Posts Tagged Hand

[Abstract + References] A Virtual Reality Serious Game for Hand Rehabilitation Therapy – IEEE Conference Publication

Abstract

The human hand is the body part most frequently injured in occupational accidents, accounting for one out of five emergency cases and often requiring surgery with subsequently long periods of rehabilitation. This paper proposes a Virtual Reality game to improve conventional physiotherapy in hand rehabilitation, focusing on resolving recurring limitations reported in most technological solutions to the problem, namely the limited diversity support of movements and exercises, complicated calibrations and exclusion of patients with open wounds or other disfigurements of the hand. The system was assessed by seven able-bodied participants using a semistructured interview targeting three evaluation categories: hardware usability, software usability and suggestions for improvement. A System Usability Score (SUS) of 84.3 and participants’ disposition to play the game confirm the potential of both the conceptual and technological approaches taken for the improvement of hand rehabilitation therapy.

References

1. A. Elnaggar and D. Reichardt, “Digitizing the Hand Rehabilitation Using Serious Games Methodology with User-Centered Design Approach”, 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 13-22, 2016. Show Context View Article Full Text: PDF (1150KB) Google Scholar 

2. L. S. Robinson, M. Sarkies, T. Brown and L. O’Brien, “Direct indirect and intangible costs of acute hand and wrist injuries: A systematic review”, Injury, vol. 47, no. 12, pp. 2614-2626, Dec. 2016. Show Context CrossRef  Google Scholar 

3. D. Johnson, S. Deterding, K.-A. Kuhn, A. Staneva, S. Stoyanov and L. Hides, “Gamification for health and wellbeing: A systematic review of the literature”, Internet Interv., vol. 6, pp. 89-106, Nov. 2016. Show Context CrossRef  Google Scholar 

4. C. Prahm, “PlayBionic Interactive rehabilitation after amputation or nerve injury of the upper extremity”, Christian Doppler Laboratory for Restoration of Extremity Function and Rehabilitation, 2019. Show Context Google Scholar 

5. M. K. Holden, “Virtual Environments for Motor Rehabilitation: Review”, CyberPsychology Behav., vol. 8, no. 3, pp. 187-211, Jun. 2005. Show Context CrossRef  Google Scholar 

6. D. Ganjiwale, R. Pathak, A. Dwivedi, J. Ganjiwale and S. Parekh, “Occupational therapy rehabilitation of industrial setup hand injury cases for functional independence using modified joystick in interactive computer gaming in Anand Gujarat”, Natl. J. Physiol. Pharm. Pharmacol., vol. 9, pp. 1, 2018. Show Context CrossRef  Google Scholar 

7. H. A. Hernández, A. Khan, L. Fay, Je.-S. Roy and E. Biddiss, “Force Resistance Training in Hand Grasp and Arm Therapy: Feasibility of a Low-Cost Videogame Controller”, Games Health J., vol. 7, no. 4, pp. 277-287, Aug. 2018. Show Context CrossRef  Google Scholar 

8. J. Broeren, L. Claesson, D. Goude, M. Rydmark and K. S. Sunnerhagen, “Virtual Rehabilitation in an Activity Centre for Community-Dwelling Persons with Stroke”, Cerebrovasc. Dis., vol. 26, no. 3, pp. 289-296, 2008. Show Context CrossRef  Google Scholar 

9. J. Broeren, M. Rydmark and K. S. Sunnerhagen, “Virtual reality and haptics as a training device for movement rehabilitation after stroke: A single-case study”, Arch. Phys. Med. Rehabil., vol. 85, no. 8, pp. 1247-1250, Aug. 2004. Show Context CrossRef  Google Scholar 

10. C. N. Walifio-Paniagua et al., “Effects of a Game-Based Virtual Reality Video Capture Training Program Plus Occupational Therapy on Manual Dexterity in Patients with Multiple Sclerosis: A Randomized Controlled Trial”, J. Healthc. Eng., vol. 2019, pp. 1-7, Apr. 2019. Show Context CrossRef  Google Scholar 

11. M. E. Gabyzon, B. Engel-Yeger, S. Tresser and S. Springer, “Using a virtual reality game to assess goal-directed hand movements in children: A pilot feasibility study”, Technol. Heal. Care, vol. 24, no. 1, pp. 11-19, Jan. 2016. Show Context CrossRef  Google Scholar 

12. M. King, L. Hale, A. Pekkari, M. Persson, M. Gregorsson and M. Nilsson, “An affordable computerised table-based exercise system for stroke survivors”, Disabil. Rehabil. Assist. Technol., vol. 5, no. 4, pp. 288-293, Jul. 2010. Show Context CrossRef  Google Scholar 

13. J. Shin et al., “Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded randomized controlled trial”, J. Neuroeng. Rehabil., vol. 13, no. 1, pp. 17, Dec. 2016. Show Context CrossRef  Google Scholar 

14. R. Lipovsky and H. A. Ferreira, “Hand therapist: A rehabilitation approach based on wearable technology and video gaming”, 2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), pp. 1-2, February 2015. Show Context View Article Full Text: PDF (1901KB) Google Scholar 

15. C. Schuster-Amft et al., “Using mixed methods to evaluate efficacy and user expectations of a virtual reality-based training system for upper-limb recovery in patients after stroke: a study protocol for a randomised controlled trial”, Trials, vol. 15, no. 1, pp. 350, Dec. 2014. Show Context CrossRef  Google Scholar 

16. Y. A. Rahman, M. M. Hoque, K. I. Zinnah and I. M. Bokhary, “Helping-Hand: A data glove technology for rehabilitation of monoplegia patients”, 2014 9th International Forum on Strategic Technology (IFOST), pp. 199-204, 2014. Show Context View Article Full Text: PDF (625KB) Google Scholar 

