[Abstract] Autonomous Use of the Home Virtual Rehabilitation System: A Feasibility and Pilot Study

Abstract

Objective: This article describes the findings of a study examining the ability of persons with strokes to use home virtual rehabilitation system (HoVRS), a home-based rehabilitation system, and the impact of motivational enhancement techniques on subjects’ motivation, adherence, and motor function improvements subsequent to a 3-month training program.

Materials and Methods: HoVRS integrates a Leap Motion controller, a passive arm support, and a suite of custom-designed hand rehabilitation simulations. For this study, we developed a library of three simulations, which include activities such as flexing and extending fingers to move a car, flying a plane with wrist movement, and controlling an avatar running in a maze using reaching movements. Two groups of subjects, the enhanced motivation (EM) group and the unenhanced control (UC) group, used the system for 12 weeks in their homes. The EM group trained using three simulations that provided 8–12 levels of difficulty and complexity. Graphics and scoring opportunities increased at each new level. The UC group performed the same simulations, but difficulty was increased utilizing an algorithm that increased difficulty incrementally, making adjustments imperceptible.

Results: Adherence to both the EM and UC protocols exceeded adherence to home exercise programs described in the stroke rehabilitation literature. Both groups demonstrated improvements in upper extremity function. Intrinsic motivation levels were better for the EM group and motivation levels were maintained for the 12-week protocol.

Conclusion: A 12-week home-based training program using HoVRS was feasible. Motivational enhancement may have a positive impact on motivation, adherence, and motor outcome.

 

via Autonomous Use of the Home Virtual Rehabilitation System: A Feasibility and Pilot Study | Games for Health Journal

[ARTICLE] Gamification in a Physical Rehabilitation Setting: Developing a Proprioceptive Training Exercise for a Wrist Robot – Full Text

Proprioception or body awareness is an essential sense that aids in the neural control of movement. Proprioceptive impairments are commonly found in people with neurological conditions such as stroke and Parkinson’s disease. Such impairments are known to impact the patient’s quality of life. Robot-aided proprioceptive training has been proposed and tested to improve sensorimotor performance. However, such robot-aided exercises are implemented similar to many physical rehabilitation exercises, requiring task-specific and repetitive movements from patients. Monotonous nature of such repetitive exercises can result in reduced patient motivation, thereby, impacting treatment adherence and therapy gains. Gamification of exercises can make physical rehabilitation more engaging and rewarding. In this work, we discuss our ongoing efforts to develop a game that can accompany a robot-aided wrist proprioceptive training exercise.

 

Figure 1: Left. WristBot being used by a participant. Right. Screenshot of the virtual environment showing an avatar controlled by user’s wrist movements.

1 INTRODUCTION

Proprioception, the sense of body awareness, is essential for normal motor function. Proprioceptive deficits are common in neurological conditions [Coupar et al. 2012; Konczak et al. 2009]. Such deficits cause a decline in precision of goal-directed movements, and altered postural and spinal reflexes resulting in balance and gait problems [Rothwell et al. 1982]. Proprioceptive training is an intervention aiming to improve proprioceptive function [Aman et al. 2015]. Previous work has established the efficacy of a robot-aided proprioceptive training using WristBot [Elangovan et al. 2017, 2018, 2019]. The WristBot (Figure 1. Left) is a three degrees-of-freedom (3-DoF) exoskeleton robot that allows full range of motion (ROM), delivers precise haptic, position, and velocity stimuli at the wrist, and accurately encodes wrist position across time. Additional details about the WristBot can found in [Cappello et al. 2015].

Nevertheless, while the WristBot has demonstrated its efficacy, it shares a limitation that is often encountered in rehabilitation settings. In a clinical setting, patients are often required to perform task-specific and repetitive movements [Kwakkel et al. 1999]. Initial patient enthusiasm to complete such activities rapidly declines as a result of the monotonous nature of movements. Patient engagement can be improved by complementing therapy with a virtual environment (VE). Prior research has shown that users have favored exercises complemented with a VE rather than conventional approaches [Hoffman et al. 2014]. Thus, our project objective is to turn these tedious movements into an interactive VE experience.

