Health-Enabling Technologies to Assist Patients With Musculoskeletal Shoulder Disorders When Exercising at Home: Scoping Review

Lena Elgert, Bianca Steiner, Birgit Saalfeld, Michael Marschollek, Klaus-Hendrik Wolf, Lena Elgert, Bianca Steiner, Birgit Saalfeld, Michael Marschollek, Klaus-Hendrik Wolf

Abstract

Background: Health-enabling technologies (HETs) are information and communication technologies that promote individual health and well-being. An important application of HETs is telerehabilitation for patients with musculoskeletal shoulder disorders. Currently, there is no overview of HETs that assist patients with musculoskeletal shoulder disorders when exercising at home.

Objective: This scoping review provides a broad overview of HETs that assist patients with musculoskeletal shoulder disorders when exercising at home. It focuses on concepts and components of HETs, exercise program strategies, development phases, and reported outcomes.

Methods: The search strategy used Medical Subject Headings and text words related to the terms upper extremity, exercises, and information and communication technologies. The MEDLINE, Embase, IEEE Xplore, CINAHL, PEDro, and Scopus databases were searched. Two reviewers independently screened titles and abstracts and then full texts against predefined inclusion and exclusion criteria. A systematic narrative synthesis was performed. Overall, 8988 records published between 1997 and 2019 were screened. Finally, 70 articles introducing 56 HETs were included.

Results: Identified HETs range from simple videoconferencing systems to mobile apps with video instructions to complex sensor-based technologies. Various software, sensor hardware, and hardware for output are in use. The most common hardware for output are PC displays (in 34 HETs). Microsoft Kinect cameras in connection with related software are frequently used as sensor hardware (in 27 HETs). The identified HETs provide direct or indirect instruction, monitoring, correction, assessment, information, or a reminder to exercise. Common parameters for exercise instructions are a patient's range of motion (in 43 HETs), starting and final position (in 32 HETs), and exercise intensity (in 20 HETs). In total, 48 HETs provide visual instructions for the exercises; 29 HETs report on telerehabilitation aspects; 34 HETs only report on prototypes; and 15 HETs are evaluated for technical feasibility, acceptance, or usability, using different assessment instruments. Efficacy or effectiveness is demonstrated for only 8 HETs. In total, 18 articles report on patients' evaluations. An interdisciplinary contribution to the development of technologies is found in 17 HETs.

Conclusions: There are various HETs, ranging from simple videoconferencing systems to complex sensor-based technologies for telerehabilitation, that assist patients with musculoskeletal shoulder disorders when exercising at home. Most HETs are not ready for practical use. Comparability is complicated by varying prototype status, different measurement instruments, missing telerehabilitation aspects, and few efficacy studies. Consequently, choosing an HET for daily use is difficult for health care professionals and decision makers. Prototype testing, usability, and acceptance tests with the later target group under real-life conditions as well as efficacy or effectiveness studies with patient-relevant core outcomes for every promising HET are required. Furthermore, health care professionals and patients should be more involved in the product design cycle to consider relevant practical aspects.

Keywords: assistive technologies; exercises; mobile phone; musculoskeletal diseases; physical therapy; shoulder; technology-assisted therapy; telerehabilitation; upper extremity.

Conflict of interest statement

Conflicts of Interest: None declared.

©Lena Elgert, Bianca Steiner, Birgit Saalfeld, Michael Marschollek, Klaus-Hendrik Wolf. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 04.02.2021.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) flow diagram.
Figure 2
Figure 2
Overview of current and completed system and project phases per year.