17. M. da Silva Cameirão, S. Bermúdez, I Badia, E. Duarte and P. F. M. J. Verschure, “Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: A randomized controlled pilot study in the acute phase of stroke using the Rehabilitation Gaming System”, Restor. Neurol. Neurosci., vol. 29, no. 5, pp. 287-298, 2011. Show Context CrossRef  Google Scholar 

18. M. R. Golomb et al., “In-Home Virtual Reality Videogame Telerehabilitation in Adolescents With Hemiplegic Cerebral Palsy”, Arch. Phys. Med. Rehabil., vol. 91, no. 1, pp. 1-8, Jan. 2010. Show Context CrossRef  Google Scholar 

19. R. Proffitt, M. Sevick, C.-Y. Chang and B. Lange, “User-Centered Design of a Controller-Free Game for Hand Rehabilitation”, Games Health J., vol. 4, no. 4, pp. 259-264, Aug. 2015. Show Context CrossRef  Google Scholar 

20. N. Arman, E. Tarakci, D. Tarakci and O. Kasapcopur, “Effects of Video Games-Based Task-Oriented Activity Training (Xbox 360 Kinect) on Activity Performance and Participation in Patients with Juvenile Idiopathic Arthritis: A Randomized Clinical Trial”, Am. J. Phys. Med. Rehabil., vol. 98, no. 3, pp. 174-181, 2019. Show Context CrossRef  Google Scholar 

21. S. Cho, W.-S. Kim, N.-J. Paik and H. Bang, “Upper-Limb Function Assessment Using VBBTs for Stroke Patients”, IEEE Comput. Graph. Appl., vol. 36, no. 1, pp. 70-78, Jan. 2016. Show Context View Article Full Text: PDF (4692KB) Google Scholar 

22. E. Tarakci, N. Arman, D. Tarakci and O. Kasapcopur, “Leap Motion Controller-based training for upper extremity rehabilitation in children and adolescents with physical disabilities: A randomized controlled trial”, J. Hand Ther., pp. 1-9, Apr. 2019. Show Context CrossRef  Google Scholar 

23. Y.-T. Wu, K.-H. Chen, S.-L. Ban, K.-Y. Tung and L.-R. Chen, “Evaluation of leap motion control for hand rehabilitation in burn patients: An experience in the dust explosion disaster in Formosa Fun Coast”, Burns, vol. 45, no. 1, pp. 157-164, Feb. 2019. Show Context CrossRef  Google Scholar 

24. T. Vanbellingen, S. J. Filius, T. Nyffeler and E. E. H. van Wegen, “Usability of Videogame- Based Dexterity Training in the Early Rehabilitation Phase of Stroke Patients: A Pilot Study”, Front. Neurol., vol. 8, no. DEC, pp. 1-9, Dec. 2017. Show Context CrossRef  Google Scholar 

25. M. Iosa et al., “Leap motion controlled videogame-based therapy for rehabilitation of elderly patients with subacute stroke: a feasibility pilot study”, Top. Stroke Rehabil., vol. 22, no. 4, pp. 306-316, Aug. 2015. Show Context CrossRef  Google Scholar 

26. A. M. D. C. Souza and S. R. Dos Santos, “Handcopter Game: A Video-Tracking Based Serious Game for the Treatment of Patients Suffering from Body Paralysis Caused by a Stroke”, 2012 14th Symposium on Virtual and Augmented Reality, pp. 201-209, 2012. Show Context View Article Full Text: PDF (795KB) Google Scholar 

27. A. L. Borstad et al., “In-Home Delivery of Constraint-Induced Movement Therapy via Virtual Reality Gaming”, J. Patient-Centered Res. Rev., vol. 5, no. 1, pp. 6-17, Jan. 2018. Show Context CrossRef  Google Scholar 

28. N. J. Seo, J. Arun Kumar, P. Hur, V. Crocher, B. Motawar and K. Lakshminarayanan, “Usability evaluation of low-cost virtual reality hand and arm rehabilitation games”, J. Rehabil. Res. Dev., vol. 53, no. 3, pp. 321-334, Jul. 2016. Show Context CrossRef  Google Scholar 

29. G. C. Burdea, A. Jain, B. Rabin, R. Pellosie and M. Golomb, “Long-term hand tele-rehabilitation on the playstation 3: Benefits and challenges”, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1835-1838, 2011. Show Context View Article Full Text: PDF (479KB) Google Scholar 

30. M. R. Golomb, M. Barkat-Masih, B. Rabin, M. Abdelbaky, M. Huber and G. Burdea, “Eleven Months of home virtual reality telerehabilitation – Lessons learned”, 2009 Virtual Rehabilitation International Conference, pp. 23-28, 2009. Show Context View Article Full Text: PDF (1370KB) Google Scholar 

31. X. Huang, F. Naghdy, G. Naghdy and H. Du, “Clinical effectiveness of combined virtual reality and robot assisted fine hand motion rehabilitation in subacute stroke patients”, 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 511-515, 2017. Show Context View Article Full Text: PDF (1865KB) Google Scholar 

32. G. Tieri, G. Morone, S. Paolucci and M. Iosa, “Virtual reality in cognitive and motor rehabilitation: facts fiction and fallacies”, Expert Rev. Med. Devices, vol. 15, no. 2, pp. 107-117, Feb. 2018. Show Context CrossRef  Google Scholar 

33. B. Garrett, T. Taverner, D. Gromala, G. Tao, E. Cordingley and C. Sun, “Virtual Reality Clinical Research: Promises and Challenges”, JMIR Serious Games, vol. 6, no. 4, pp. e10839, Oct. 2018. Show Context CrossRef  Google Scholar 

34. P. Lankoski, Game Research Methods: An Overview, 2015. Show Context Google Scholar 

Source: https://ieeexplore.ieee.org/abstract/document/9201789

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[Abstract] Functional implications of impaired bimanual force coordination in chronic stroke

Highlights

• We examined the role of bimanual force coordination in bimanual dexterity after stroke.