2 GAMIFICATION OF PROPRIOCEPTIVE TRAINING

Gamification process accounted for two key considerations: (1) the game should foster patient motivation and attention (2) and be clinically meaningful. To address these objectives, we reviewed the literature on game development [Bond 2014; Fullerton 2018] and identified four essential components: (1) Variability, (2) Feedback, (3) Rewards, and (4) a Compelling Purpose. The user will be gradually exposed to increasing levels of difficulty, which will likely reduce user frustrations. The user will receive meaningful feedback on concurrent metrics (e.g., Optimal ROM), as well as on previous treatment sessions. During game progress, the user will be alerted about deviations from the target movement requirements. Achievement badges will be rewarded to the user upon reaching therapy milestones, such as target ROM. Lastly, to encourage game completion, we establish an interesting backstory and a meaningful character arc for our virtual avatars. The developed game will be adaptable based on the user’s current clinical status, thus, making the game clinically meaningful. The clinician will have the ability to prescribe exercises based on user needs such as 1 DoF vs 3 DoF movements, continuous vs discrete movements, and strength training vs mobility training. WristBot will provide supportive forces aiding the user to achieve therapy milestones.

Gamified exercise is being developed using the Unity Game Engine, Python and libraries which interface with the WristBot. The game closely resembles an endless runner type game (Figure 1. Right) and utilizes the WrsitBot’s 3-DoF functionality to interact with the VE. Wrist flexion, extension, and abduction can be used to traverse their environment. The remaining 3 movements will allow interactions with their VE in unique ways, such as opening/closing doors, crouching, and pulling levers. In the VE, coins are strategically placed to maximize and improve the use of available ROM. Upon contact with either a wall or obstacle, visual feedback will be provided in the form of avatar damage and coin deduction. Consequently, users achieve improved mobility.

In Python, the connection between Unity and the WristBot library is managed through the use of a local WebSocket, a protocol for two-way communication over a single Transmission Control Protocol (TCP) connection [Fette and Melnikov 2011]. Through the WebSocket, reciprocal data are transferred between the WristBot and Unity. For example, wrist kinematic data will be streamed to the game while game progress is being relayed to the WristBot library. Game progress data will be utilized to compute and deliver haptic feedback to the user. Haptic feedback provided in the form of haptic assistance will aid users to improve their available ROM, while haptic resistance will improve muscle strength within the desired ROM. The clinical motive of the game is to transition the user from use of haptic assistance to resistance during game play. WristBot will adapt haptic feedback based on time spent and progress achieved in game play.

3 USABILITY TESTING

Usability testing will be conducted to ensure proper game usage by the clinical population and healthcare professionals. Specifically, the usability testing will evaluate areas such as 1) ease of game play, 2) game efficiency, and 3) user engagement. We will test the assumptions in each of these areas are accurately depicted in game development and met during game play. For example, we expect online visual feedback of deviations from target to help user focus on achieving the movement requirements. The users will be asked to verify the benefits of visual feedback in modifying their movements. Similarly, other assumptions such as performance badges and coins as rewards, and increase in difficulty levels will be evaluated. A common pitfall of usability studies involving physical rehabilitation setting is not recruiting from the representative population, most notably elderly population [Laver et al. 2017] as age has been shown to interfere with interactions in VE [Meldrum et al. 2012]. Therefore, to ensure our game is intuitive, we will recruit representative users from our patient populations.

ACKNOWLEDGMENTS

This project was supported by National Science Foundation Partnerships For Innovation Technology Translation Award to Jürgen Konczak (1919036). Christopher Curry was supported by National Research Trainee-Understanding the Brain: Graduate Training Program in Sensory Science: Optimizing the Information Available for Mind and Brain (1734815).

