept study of wrist-worn accelerometers with a vibrating alert function. J Rehabil Assis Technol Eng 2018; 5: 1–8. Google Scholar|
|50.||Chen, J, Nichols, D, Brokaw, EB. Home-based therapy after stroke using the hand spring operated movement enhancer (HandSOME). IEEE Trans Neural Syst Rehabil Eng 2017; 25: 2305–2312. Google Scholar, Crossref, Medline|
|51.||Fryer, CE, Luker, JA, McDonnell, MN. Self management programmes for quality of life in people with stroke. Cochrane Database Syst Rev 2016; 8: CD010442. Google Scholar|
|52.||Wray, F, Clarke, D, Forster, A. Post-stroke self-management interventions: a systematic review of effectiveness and investigation of the inclusion of stroke survivors with aphasia. Disabil Rehabil 2018; 40: 1237–1251. Google Scholar, Crossref, Medline|
|53.||Krakauer, JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol 2006; 19: 84–90. Google Scholar, Crossref, Medline, ISI|
|54.||Korpershoek, C, van der Bijl, J, Hafsteinsdottir, TB. Self-efficacy and its influence on recovery of patients with stroke: a systematic review. J Adv Nurs 2011; 67: 1876–1894. Google Scholar, Crossref, Medline, ISI|
|55.||Jones, F, Riazi, A. Self-efficacy and self-management after stroke: a systematic review. Disabil Rehabil 2011; 33: 797–810. Google Scholar, Crossref, Medline, ISI|
|56.||Brown, E, Cairns, P. A grounded investigation of game immersion. In: Proceedings of the extended abstracts of the 2004 conference on human factors in computer systems, Vienna, 24–29 April 2004, pp.1297–1300. New York: ACM Press. Google Scholar|
|57.||Wade, D. Rehabilitation – a new approach. Part four: a new paradigm, and its implications. Clin Rehabil 2016; 30: 109–118. Google Scholar, SAGE Journals, ISI|
|58.||Farmer, SE, Durairaj, V, Swain, I. Assistive technologies: can they contribute to rehabilitation of the upper limb after stroke? Arch Phys Med Rehabil 2014; 95: 968–985. Google Scholar, Crossref, Medline, ISI|
|59.||Hibbard, JH, Stockard, J, Mahoney, ER. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res 2004; 39: 1005–1026. Google Scholar, Crossref, Medline, ISI|
[Abstract] Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton
Adaptive integral sliding mode control design for exoskeletons.
Finite time convergence of the closed-loop system.
Robustness of the control law with respect to parametric variations and disturbances.
No requirement of the knowledge of the system bounds.
Real experiments using an upper limb exoskeleton with and without human subjects.
A robust adaptive integral terminal sliding mode control strategy is proposed in this paper to deal with unknown but bounded dynamic uncertainties of a nonlinear system. This method is applied for the control of upper limb exoskeleton in order to achieve passive rehabilitation movements. Indeed, exoskeletons are in direct interaction with the human limb and even if it is possible to identify the nominal dynamics of the exoskeleton, the subject’s limb dynamics remain typically unknown and defer from a person to another. The proposed approach uses only the exoskeleton nominal model while the system upper bounds are adjusted adaptively. No prior knowledge of the exact dynamic model and upper bounds of uncertainties is required. Finite time stability and convergence are proven using Lyapunov theory. Experiments were performed with healthy subjects to evaluate the performance and the efficiency of the proposed controller in tracking trajectories that correspond to passive arm movements.
[ARTICLE] Interactive Design and Development of Real Arm Movements for Application in Rehabilitation – Full Text PDF
An interactive real arm movements for application in rehabilitation is designed and
developed. The aim is to encourage hand paralysis patients performing their physical therapy by introducing games application in replacing conventional hand therapy module and methods. In this project, the accelerometer is used for tracking the orientation of the arm. As the arm moves, the values from x, y and z axis from the accelerometer changes and are being read by the Analog Inputs of the Arduino Board. After being read by the Analog Inputs of the Arduino Board, the 3D model moves as well. Solidworks software was used to modeled the hand in which the data is then transferred to Matlab/Simulink using SimMechanicalLink from Mathworks. Lastly, the sensor glove was programmed to work as a controller of games application in hand rehabilitation thus makes it an enjoyable therapy process. […]
Pain is a subjective experience that results from the modulation of nociception conveyed to the brain via the nervous system. Perception of pain takes place when potential or actual noxious stimuli are appraised as threats of injury. This appraisal is influenced by one’s cognitions and emotions based on her/his pain-related experiences, which are processed in the forebrain and limbic areas of the brain. Unarguably, patients’ psychological factors such as cognitions (eg, pain catastrophizing), emotions (eg, depression), and pain-related behaviors (eg, avoidance) can influence perceived pain intensity, disability, and treatment outcomes. Therefore, hand therapists should address the patient pain experience using a biopsychosocial approach. However, in hand therapy, a biomedical perspective predominates in pain management by focusing solely on tissue healing.