• Stroke group showed impaired dexterity in a bimanual task with a shared goal.

• Stroke group had poor bimanual coordination of forces during dynamic force modulation.

• Reduced bimanual force coordination predicted impaired dexterity in a bimanual task.

Abstract

Background

The ability to coordinate forces with both hands is crucial for manipulating objects in bimanual tasks. The purpose of this study was to determine the influence of bimanual force coordination on collaborative hand use for dexterous tasks in chronic stroke survivors.

Methods

Fourteen stroke survivors (63.03 ± 15.33 years) and 14 healthy controls (68.85 ± 8.16) performed two bimanual tasks: 1) Pegboard assembly task, and 2) dynamic force tracking task using bilateral index fingers. The Pegboard assembly task required collaborative use of both hands to construct a structure with pins, collars, and washers. We quantified bimanual dexterity with Pegboard assembly score as the total number of pins, collars, and washers assembled in one minute. The force tracking task involved controlled force increment and decrement while tracking a trapezoid trajectory. The task goal was to match the target force with the total force, i.e., sum of forces produced by both hands as accurately as possible. We quantified bimanual force coordination by computing time-series cross-correlation coefficient, time-lag, amplitude of coherence in 0 – 0.5 Hz, and 0.5 – 1 Hz for force increment and decrement phases.

Results

In the Pegboard assembly task, the stroke group assembled fewer items relative to the control group (p = 0.004). In the bimanual force tracking task, the stroke group showed reduced cross-correlation coefficient (p = 0.01), increased time-lag (p = 0.00), and reduced amplitude of coherence in 0 – 0.5 Hz (p = 0.03) and in 0.5 – 1 Hz (p = 0.00). Multiple regression analysis in the stroke group revealed that performance on Pegboard assembly task was explained by cross-correlation coefficient and coherence in 0.5 – 1 Hz during force increment (R2 = 0.52, p = 0.00).

Conclusions

Individuals with stroke show impaired bimanual dexterity and diminished bimanual force coordination. Importantly, stroke-related deterioration in bimanual force coordination is associated with poor performance on dexterous bimanual tasks that require collaboration between hands. Re-training bimanual force coordination in stroke survivors could facilitate a higher degree of participation in daily activities through improved bimanual dexterity.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0304394020306571

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[Abstract] Motiv’Handed, a New Gamified Approach for Home-Based Hand Rehabilitation for Post-stroke Hemiparetic Patients – Conference paper

Abstract

This document summarizes a master thesis project trying to bring a new solution to hemiplegia rehabilitation, one of the numerous consequences of strokes. A hemiplegic patients observe paralysis on one side of their body, and as so, loses autonomy and their quality of life decreases. In this study, we decided to only focus on the hand rehabilitation aspect. However, there is a clear tendency in stroke patients to stop training regularly when returning home from the hospital and the first part of their rehabilitation is over. They often experience demotivation, having the feeling that they will never get back to a fully autonomous person ever again and tend to put their training aside, especially when they do not see clear and visible results anymore. This is also due to the supervised training becoming sparser. All of this results in patients stagnating or even worse, regressing. Thus, we decided to offer a motivating solution for hand rehabilitation at home through gamification.

References

  1. 1.Stroke Paralysis. Portea. https://www.portea.com/physiotherapy/stroke-paralysis#section_1. Accessed 15 June 2020
  2. 2.Recovering from Hand Weakness after Stroke. Saebo. https://www.saebo.com/stroke-hand-weakness-recovery/. Accessed 15 June 2020
  3. 3.UHMA, a new solution for post-stroke home-based hand rehabilitation for patient with hemiparese, Duval–Dachary Sarah, Master thesis (2019)Google Scholar
  4. 4.Motiv’Handed, a new home-based hand rehabilitation device for post-stroke hemiparetic patients, Chevalier–Lancioni Jean-Philippe, Master Thesis (2020)Google Scholar
  5. 5.WIM, Jenny Holmsten website. https://www.jennyholmsten.com/wim. Accessed 15 June 2020
  6. 6.Carneiro, F., Tavares, R., Rodrigues, J., Abreu, P., Restivo, M.: A gamified approach for hand rehabilitation device. Int. J. OnlineGoogle Scholar
  7. 7.Engineering (iJOE), January 2018. Virtual reality for therapeutic purposes in stroke: A systematic review. S. Viñas-Diza, M. Sobrido-Prieto. s.l. : Elsevier España, S.L.U (2015)Google Scholar

Source: https://link.springer.com/chapter/10.1007/978-3-030-58796-3_22

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[ARTICLE] Evidence of Neuroplasticity: A Robotic Hand Exoskeleton Study for Post-Stroke Rehabilitation – Full Text PDF

Abstract

Background: A novel electromechanical robotic-exoskeleton was designed in-house for rehabilitation of wrist joint and Metacarpophalangeal (MCP) joint.

Objective: The objective was to compare the rehabilitation effectiveness (clinical-scales and neurophysiological-measures) of robotic-therapy training-sessions with dose-matched control in patients with stroke.