REFERENCES

• Joshua E Aman, Naveen Elangovan, I Yeh, Jürgen Konczak, et al. 2015. The effectiveness of proprioceptive training for improving motor function: a systematic review. Frontiers in human neuroscience 8 (2015), 1075. 
  Navigate to
  citation 1
• Jeremy Gibson Bond. 2014. Introduction to Game Design, Prototyping, and Development: From Concept to Playable Game with Unity and C. Addison-Wesley Professional. 
  Navigate to
  citation 1
• Leonardo Cappello, Naveen Elangovan, Sara Contu, Sanaz Khosravani, Jürgen Konczak, and Lorenzo Masia. 2015. Robot-aided assessment of wrist proprioception. Frontiers in human neuroscience 9 (2015), 198. 
  Navigate to
  citation 1
• Fiona Coupar, Alex Pollock, Phil Rowe, Christopher Weir, and Peter Langhorne. 2012. Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clinical rehabilitation 26, 4 (2012), 291–313. 
  Navigate to
  citation 1
• Naveen Elangovan, Leonardo Cappello, Lorenzo Masia, Joshua Aman, and Jürgen Konczak. 2017. A robot-aided visuo-motor training that improves proprioception and spatial accuracy of untrained movement. Scientific reports 7, 1 (2017), 17054. 
  Navigate to
  citation 1
• Naveen Elangovan, Paul Tuite, and Jürgen Konczak. 2018. Somatosensory training improves proprioception and untrained motor function in Parkinson’s disease. Frontiers in neurology 9(2018), 1053. 
  Navigate to
  citation 1
• Naveen Elangovan, I-Ling Yeh, Jessica Holst-Wolf, and Jürgen Konczak. 2019. A robot-assisted sensorimotor training program can improve proprioception and motor function in stroke survivors. In 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, 660–664. 
  Navigate to
  citation 1
• I. Fette and A. Melnikov. 2011. The WebSocket Protocol. Technical Report. 
  Navigate to
  citation 1
• Tracy Fullerton. 2018. Game design workshop: a playcentric approach to creating innovative games. AK Peters/CRC Press. 
  Navigate to
  citation 1
• Hunter G Hoffman, Walter J Meyer III, Maribel Ramirez, Linda Roberts, Eric J Seibel, Barbara Atzori, Sam R Sharar, and David R Patterson. 2014. Feasibility of articulated arm mounted Oculus Rift Virtual Reality goggles for adjunctive pain control during occupational therapy in pediatric burn patients. Cyberpsychology, Behavior, and Social Networking 17, 6(2014), 397–401. 
  Navigate to
  citation 1
• Jürgen Konczak, Daniel M Corcos, Fay Horak, Howard Poizner, Mark Shapiro, Paul Tuite, Jens Volkmann, and Matthias Maschke. 2009. Proprioception and motor control in Parkinson’s disease. Journal of motor behavior 41, 6 (2009), 543–552. 
  Navigate to
  citation 1
• Gert Kwakkel, Boudewijn J Kollen, and Robert C Wagenaar. 1999. Therapy impact on functional recovery in stroke rehabilitation: a critical review of the literature. Physiotherapy 85, 7 (1999), 377–391. 
  Navigate to
  citation 1
• Kate E Laver, Belinda Lange, Stacey George, Judith E Deutsch, Gustavo Saposnik, and Maria Crotty. 2017. Virtual reality for stroke rehabilitation. Cochrane database of systematic reviews11 (2017). 
  Navigate to
  citation 1
• Dara Meldrum, Aine Glennon, Susan Herdman, Deirdre Murray, and Rory McConn-Walsh. 2012. Virtual reality rehabilitation of balance: assessment of the usability of the Nintendo Wii® Fit Plus. Disability and rehabilitation: assistive technology 7, 3 (2012), 205–210. 
  Navigate to
  citation 1
• JC Rothwell, MM Traub, BL Day, JA Obeso, PK Thomas, and CD Marsden. 1982. Manual motor performance in a deafferented man. Brain 105, 3 (1982), 515–542.

via Gamification in a Physical Rehabilitation Setting: Developing a Proprioceptive Training Exercise for a Wrist Robot