Purpose of the Study
This review aims to raise awareness among hand therapists of the impact of pain-related psychological factors.
Methods and Results
This literature review allowed to describe (1) how the neurophysiological mechanisms of pain can be influenced by various psychological factors, (2) several evidence-based interventions that can be integrated into hand therapy to address these psychological issues, and (3) some approaches of psychotherapy for patients with maladaptive pain experiences.
Discussion and Conclusion
Restoration of sensory and motor functions as well as alleviating pain is at the core of hand therapy. Numerous psychological factors including patients’ beliefs, cognitions, and emotions alter their pain experience and may impact on their outcomes. Decoding the biopsychosocial components of the patients’ pain is thus essential for hand therapists.
Flexo-glove is a 3D printed soft exoskeleton robotic glove with compact and streamlined design for assistance in activities of daily livings and rehabilitation purposes of patients with hand function impairment.
- Overall weight of 330g including battery
- Providing 22N pinch force, 48N power grasp force and object grasp size of up to 81mm in diameter
- Two control modes: intention-sensing via wireless surface EMG for assistive mode and externally-directed via an accompanying smartphone
Project Details: —> Visit site
- Initiated the project with the idea of using soft 3D printed materials in design of the Flexo-glove inspired by X-Limb
- Performed feasibility study for using cable-driven mechanism in actuation of rehabilitation glove
- Leading a group of four mechatronics engineering students to fabricate the prototype and characterise the grip forces
- Received Dyason fellowship, $5000 travel fellowship awarded by Melbourne Robotic Lab. to visit Harvard BioRobotics Lab
Flexo-glove: A 3D Printed Soft Exoskeleton Robotic Glove for Impaired Hand Rehabilitation and Assistance
40th International Engineering in Medicine and Biology Conference (EMBC), 2018.
Full Text PDF
Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. Game-based therapy can promote patients’ engagement in rehabilitation therapy as a more interesting and a motivating tool. Mobile devices such as smartphones and tablet PCs can provide personalized home-based therapy with interactive communication between patients and clinicians. In this study, a mobile VR upper extremity rehabilitation program using game applications was developed. The findings from the study show that the mobile game-based VR program effectively promotes upper extremity recovery in patients with stroke. In addition, patients completed two weeks of treatment using the program without adverse effects and were generally satisfied with the program. This mobile game-based VR upper extremity rehabilitation program can substitute for some parts of the conventional therapy that are delivered one-on-one by an occupational therapist. This time-efficient, easy to implement, and clinically effective program would be a good candidate tool for tele-rehabilitation for upper extremity recovery in patients with stroke. Patients and therapists can collaborate remotely through these e-health rehabilitation programs while reducing economic and social costs.
[Abstract] Evolution of upper limb kinematics four years after subacute robot-assisted rehabilitation in stroke patients
Purpose: To assess functional status and robot-based kinematic measures four years after subacute robot-assisted rehabilitation in hemiparesis.
Material and methods: Twenty-two patients with stroke-induced hemiparesis participated in a ≥3-month upper limb combined program of robot-assisted and occupational therapy from two months post-stroke, and received community-based therapy after discharge. Four years later, nineteen (86%) participated in this long-term follow-up study. Assessments two, five and 54 months post-stroke included Fugl-Meyer (FM), Modified Frenchay Scale (MFS, at Month 54) and robot-based kinematic measures of targeting tasks in three directions, north, paretic and non-paretic: distance covered, velocity, accuracy (RMS error from straight line) and smoothness (number of velocity peaks; upward changes in accuracy and smoothness measures represent worsening). Analysis was stratified by FM score at two months: ≥17 (Group 1) or < 17 (Group 2). Correlation between impairment (FM) and function (MFS) was explored at 54 months.
Results: Fugl-Meyer scores were stable from five to 54 months (+1[-2;4], median[1st;3rd quartiles], ns). Kinematic changes in the three directions pooled were: distance covered, -1[-17;2]% (ns); velocity, -8[-32;28]% (ns); accuracy, +6[-13;98]% (ns); smoothness, +44[-6;126]% (p<0.05). Group 2 showed decline vs Group 1 (p<0.001) in FM (Group 1, +3[1;5], p<0.01; Group 2, -7[-11;-1], ns) and accuracy (Group 1, -3[-27;38]%, ns; Group 2, +29[17;140]%, p<0.001). At 54 months, FM and MFS were highly correlated (Pearson’s rho = 0.89; p<0.001).
Conclusions: While impairment appeared stable four years after robot-assisted upper limb training during subacute post-stroke phase, kinematic performance deteriorated in spite of community-based therapy, especially in patients with more severe impairment.