Methods: An observational pilot study was designed with patients within 2 years of chronicity. Patients received an intervention of 20 sessions of 45-minutes each, five days a week for four-weeks) in Robotic-therapy Group (RG) (n=12) and conventional upper-limb rehabilitation in Control-Group (CG) (n=11). Clinical-scales– Modified Ashworth Scale, Active Range of Motion, Barthel-Index, Brunstrom-stage and Fugl-Meyer scale (Shoulder/Elbow and Wrist/Hand component), and neurophysiological-measures of cortical-excitability (using Transcranial Magnetic Stimulation) –Motor Evoked Potential and Resting Motor-threshold, were acquired pre and post-therapy.

Results: RG and CG showed significant improvement in all clinical motor-outcomes (p<0.05) except Modified Ashworth Scale in CG. RG showed significantly higher improvement over CG in Modified Ashworth Scale, Active Range of Motion and Fugl-Meyer (FM) scale and FM Wrist-/Hand component) (p<0.05). Increase in cortical-excitability in ipsilesional-hemisphere was found to be statistically significant in RG over CG, as indexed by decrease in Resting Motor-Threshold and increase in amplitude of Motor Evoked Potential (p<0.05). No significant changes were shown by the contralesional-hemisphere. Interhemispheric RMT-asymmetry evidenced significant changes in RG over CG (p<0.05) indicating increased cortical-excitability in ipsilesional-hemisphere along with interhemispheric changes.

Conclusion: Neurophysiological-changes in RG could be most likely a consequence of plastic-reorganization and use-dependent plasticity. Robotic-exoskeleton training could significantly improve motor-outcomes and cortical-excitability in patients with stroke.


1. Introduction

Stroke is one of the leading causes of mortality and morbidity worldwide (1). The ability to actively initiate extension movements at wrist and fingers against flexor-hypertonia is one of the key indicators of motor recovery (2),(3). Regaining hand-function and Activities of daily-living (ADL) is particularly impervious to therapy or rehabilitation pertaining to the complexity of motor-control needed for distal-joints (4). Conventional rehabilitation-therapy is time taking, labour-intensive and subjective, which with high clinical-load and absence of skilled resources gets difficult for the present medical and healthcare-system to provide appropriate or effective rehabilitation services (5).

Although rehabilitation with neuro-rehabilitation robots has shown encouraging clinical-results (5, 6, 15, 7–14), it is currently limited to a very few hospitals and not widely used because of associated high-cost and an infrastructural-requirement to station, size, complexity, set-up time, safety and usability restricting its success (16),(17),(18). Rehabilitation-strategies need to take into account the multifaceted nature of disability, which itself changes with time elapsed post-stroke and address with a multimodal-approach. Hence, the device needs to be flexible enough to accommodate a large patient-population. An effective rehabilitation device for hand should be able to facilitate a specific pattern of movements mirroring complex inter-joint coordination of hand with a patient-specific impairment, currently not integrated by the available devices.

In our previous work, we designed a robotic-hand exoskeleton for rehabilitation of the wrist and MCP (Metcarpo-phallengeal) joint, to synchronize wrist-extension with finger-flexion and wrist-flexion with finger-extension, mimicking ADL (19). With simple and easy-to-operate exoskeleton for low-resource settings, the exoskeleton targets spasticity through a synergy-based rehabilitation approach while also maintaining patient-initiated therapy through residual muscle-activity for maximizing voluntary effort. The lightweight and portable device has shown evidence of improvement in quantitative motor clinical-outcomes in patients with chronic stroke (19).

The aim of the present study was twofold. The first objective was to assess the clinical effectiveness of the novel robotic-exoskeleton device (19) and the second is comparison of its clinical-effectiveness with conventional upper-limb rehabilitation. We hypothesized that the exoskeleton could show higher improvement of distal-function and cortical-excitability in patients with stroke as compared to conventional-rehabilitation.[…]

Continue —-> https://www.researchsquare.com/article/rs-67841/v1

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Figure 2
Figure 2

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[Conference Paper] HandMATE: Wearable Robotic Hand Exoskeleton and Integrated Android App for At Home Stroke Rehabilitation – Full Text

Abstract

We have developed HandMATE (Hand Movement Assisting Therapy Exoskeleton); a wearable motorized hand exoskeleton for home-based movement therapy following stroke. Each finger and the thumb is powered by a linear actuator which provides flexion and extension assistance. Force sensitive resistors integrated into the design measure grasp and extension initiation force. An assistive therapy mode is based on an admittance control strategy. We evaluated our control system via subject and bench testing. Errors during a grip force tracking task while using the HandMATE were minimal (<1%) and comparable to unassisted healthy hand performance. We also outline a dedicated app we have developed for optimal use of HandMATE at home. The exoskeleton communicates wirelessly with an Android tablet which features guided exercises, therapeutic games and performance feedback. We surveyed 5 chronic stroke patients who used the HandMATE device to further evaluate our system, receiving positive feedback on the exoskeleton and integrated app.

SECTION I.

Introduction

Stroke is the leading cause of severe long-term disability in the US [1]. The probability of regaining functional use of the impaired upper extremity is low [2]. At 6 months post stroke, 62% of survivors failed to achieve some dexterity [3]. Such impairments can inhibit the individual’s ability to perform activities of daily living (ADL). Subsequently, upper limb rehabilitation recovery to improve ADL is one of the main self-reported goals of stroke survivors [4].

Outpatient rehabilitation is recommended for survivors that have been discharged from inpatient rehabilitative services [5]. However, outpatient rehabilitation in general is largely underutilized, with only 35.5% of stroke survivors using services [6]. Factors inhibiting outpatient therapy include cost, lack of resources and transportation. Wearable robotics that enable home-based therapy have the potential to overcome these barriers. They provide assistive movement forces which enable task-specific training in real-life situations that patients are often unable to practice without a clinician. See [7] for wearable hand robots for rehabilitation review.