[Abstract] A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation

Abstract

Exoskeleton robots have been demonstrated to effectively assist the rehabilitation of patients with upper or lower limb disabilities. To make exoskeletons more accessible to patients, they need to be lightweight and compact without major performance tradeoffs. Existing upper-limb exoskeletons focus on assistance with coarse-motion of the upper arm while forearm fine-motion rehabilitation is often ignored. This paper presents an elbow-wrist exoskeleton with five degrees-of-freedom (DoFs). Using geared bearings, slider crank mechanisms, and a spherical mechanism for the wrist and elbow modules, this exoskeleton can provide 5-DoF rotary motion forearm assistance. The optimized exoskeleton dimensions allow sufficient rotation output while the motors are placed parallel to the forearm and elbow joint. Thus compactness and less inertia loading can be achieved. Linear and rotary series elastic actuators (SEAs) with high torque-to-weight ratios are proposed to accurately measure and control interaction force and impedance between exoskeleton and forearm. The resulting 3-kg exoskeleton can be used alone or easily in combination with other exoskeleton robots to provide various robot-aided upper limb rehabilitation.

via A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation – IEEE Journals & Magazine

[Abstract] Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication

Abstract

Spasticity and contractures are secondary to most neurological and orthopaedic pathologies. The most conservative method of management of spasticity and contractures is passive stretching exercises. Robotic rehabilitation aims to provide a solution to this problem. We describe in details the design of a powered wearable orthosis especially designed for managing spasticity and contractures. The device is fully portable, allowing the patient to undergo repeated-passive-dynamic exercises outside the hospital environment. The design of the device is modular to make it adaptable to different anatomies and pathologies. The device is also fitted with electrogoniometers and torque sensors to record kinematics and dynamics of the patient, improving the insight of the clinicians to the rehabilitation of the patient, as well as providing data for further clinical and scientific investigations. The mechanical integrity of the device elements is simulated and verified.

via Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication

[Abstract + References] A Feasibility Study on Wrist Rehabilitation Using the Leap Motion – Conference paper

Abstract

Wrist and hand rehabilitation are common as people suffer injuries during work and exercise. Typically, the rehabilitation involves the patient and the therapist, which is both time consuming and cost burdening. It is desirable to use advanced telemedicine technologies such that the patient is able to enjoy the freedom of performing the required exercise at their own time and pace, while the healthcare system can operate more efficiently. The Leap Motion Controller (LMC), an inexpensive motion detection device, seems to be a good candidate for remote wrist rehabilitation. In this paper, the functionality and capability of the LMC are examined. Experiments are carried out with a total of twelve people performing twelve different movements. From the experimental results, the feasibility of using the LMC as a rehabilitation device is discussed.

References

1. 1.
   Golomb, M.R., McDonald, B.C., Warden, S.J., Yonkman, J., Saykin, A.J., Shirley, B., Huber, M., Rabin, B., AbdelBaky, M., Nwosu, M.E., Barkat-Masih, M.: In-home virtual reality videogame telerehabilitation in adolescents with hemiplegic cerebral palsy. Arch. Phys. Med. Rehabil. 91(1), 1–8 (2010)CrossRefGoogle Scholar
2. 2.
   Zhang, L., Li, K.F., Lin, J., Ren, J.: Leap motion for telerehabilitation: a feasibility study. In: Advances on Broadband and Wireless Computing, Communication and Applications, 13th International Conference on BWCCA, pp. 213–223 (2018)Google Scholar
3. 3.
   https://en.wikipedia.org/wiki/Leap_Motion
4. 4.
   https://developer-archive.leapmotion.com/documentation/javascript/devguide/Leap_Overview.html?highlight=palmwidth
5. 5.
   Golgan, A.: Changing How People Look at Physical Therapy [Blog] Leap Motion (2018). http://blog.leapmotion.com/changing-people-look-physical-therapy/. Accessed 20 Aug 2018
6. 6.
   Sathiyanarayanan, M., Rajan, S.: Understanding the Use of Leap Motion Touchless Device in Physiotherapy and Improving the Healthcare System in India (2018). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7945443&tag=1. Accessed 20 Aug 2018
7. 7.
   https://myhealth.alberta.ca/Health/aftercareinformation/pages/conditions.aspx?hwid=bo1655
8. 8.
   https://developer-archive.leapmotion.com/documentation/python/index.html
9. 9.
   Salvador, S., Chan, P.: FastDTW: toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)CrossRefGoogle Scholar

via A Feasibility Study on Wrist Rehabilitation Using the Leap Motion | SpringerLink

[BLOG POST] 5 Smartphone Games That Encourage Wrist Rehabilitation

By Kristin Valdes, OTD, OT, CHT

Tired of using dumbbells for rehabilitation following distal radius fractures? Looking for new interventions to increase client engagement? Look no further than your patient’s smartphone! Incorporate it into exercise routines to help your patients regain wrist balance and to provide proprioceptive input.

Evidence Supports Proprioceptive Activities

Emerging evidence supports the use of proprioceptive activities for distal radius fracture rehabilitation.¹ A cross-sectional study involving females treated operatively and non-operatively for a distal radius fracture found that participants had significantly less joint position sense in comparison to study controls.² The proprioceptive limitations correlated highly with functional impairment on the Patient Rated Wrist Evaluation.³

By addressing proprioceptive deficits while encouraging functional wrist range of motion, smartphone applications complement a traditional hand therapy program for individuals requiring skilled therapy following a distal radius fracture.

Some games to consider:

• Chopper Lite – Action packed side-scrolling helicopter game where a tilt of the screen flies the chopper.
• Labyrinth – Classic labyrinth game in which you must guide a ball through a labyrinth by moving your device.
• Tilt Maze Lite – Maze game where a tilt of your device helps a marble through a maze toward the exit. Use different mazes to test wrist balance and timing. The game stores the player’s best time for each maze so patients can track their performance as their wrist heals.
• Water Slide Extreme – Unique water slide game featuring tight corners and huge loops that you must navigate by twisting or leaning your device.
• Snail Mail – Kart-style racing game in which the player controls a racing snail on a mission to collect packages and deliver them to the farthest reaches of the universe while dodging obstacles such as laser towers, slugs, asteroids, and salt.

The clinician should consider using smartphones as an intervention following distal radius fractures. Skilled hand therapists can assist with appropriate postural mechanics and provide guidelines for the amount of time a patient should devote to gaming.

Rehabilitation at Your Fingertips

Certain smartphone applications can be used to address client-specific deficits, decrease functional concerns, and achieve client-centered goals. Incorporating smartphone gaming in hand therapy may provide motivation and convenience to your clients.

 

Below, Kristin Valdes discusses wrist proprioception using smartphone apps in a short video from her course, Rehabilitation Following Wrist Fracture.

Watch First Chapter FREE

 

About Kristin Valdes, OTD, OT, CHT

Kristin Valdes, OTD, OT, CHT, is the owner and director of Hand Works Therapy in Sarasota, Florida. She is an associate professor at Gannon University and has been in private practice for over twenty years, specializing in the treatment and rehabilitation of the upper extremity. Her clinical expertise includes treatment of the hand, wrist, elbow, shoulder, splinting, and arthritis. Dr. Valdes has published articles in the Journal of Hand Therapy, Hand, Journal of Manipulative and Physiological Therapeutics, and the Journal of Physical Therapy Science. She serves as a member of the Board of the American Society of Hand Therapists and American Association of Hand Surgery.Dr. Valdes has published chapters on hand, elbow, and shoulder fractures and dislocations in Advanced Concepts of Hand Pathology and Surgery. She is an assistant editor of the Journal of Hand Therapy. Dr. Valdes received her OTD degree with a specialization in hand rehabilitation from Rocky Mountain University of Health Care Professions in Provo, Utah. Dr Valdes is member of the Guatemala Healing Hands Medical Mission.