At home therapy is not without its limitations. The inability to motivate oneself and fatigue are the most common reported factors resulting in failure to adhere to home based exercise programs for stroke recovery [8]. While wearable robotics can reduce fatigue during exercise, it does not directly address lack of motivation. Research has shown incorporating games into home therapy can encourage compliance [9]. Zondervan et al. showed that use of an instrumented sensor glove, named the MusicGlove, improved self-reported use and quality of movement, greater than convention at home exercises [9]. Other studies showed increased motivation to complete the therapeutic exercises and optimized movement when the user is given feedback of their performance via the Microsoft Kinect [10]. Wearable robotic systems that offer feedback and gaming capability may optimize at home stroke therapy.

Such a system was presented by Nijenhuis et al. in which stroke survivors showed motor improvements after completing a 6 week self-administered training program comprised of a dynamic hand orthosis and gaming environment [11]. However, the hand device was passive, assisting only with extension, which limits the range of stroke survivors who could utilize such a system. Research groups have proposed combining their powered take-home wearable hand devices with custom integrated gaming systems [12], or guided exercises [13]; however, they have yet to conduct clinical trials. Notably, Ghassemi et al., have developed an integrated multi-user VR system to use with their X-Glove actuated orthosis, which will allow for client-therapist sessions without the patient having to travel [12].

Tablets are relatively inexpensive, portable, and straight forward to use, with 47% of internet users globally already owning one [14]. Furthermore, a recent study demonstrated the success of a tablet based at home exercise program in improving the recovery of stroke survivors [15]. Notably, the study evaluated the accessibility of tablets, concluding every participant used the tablet successfully. Therefore a wearable powered hand robot with a dedicated tablet app which will provide functional games, task-specific guided exercises and feedback of movement, could optimize at home stroke therapy.

SECTION II.

Aims

The goal of this project was to create a wearable robotic exoskeleton that enables repetitive practice of task-specific and goal orientated movements, which translates into improvements in ADL. Furthermore, for maximum use and successful integration into home-based rehabilitation, we aimed to create an Android application compatible with the robotic exoskeleton.

To meet these goals, the following design objectives were established: 1) Assistance with finger flex/extension. 2) Assistance with thumb carpometacarpal (CMC) add/abduction and thumb metacarpophalangeal (MCP) flex/extension. 3) Independent assistive control of each finger and thumb. 4) Portable for at home use, meaning the device has to be lightweight and wireless. 5) Relatively affordable. 6) Integrated with android tablet app. Specific design goals for the app included: 1) Easy to use. 2) Allow the user to control the exoskeletons assistance mode through the app. 3) Records the user’s data and prompts the user via notifications to complete the allocated daily or weekly recommended activity time.

In this paper we will evaluate if the proposed device and app goals have been achieved via bench and subject testing.

SECTION III.

Design

The HandMATE device (Fig. 1) builds upon the Hand Spring Operated Movement Enhancer (HandSOME) devices [16][17][18]. The HandSOME devices are non-motorized wearable exoskeletons that assists stroke patients with finger and thumb extension movements. The HandSOME I device assists with gross whole hand opening movements, while the HandSOME II assists isolated extension movement of 15 finger and thumb degrees of freedom (DOF), allowing performance of various grip patterns used in ADL. While both devices have been shown to significantly increase range of motion (ROM) and functional ability in chronic stroke subjects [16],[18], the HandSOME devices only assist with extension movements and require enough flexion activity to overcome the assistance of the extension springs. As many stroke patients also suffer finger and thumb flexion weakness, we decided to build upon the work of the high DOF HandSOME II and additionally utilize power actuation so we can assist with both flexion and extension movements.

Figure 1: - 
HandMATE device. Individually actuated fingers and thumb shown. Electronics box is affixed to back of splint.
Figure 1:
HandMATE device. Individually actuated fingers and thumb shown. Electronics box is affixed to back of splint.

Continue —-> https://ieeexplore.ieee.org/abstract/document/9175332

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[ARTICLE] Development of the Home based Virtual Rehabilitation System (HoVRS) to Remotely Deliver an Intense and Customized Upper Extremity Training – Full Text PDF

Abstract

Background: After stroke, sustained hand rehabilitation training is required for continuous improvement
and maintenance of distal function.
Methods: In this paper, we present a system designed and implemented in our lab: the Home based
Virtual Rehabilitation System (HoVRS). Fifteen subjects with chronic stroke were recruited to test the
feasibility of the system as well as to rene the design and training protocol to prepare for a future
ecacy study. HoVRS was placed in subjects’ homes, and subjects were asked to use the system at least
15 minutes every weekday for 3 months (12 weeks) with limited technical support and remote clinical
monitoring.
Results: All patients completed the study without any adverse events. Subjects on average spent 13.5
hours using the system. Clinical and kinematic data were collected pre and post study. The whole group
improved on the Fugl-Meyer (FM) assessment and on six kinematic measurements. In addition, a
combination of these kinematic measures was able to predict a substantial portion of subjects’ FM
scores.
Conclusion: The outcomes of this pilot study warrant further investigation of the system’s ability to
promote recovery of hand function in subacute and chronic stroke[…]

Full Text PDF

Figure 1
Figure 1
HoVRS sub-systems diagram and types of arm positioning. A: HoVRS sub-systems diagram: The clientbased platform provides hand and arm training. A cloud-based data server provides secure data
streaming, analysis and presenting. Therapists can access patients’ progress through web portal. Two
different types of arm positioning above LMC: B: Passive arm support. C: Hip wedge

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[Abstract] DESC glove: Prototyping a novel wearable device for post-stroke hand rehabilitation