 

References

1. Algar, L., & Valdes, K. (2014). Using smartphone applications as hand therapy interventions. Journal of Hand Therapy, 27(3), 254–257. doi:10.1016/j.jht.2013.12.009
2. Karagiannopoulos, C., Sitler, M., Michlovitz, S., & Tierney, R. (2014a). A Descriptive Study on Wrist and Hand Sensori-Motor Impairment and Function Following Distal Radius Fracture Intervention. Journal of Hand Therapy, 27(3), e2–e3. doi:10.1016/j.jht.2013.08.006
3. Karangiannopoulos, et al. (2014)

via 5 Smartphone Games That Encourage Wrist Rehabilitation | MedBridge Blog

[Abstract] Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings

Abstract

Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation of the wrist and fingers joint, an electromechanical exoskeleton was developed that simultaneously moves the wrist and metacarpophalangeal joints.

The device was designed for the ease of manufacturing and maintenance, with specific considerations for countries with limited resources. Active participation of the user is ensured by the implementation of electromyographic control and visual feedback of performance. Muscle activity requirements, movement parameters, range of motion, and speed of the device can all be customized to meet the needs of the user.

Twelve stroke survivors, ranging from the subacute to chronic phases of recovery (mean 10.6 months post-stroke) participated in a pilot study with the device. Participants completed 20 sessions, each lasting 45 minutes. Overall, subjects exhibited statistically significant changes (p < 0.05) in clinical outcome measures following the treatment, with the Fugl-Meyer Stroke Assessment score for the upper extremity increasing from 36 to 50 and the Barthel Index increasing from 74 to 89. Active range of wrist motion increased by 190 while spasticity decreased from 1.75 to 1.29 on the Modified Ashworth Scale.

Thus, this device shows promise for improving rehabilitation outcomes, especially for patients in countries with limited resources.

via Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings – IEEE Journals & Magazine

[Abstract] DESIGN AND DEVELOPMENT OF A NEW APPROACH TO WRIST REHABILITATION

Abstract

Wrist injuries are a very common type of pathology that can compromise most daily
tasks. Conventional therapy is dependent on the availability of physiotherapists as well as devices
designed for this purpose. Conventional devices do not accompany the patient throughout their
rehabilitation process, requiring their constant replacement. Vibratory therapies emerged in recent
years and have demonstrated several benefits in this area. However, there are few vibratory
devices designed for wrist rehabilitation. In this paper, we propose two different portable and
active models for wrist rehabilitation based on vibratory therapy for wrist rehabilitation. The first
model has a cylindrical shape and the second model has a dumbbell shape. The results obtained
showed that vibratory therapy can assist the wrist rehabilitation because it promoted
improvements in joint amplitude gain in all wrist movements. Furthermore, the second device
demonstrated higher joint gains than the first device. In addition, the results obtained from the
measurement of accelerations demonstrate that the natural frequencies of both devices are
adequate for wrist and forearm rehabilitation as well as the mode of vibration. There are
differences between what the simulations predicted and what was obtained in practice in terms of
natural frequency values.

Full Text PDF

[Abstract] Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger

Abstract

Purpose

The purpose of this paper is to introduce a new design for a finger and wrist rehabilitation robot. Furthermore, a fuzzy sliding mode controller has been designed to control the system.

Design/methodology/approach

Following an introduction regarding the hand rehabilitation, this paper discusses the conceptual and detailed design of a novel wrist and finger rehabilitation robot. The robot provides the possibility of rehabilitating each phalanx individually which is very important in the finger rehabilitation process. Moreover, due to the model uncertainties, disturbances and chattering in the system, a fuzzy sliding mode controller design method is proposed for the robot.

Findings

With the novel design for moving the DOFs of the system, the rehabilitation for the wrist and all phalanges of fingers is done with only two actuators which are combined in one device. These features make the system a good choice for home rehabilitation. To control the robot, a fuzzy sliding mode controller has been designed for the system. The fuzzy controller does not affect the coefficient of the sliding mode controller and uses the overall error of the system to make a control signal. Thus, the dependence of the controller to the model decreases and the system is more robust. The stability of the system is proved by the Lyapunov theorem.