Abstract

The human brain integrates tactile sensory information from the fingertips to efficiently manipulate objects. Sensory impairments due to neurological disorders, e.g. stroke, largely reduce hand dexterity and the ability to perform daily living activities. Several feedback augmentation techniques have been investigated for rehabilitative purposes with promising outcomes. However, they often require the use of unpractical, expensive, or complex devices. In this work we propose the delivery of vibrotactile feedback based on the Discrete Event-driven Sensory feedback Control (DESC) to promote motor learning in post stroke rehabilitation. For this purpose, we prototyped a novel wearable device, namely the DESC glove. It consisted of a soft glove instrumented with PolyVinylidene Fluoride (PVDF) sensors at the fingertips and eccentric-mass vibration actuators to be worn on the forearm. We proceeded with the characterization of the device, which resulted in promising outcomes. The DESC glove was tested with ten healthy participants subsequently in a pick and lift timed task. The effects of augmented vibrotactile feedback were assessed comparing it to a baseline, consisting of wearing the device unpowered. The results of this pilot study showed a decrease in the time necessary to perform the task, a reduction in the time delay from load force to grip force activation and a diminishing of the grip force applied on the object, which led to a lower breakage rate in the intervention condition. These promising outcomes encourage further experiments with stroke survivors to validate the effectiveness of the device to improve hand dexterity and promote stroke rehabilitation.

via DESC glove | TU Delft Repositories

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[BLOG POST] Hand Rehab after Stroke: The Top 5 Evidenced-Based Methods

After a stroke, it’s challenging enough to navigate the medical system to find what services you need, let alone the right treatment approach for you.

You’ve probably heard a lot of recommendations on how to recover hand function after stroke, and everyone seems to give different advice. That’s why we sifted through the research for you. We’ll explain the top 5 evidence-based methods for hand rehabilitation, why they work, and who they work for.

The top 5 evidence-based treatments for improving hand function after stroke:

  1. Constraint‐induced movement therapy (CIMT)
  2. Mental practice
  3. Mirror therapy
  4. Virtual reality
  5. High dose repetitive task practice

Constraint-Induced Movement Therapy

Unaffected arm wearing oven mitt for at-home constraint therapy.
You can restrict your unaffected side at home by wearing an oven mitt or placing it inside your pants or sweatshirt pocket. This will help remind you to rely on your affected side when completing therapy tasks.

What it is:

Constraint-Induced Movement Therapy (CIMT) is a neuro-rehabilitation method where the non-affected hand is constrained or restricted in order to force the brain to use the affected hand, thereby increasing neuroplasticity.

There are two key components: constraint and shaping.

Constraint refers to the way in which the hand is restricted. Therapists have used casts, splints, and mitts to restrict the use of the non-affected hand. None of them have been shown to be more effective than the other.

Shaping involves repetitive movements or activities at the patient’s ability level which become progressively harder. Therapists use shaping techniques to avoid overwhelming the motor system.

Why it works:

Our brain automatically completes a task in the easiest way possible. Our brain is more interested in completing a task than in how it is accomplished.

After a stroke, it’s easier for our brain to do tasks one-handed. This leads to “learned non-use”.

When we constrain our non-affected hand, suddenly our stronger hand becomes the weaker, less functional hand and we’re forced to use our affected hand. Our affected hand might not have much movement, but to our brain any movement is better than no movement, and the brain is highly motivated to figure out how to accomplish a task.

This is where the “shaping” piece is so important. If you are presented with rehab tasks that overwhelm the motor system or are higher level than your affected hand can functionally do, you’ll be more likely to knock the table over than to participate in picking up pennies from the table.

If you knock the table over with your affected hand, your occupational therapist might actually be excited about it; but in practical life finding that balance of not being too easy and not being so hard that you give up is an important lesson for every human being, not just those after stroke.

Who it’s for:

This approach is used for people who have at least 10 degrees of active wrist and finger extension, as well as 10 degrees of thumb abduction (the ability of the thumb to move out of the palm).

It’s been shown to be effective even years after stroke. Lower intensity CIMT is better than higher intensity in the very early stages after stroke.

Mental Practice

Man in headphones listening to mental practice recordings.
You might listen to an audio recording describing the sequence of throwing a ball, imagining yourself doing it. After listening, actually practice throwing the ball the way you envisioned!

What it is:

Mental practice, sometimes called motor imagery or mental imagery, is a training method for improving your hand and arm function without moving a muscle!

Mental practice is typically done by listening to pre-recorded audio that describes in detail the motor movement of a specific task. The listener imagines their hand and arm moving in a “typical” way, and the instructor provides cues to extend their arm or open their fingers, as well as the entire sensory experience of the task.

While it’s true that you can do mental practice on its own, it’s best combined with physical practice immediately following.

Why it works:

Brain scans show that similar parts of the brain are activated whether movement is actual, observed or imagined.

It’s a separate area of the brain that’s responsible for actually triggering the muscle movement, but it goes to show that there’s a lot more required of the brain to complete a task than just sending a signal to the muscle.

Who it’s for:

Mental practice has been shown to improve arm movement and functional use in patients after stroke of all levels of abilities and as a treatment approach for people months or years after stroke!

Mirror Therapy

Unaffected hand and its mirror image reflected in mirror box.
It is critical to stay focused on the reflected image of your hand during mirror therapy, imagining that it is your affected side performing the target movements.

What it is:

Mirror therapy is another voodoo-seeming approach that has a lot of scientific evidence to back it up. It essentially tricks your brain into thinking your affected hand is moving.

You position a mirror to reflect your non-affected hand, while hiding your affected hand. Any movement of your non-affected hand will be reflected in the mirror and make it seem as though you are actually moving your affected hand.

Why it works:

The approach is centered around mirror neurons, which fire in your brain when you see your arm move. Typically, we think about motor neurons being sent from the brain to the muscle, but we don’t realize that mirror neurons are connected to the motor neurons.

After a stroke you lose the ability to access your motor neurons, but not your mirror neurons. By accessing your mirror neurons through seeing your movement (even if the movement is fake), you are tapping into the network between the neurons.

It’s like trying to reconnect with an old friend on Facebook by finding the friends they’re connected with. It might not be the most direct approach in a real life situation, but in stroke rehab that friend of a friend might be your strongest connection.

Who it’s for:

Mirror therapy can be used for people with no movement of the hand or smaller movements of the hand and shoulder, but not functional movement of the hand.

If you have functional movement of your hand, meaning individual finger movement and wrist movement, you have surpassed the benefit that mirror therapy can provide.

It can be used early after stroke, as well as in the chronic stages of stroke.

Virtual Reality

Neofect Smart Board virtual reality arm exercise system.
The Neofect Smart Board is a non-immersive virtual reality rehabilitation system.

What it is:

Virtual reality uses a computer interface to simulate a real life objects and events. It’s become an increasingly more prevalent rehabilitation technique to provide motivation and engagement in therapy.

There are two types:

  1. Immersive: goggles are placed over the eyes and the patient is visually in a different environment than their actual physical one
  2. Non-immersive: sensors are placed on the body and track the movement of the body and the movements are shown on a screen

Why it works:

Virtual reality works best when paired with traditional therapy. It’s theorized to provide more motivation and engagement for the intensity of therapeutic exercise needed for neuroplasticity. It’s been shown to beneficial in high doses, meaning more than 20 hours.

Another possible factor of why virtual reality works are the same mechanisms that make mirror therapy effective (tapping into the mirror neurons) could be similar.

Virtual reality also creates a biofeedback loop: your brain sends a signal to the muscle, the brain receives a signal back in the form of visual or auditory input. Basically, you get rewarded for your effort.

Who it’s for:

Virtual reality can be used with people who have mild to severe impairments, and from early after stroke to years out.

When deciding what’s right for you, it’s important to look at the adjustability of the device to meet you where you’re at and also to increase in difficulty as you improve.

If you have minimal movements, you’ll want a virtual reality tool specifically for stroke rehabilitation. If you have more movement, it’s possible to use gaming systems not specifically designed for rehab, but make sure you have the support to optimize it for rehab.

High Dose Repetitive Task Practice

Putting coins in a piggy bank during repetitive task practice.
There are many ways to do task-specific training at home. Placing coins into a piggy bank is just one of them!

What it is:

Repetitive Task Practice is when you practice a task or a part of a task over and over. Task-specific training is a type of repetitive task practice, and refers to the task we complete that is relevant to our daily life.

“Reach to grasp, transport and release” is a type of task-specific training because it is one of the common motor requirements for many functional daily tasks.

The keys for repetitive task practice:

  • Task must be meaningful
  • Participant must be an active problem-solver
  • Real life objects are used
  • Difficulty level is not too high and not too low
  • Repetition is key

Why it works:

Repetitive Task Practice is based on motor learning theory. Our brains are driven by function. We’re able to achieve neuroplasticity with development of skills, as our brain processes the demands of the task, which have motor and cognitive components.

It’s often used with other treatments, such as virtual reality, to increase the 15 hour dosage that has been shown to be beneficial.

Who it’s for:

Task-specific practice is generally used and is studied in people who have some functional ability of their hand. It’s been shown to be beneficial throughout the rehabilitation process.

Even though the research has been focused on “functional ability” of the hand by practicing reach, grasp, transport, release; there’s potential for recovery by using the same principles of task-specific practice: real life objects, functional tasks, and problem-solving even without the ability to grasp.

Functionally, we can use our affected upper extremity as a stabilizer, an assist, or for manipulation. There are lots of ways to get that side involved to prevent “learned non-use” and to improve your problem-solving skills.

Now what?

There are two key factors to any hand recovery method: support and meaning.

Neofect aims to support and inspire you to live your best life with virtual reality tools that can be used as part of a constraint-induced movement therapy program or with repetitive task practice.

Our comprehensive recovery and wellness app: Neofect Connect and our YouTube Channel: Find What Works are based on the principles of repetitive task practice and aim to give you the tools to live your best life.

Now the only question is, what are you waiting for?

Pollock  A, Farmer  SE, Brady  MC, Langhorne  P, Mead  GE, Mehrholz  J, van Wijck  F. Interventions for improving upper limb function after stroke. Cochrane Database of Systematic Reviews 2014, Issue 11. Art. No.: CD010820. DOI: 10.1002/14651858.CD010820.pub2.

via Hand Rehab after Stroke: The Top 5 Evidenced-Based Methods

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[Abstract] Neural coordination of bilateral power and precision finger movements

Cover imageAbstract

The dexterity of hands and fingers is related to the strength of control by cortico‐motoneuronal connections which exclusively exist in primates. The cortical command is associated with a task‐specific, rapid proprioceptive adaptation of forces applied by hands and fingers to an object. This neural control differs between “power grip” movements (e.g., reach and grasp of a cup) where hand and fingers act as a unity and “precision grip” movements (e.g., picking up a raspberry) where fingers move independently from the hand.

In motor tasks requiring hands and fingers of both sides a “neural coupling” (reflected in bilateral reflex responses to unilateral stimulations) coordinates power grip movements (e.g., opening a bottle). In contrast, during bilateral precision movements, such as playing piano, the fingers of both hands move independently, due to a direct cortico‐motoneuronal control, while the hands are coupled (e.g., to maintain the rhythm between the two sides).