Originality/value

The paper provides a novel design of a hand rehabilitation robot and a controller which is used to compensate the effects of the uncertain parameters and chattering phenomenon.

via Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger | Emerald Insight

[Abstract + References] Improving Motivation in Wrist Rehabilitation Therapies – Conference paper

Abstract

Rehabilitation encompasses a wide variety of activities aimed at reducing the impact of injuries and disabilities by applying different exercises. Frequently, such exercises are carried out at home as a repetition of the same movements or tasks to achieve both motor learning and the necessary cortical changes. Although this increases the patients’ available time for rehabilitation, it may also have some unpleasant side effects. That occurs because carrying out repetitive exercises in a more isolated environment may result in a boring activity that leads patients to give up their rehabilitation. Therefore, patients’ motivation should be considered an essential feature while designing rehabilitation exercises. In this paper, we present how we have faced this need by exploiting novel technology to guide patients in their rehabilitation process. It includes a game crafted to make recovery funny and useful, at the same time. The game and the use we made of the specific hardware follow the recommendations and good practices provided by medical experts.

References

1. 1.
   Aguiar, L.F., Bo, A.P.L.: Hand gestures recognition using electromyography for bilateral upper limb rehabilitation. In: 2017 IEEE Life Sciences Conference (LSC), pp. 63–66. IEEE (2017)Google Scholar
2. 2.
   Amirabdollahian, F., Walters, M.L.: Application of support vector machines in detecting hand grasp gestures using a commercially off the shelf wireless myoelectric armband. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 111–115 (2017)Google Scholar
3. 3.
   Batista, T.V.V., Machado, L.S., Valenca, A.M.G.: Surface electromyography for game-based hand motor rehabilitation. In: 2016 XVIII Symposium on Virtual and Augmented Reality (SVR), pp. 140–144. IEEE (2016)Google Scholar
4. 4.
   Bevilacqua, V., Brunetti, A., Trigiante, G., Trotta, G.F., Fiorentino, M., Manghisi, V., Uva, A.E.: Design and Development of a Forearm Rehabilitation System Based on an Augmented Reality Serious Game. Presented at the (2016)Google Scholar
5. 5.
   Bütefisch, C., Hummelsheim, H., Denzler, P., Mauritz, K.H.: Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J. Neurol. Sci. 130(1), 59–68 (1995)CrossRefGoogle Scholar
6. 6.
   Charles, S.K., Krebs, H.I., Volpe, B.T., Lynch, D., Hogan, N.: Wrist rehabilitation following stroke: initial clinical results. In: Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, pp. 13–16. IEEE (2005)Google Scholar
7. 7.
   Cialdini, R.B.: Influence: The Psychology of Persuation. Morrow, New York (1993)Google Scholar
8. 8.
   Cram, J.R., Steger, J.C.: EMG scanning in the diagnosis of chronic pain. Biofeedback Self Regul. 8(2), 229–241 (1983)CrossRefGoogle Scholar
9. 9.
   Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D.: Gamification using game-design elements in non-gaming contexts. In: 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA 2011), pp. 24–25. ACM Press, Vancouver (2011)Google Scholar
10. 10.
    Dromerick, A.W., Edwards, D.F., Hahn, M.: Does the application of constraint-induced movement therapy during acute rehabilitation reduce arm impairment after ischemic stroke? Stroke 31(12), 2984–2988 (2000)CrossRefGoogle Scholar
11. 11.
    Esfahlani, S.S., Thompson, T., Parsa, A.D., Brown, I., Cirstea, S.: ReHabgame: a non-immersive virtual reality rehabilitation system with applications in neuroscience. Heliyon 4(2), e00526 (2018)CrossRefGoogle Scholar
12. 12.
    He, S., Yang, C., Wang, M., Cheng, L., Hu, Z.: Hand gesture recognition using MYO armband. Chinese Automation Congress (CAC), 2017, pp. 4850–4855 (2017)Google Scholar