While most studies on prehension concern unilateral hand movements, many activities of daily life are tackled by bilateral power grips where a neural coupling serves for an automatic movement performance. In primates this mode of motor control is supplemented by a system that enables the uni‐ or bilateral performance of skilled individual finger movements.

via Neural coordination of bilateral power and precision finger movements – Dietz – – European Journal of Neuroscience – Wiley Online Library

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[Abstract + References] sEMG-biofeedback armband for hand motor rehabilitation in stroke patients: a preliminary pilot longitudinal study – IEEE Conference Publication

Abstract

Upper limb motor impairment is one of the most debilitating sequelae after stroke, thus the aim of rehabilitation is to promote functional recovery and improve quality of life. Surface Electromyography Biofeedback (sEMG-BFB) is a therapeutic tool based on providing amplified neuromuscular information on motor performance to the patient, for enhancing motor learning and driving to a successful recovery. A preliminary pilot longitudinal study was carried out to preliminarily investigate any clinical and instrumental effect due to an innovative treatment based on sEMG-BFB, in stroke survivors. Fifteen stroke patients with impairment of hand function were enrolled for a 3-weeks- training with REcognition MOvement (REMO®), a sEMG-BFB armband, clinical and instrumental assessments were administered before and after the training. After training, statistically significant differences were observed at the Box and Block Test (BBT) and in the relation between changes at BBT and chMAX-chMIN of wrist extension movement. Our results indicated that improvement in the device control is associated to a better hand function. Further studies need to be conducted to investigate the feasibility of using REMO® to study motor behavior in both healthy and diseased subjects.
1. R. L. Sacco et al., “AHA / ASA Expert Consensus Document An Updated Definition of Stroke for the 21st Century A Statement for Healthcare Professionals From the American Heart Association / American Stroke Association”, Stroke, pp. 2064-89, 2013.

3. A. Italiana et al., “SPREAD – Stroke Prevention and Educational Awareness Diffusion”, 2016.

4. P. Langhorne, F. Coupar and A. Pollock, “Motor recovery after stroke : a systematic review”, Lancet Neurol, vol. 8, no. 8, pp. 741-754, 2009.

5. S. Balasubramanian, J. Klein and E. Burdet, “Robot-assisted rehabilitation of hand function”, Curr. Opin. Neurol, pp. 661-670, Dec. 2010.

6. F. E. Buma, E. Lindeman, N. F. Ramsey and G. Kwakkel, “Functional Neuroimaging Studies of Early Upper Limb Recovery After Stroke : A Systematic Review of the Literature”, Neurorehabil Neural Repair, pp. 589-608, Sep. 2010.

7. A. Pollock et al., “Interventions for improving upper limb function after stroke (Review)”, The Cochrane Database of Systematic Reviews, no. 11, pp. 1-172, Nov. 2014.

8. J. A. Kleim and T. A. Jones, “Principles of Experience-Dependent Neural Plasticity : Implications for Rehabilitation After Brain Damage”, J. Speech Lang. Hear. Res, vol. 51, pp. 225-240, Feb. 2008.

9. J. W. Krakauer and P. Mazzoni, “Human sensorimotor learning : adaptation skill and beyond”, Curr. Opin. Neurobiol, vol. 21, no. 4, pp. 636-644, Aug. 2011.

10. O. M. Giggins, U. M. Persson and B. Caulfield, “Biofeedback in rehabilitation”, J. Neuroeng. Rehabil, pp. 1-11, Jun. 2013.

11. D. Farina et al., “The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses : Emerging Avenues and Challenges”, IEEE Trans Neural Syst Rehabil Eng, vol. 22, no. 4, pp. 797-809, Feb. 2014.

12. O. Armagan, F. Tascioglu and C. Oner, “Electromyographic Biofeedback in the treatment of the Hemiplegic Hand: A placebo-controlled study”, Am J Phys Med Rehabil, vol. 82, pp. 856-861, Nov. 2003.

13. M. Lyu et al., “Training wrist extensor function and detecting unwanted movement strategies in an EMG-controlled visuomotor task”, Int Conf Rehabil Robot, pp. 1549-1555, 2017.

14. W. Hj and P. Cim, “EMG biofeedback for the recovery of motor function after stroke ( Review )”, pp. 1-19, 2009.

15. R. Neblett, “Surface Electromyographic (SEMG) Biofeedback for Chronic Low Back Pain”, Healthcare, 2016.

16. M. Di Girolamo, A. Favetto, M. Paleari, N. Celadon and P. Ariano, “A comparison of sEMG temporal and spatial information in the analysis ofcontinuous movements”, Informatics in Medicine Unlocked, vol. 9, pp. 255-263, 2017.

17. V. Mathiowetz, G. Volland, N. Kashman and K. Weber, “Adult norms for the Box and Block Test of Manual Dexterity”, Am J Occup Ther, vol. 39, pp. 386-391, Jun. 1985.

18. J. Inglis, M.W. Donald, TN Monga, M. Sproule and MJ Young, “Electromyographic biofeedback and physical therapy of the hemiplegic upper limb”, Arch Phys Med Rehabil, vol. 65, pp. 756-759, Dec. 1984.

19. C.E. Lang et al., “Assessment of upper extremity impairment function and activity after stroke: foundations for clinical decision making”, J. Hand Ther, vol. 26, no. 2, pp. 104-115, Apr. 2013.

20. L.A. Connell and S.F. Tyson, “Clinical reality of measuring upper-limb ability in neurologic conditions: a systematic review”, Arch Phys Med Rehabil, vol. 93, pp. 221-228, Feb. 2012.

via sEMG-biofeedback armband for hand motor rehabilitation in stroke patients: a preliminary pilot longitudinal study – IEEE Conference Publication

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