13. 13.
    Holden, M.K.: Virtual environments for motor rehabilitation: review. CyberPsychology Behav. 8(3), 187–211 (2005)CrossRefGoogle Scholar
14. 14.
    Horger, M.M.: The reliability of goniometric measurements of active and passive wrist motions. Am. J. Occup. Ther. 44(4), 342–348 (1990)CrossRefGoogle Scholar
15. 15.
    Kingston, B.: Understanding Joints: A Practical Guide to Their Structure and Function. Nelson Thornes (2000)Google Scholar
16. 16.
    Langan, J., Subryan, H., Nwogu, I., Cavuoto, L.: Reported use of technology in stroke rehabilitation by physical and occupational therapists. Disabil. Rehabil. Assist. Technol. 13(7), 1–7 (2017)Google Scholar
17. 17.
    Leap Motion Inc: Leap Motion. https://www.leapmotion.com/
18. 18.
    Van der Lee, J.H., Wagenaar, R.C., Lankhorst, G.J., Vogelaar, T.W., Devillé, W.L., Bouter, L.M.: Forced use of the upper extremity in chronic stroke patients: results from a single-blind randomized clinical trial. Stroke 30(11), 2369–2375 (1999)CrossRefGoogle Scholar
19. 19.
    López-Jaquero, V., Montero, F., Teruel, M.A.: Influence awareness: considering motivation in computer-assisted rehabilitation. J. Ambient Intell. Humaniz. Comput. 10(6), 2018–2197 (2017)Google Scholar
20. 20.
    Mendez, I., Hansen, B.W., Grabow, C.M., Smedegaard, E.J.L., Skogberg, N.B., Uth, X.J., Bruhn, A., Geng, B., Kamavuako, E.N.: Evaluation of the Myo armband for the classification of hand motions. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 1211–1214 (2017)Google Scholar
21. 21.
    World Health Organization: International Classification of Functioning, Disability and Health: ICF. World Health Organization (2001)Google Scholar
22. 22.
    Ortiz-Catalan, M., Nijenhuis, S., Ambrosch, K., Bovend’Eerdt, T., Koenig, S., Lange, B.: Virtual reality. In: Emerging Therapies in Neurorehabilitation, pp. 249–265. Springer (2014)Google Scholar
23. 23.
    Rechy-Ramirez, E.J., Marin-Hernandez, A., Rios-Figueroa, H.V.: A human-computer interface for wrist rehabilitation: a pilot study using commercial sensors to detect wrist movements. Vis. Comput., 1–15 (2017)Google Scholar
24. 24.
    Sathiyanarayanan, M., Rajan, S.: MYO Armband for physiotherapy healthcare: A case study using gesture recognition application. In: 2016 8th International Conference on Communication Systems and Networks (COMSNETS), pp. 1–6 (2016)Google Scholar
25. 25.
    Skirven, T.M., Osterman, A.L., Fedorczyk, J.M., Amadio, P.C.: Rehabilitation of the Hand and Upper Extremity. Mosby (2011)Google Scholar
26. 26.
    Slutsky, D.J., Herman, M.: Rehabilitation of distal radius fractures: a biomechanical guide. Hand Clin. 21(3), 455–468 (2005)CrossRefGoogle Scholar
27. 27.
    Tabor, A., Bateman, S., Scheme, E., Flatla, D.R., Gerling, K.: Designing game-based myoelectric prosthesis training. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems – CHI 2017, pp. 1352–1363. ACM Press, New York (2017)Google Scholar
28. 28.
    Teruel, M.A., Navarro, E., González, P., López-Jaquero, V., Montero, F.: Applying thematic analysis to define an awareness interpretation for collaborative computer games. Inf. Softw. Technol. 74, 17–44 (2016)CrossRefGoogle Scholar
29. 29.
    Thalmic Labs Inc.: Myo Gesture Control ArmbandGoogle Scholar
30. 30.
    Vines, A.: Helping your wrist to recover after a fracture. Oxford University Hospitals NHS Trust (2015)Google Scholar
31. 31.
    Wolf, S.L., Winstein, C.J., Miller, J.P., Taub, E., Uswatte, G., Morris, D., Giuliani, C., Light, K.E., Nichols-Larsen, D.: EXCITE investigators, for the: effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. J. Am. Med. Assoc. 296(17), 2095–2104 (2006)CrossRefGoogle Scholar
32. 32.
    Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Signal Process. Control 3(1), 1–18 (2008)CrossRefGoogle Scholar

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