References

    1. Haux R, Koch S, Lovell NH, Marschollek M, Nakashima N, Wolf KH. Health-enabling and ambient assistive technologies: past, present, future. Yearb Med Inform. 2016 Jun 30;Suppl 1:S76–91. doi: 10.15265/IYS-2016-s008.
    1. Kohlmann M, Gietzelt M, Haux R, Song B, Wolf KH, Marschollek M. A methodological framework for the analysis of highly intensive, multimodal and heterogeneous data in the context of health-enabling technologies and ambient-assisted living. Inform Health Soc Care. 2014;39(3-4):294–304. doi: 10.3109/17538157.2014.931847.
    1. Winters JM. Telerehabilitation research: emerging opportunities. Annu Rev Biomed Eng. 2002;4:287–320. doi: 10.1146/annurev.bioeng.4.112801.121923.
    1. Institute of Medicine . Telemedicine: A Guide to Assessing Telecommunications for Health Care. Washington, DC: The National Academies Press; 1996.
    1. Peretti A, Amenta F, Tayebati SK, Nittari G, Mahdi SS. Telerehabilitation: review of the state-of-the-art and areas of application. JMIR Rehabil Assist Technol. 2017 Jul 21;4(2):e7. doi: 10.2196/rehab.7511.
    1. Turolla A, Rossettini G, Viceconti A, Palese A, Geri T. Musculoskeletal physical therapy during the COVID-19 pandemic: is telerehabilitation the answer? Phys Ther. 2020 Aug 12;100(8):1260–1264. doi: 10.1093/ptj/pzaa093.
    1. Haux R, Howe J, Marschollek M, Plischke M, Wolf KH. Health-enabling technologies for pervasive health care: on services and ICT architecture paradigms. Inform Health Soc Care. 2008 Jun;33(2):77–89. doi: 10.1080/17538150802127140.
    1. Virta L, Joranger P, Brox JI, Eriksson R. Costs of shoulder pain and resource use in primary health care: a cost-of-illness study in Sweden. BMC Musculoskelet Disord. 2012 Feb 10;13:17. doi: 10.1186/1471-2474-13-17.
    1. Huisstede BM, Bierma-Zeinstra SM, Koes BW, Verhaar JA. Incidence and prevalence of upper-extremity musculoskeletal disorders. A systematic appraisal of the literature. BMC Musculoskelet Disord. 2006 Jan 31;7:7. doi: 10.1186/1471-2474-7-7.
    1. CONCEPT PAPER: WHO Guidelines on Health-Related Rehabilitation (Rehabilitation Guidelines) World Health Organization. [2020-06-04]. .
    1. Laver KE, Adey-Wakeling Z, Crotty M, Lannin NA, George S, Sherrington C. Telerehabilitation services for stroke. Cochrane Database Syst Rev. 2020 Jan 31;1:CD010255. doi: 10.1002/14651858.CD010255.pub3.
    1. Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EE, Meskers CG, Kwakkel G. Effects of Robot-Assisted Therapy for the Upper Limb After Stroke. Neurorehabil Neural Repair. 2017 Feb;31(2):107–121. doi: 10.1177/1545968316666957.
    1. Chen Y, Abel KT, Janecek JT, Chen Y, Zheng K, Cramer SC. Home-based technologies for stroke rehabilitation: a systematic review. Int J Med Inform. 2019 Mar;123:11–22. doi: 10.1016/j.ijmedinf.2018.12.001.
    1. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L, Hempel S, Akl EA, Chang C, McGowan J, Stewart L, Hartling L, Aldcroft A, Wilson MG, Garritty C, Lewin S, Godfrey CM, Macdonald MT, Langlois EV, Soares-Weiser K, Moriarty J, Clifford T, Tunçalp Ö, Straus SE. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018 Oct 02;169(7):467–473. doi: 10.7326/M18-0850.
    1. Hoogendam A, de Vries Robbé PF, Overbeke AJ. Comparing patient characteristics, type of intervention, control, and outcome (PICO) queries with unguided searching: a randomized controlled crossover trial. J Med Libr Assoc. 2012 Apr;100(2):121–6. doi: 10.3163/1536-5050.100.2.010.
    1. Schreier M. Qualitative content analysis in practice. London: SAGE; 2012.
    1. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.
    1. Antón D, Goñi A, Illarramendi A. Exercise recognition for Kinect-based telerehabilitation. Methods Inf Med. 2015;54(2):145–55. doi: 10.3414/ME13-01-0109.
    1. Anton D, Berges I, Bermúdez J, Goñi A, Illarramendi A. A telerehabilitation system for the selection, evaluation and remote management of therapies. Sensors (Basel) 2018 May 08;18(5) doi: 10.3390/s18051459.
    1. Macías-Hernández SI, Vásquez-Sotelo DS, Ferruzca-Navarro MV, Badillo SSH, Gutiérrez-Martínez J, Núñez-Gaona MA, Meneses HA, Velez-Gutiérrez OB, Tapia-Ferrusco I, Soria-Bastida ML, Coronado-Zarco R, Morones-Alba JD. Proposal and evaluation of a telerehabilitation platform designed for patients with partial rotator cuff tears: a preliminary study. Ann Rehabil Med. 2016 Aug;40(4):710–7. doi: 10.5535/arm.2016.40.4.710.
    1. Ongvisatepaiboon K, Chan J, Vanijja V. Smartphone-Based Tele-Rehabilitation System for Frozen Shoulder Using a Machine Learning Approach. IEEE Symposium Series on Computational Intelligence; 2015 Dec 7-10; Cape Town, South Africa. 2015. pp. 811–815.
    1. Ongvisatepaiboon K, Vanijja V, Chan J. Smartphone-based tele-rehabilitation framework for patient with frozen shoulder. Frontiers in Artificial Intelligence and Applications. 2015;275:158–169. doi: 10.3233/978-1-61499-503-6-158.
    1. Ongvisatepaiboon K, Vanijja V, Chignell M, Mekhora K, Chan JH. Smartphone-based audio-biofeedback system for shoulder joint tele-rehabilitation. J Med Imaging Hlth Inform. 2016 Aug 01;6(4):1127–1134. doi: 10.1166/jmihi.2016.1810.
    1. Pastora-Bernal JM, Martín-Valero R, Barón-López FJ, Moyano NG, Estebanez-Pérez MJ. Telerehabilitation after arthroscopic subacromial decompression is effective and not inferior to standard practice: Preliminary results. J Telemed Telecare. 2018 Jul;24(6):428–433. doi: 10.1177/1357633X17706583.
    1. Pastora-Bernal JM, Martín-Valero R, Barón-López FJ. Cost analysis of telerehabilitation after arthroscopic subacromial decompression. J Telemed Telecare. 2018 Sep;24(8):553–559. doi: 10.1177/1357633X17723367.
    1. Cabana F, Pagé C, Svotelis A, Langlois-Michaud S, Tousignant M. Is an in-home telerehabilitation program for people with proximal humerus fracture as effective as a conventional face-to face rehabilitation program? A study protocol for a noninferiority randomized clinical trial. BMC Sports Sci Med Rehabil. 2016;8(1):27. doi: 10.1186/s13102-016-0051-z.
    1. Tousignant M, Giguère A, Morin M, Pelletier J, Sheehy A, Cabana F. In-home telerehabilitation for proximal humerus fractures: a pilot study. Int J Telerehabil. 2014;6(2):31–7. doi: 10.5195/ijt.2014.6158.
    1. Eriksson L, Lindström B, Gard G, Lysholm J. Physiotherapy at a distance: a controlled study of rehabilitation at home after a shoulder joint operation. J Telemed Telecare. 2009 Jul 09;15(5):215–220. doi: 10.1258/jtt.2009.081003.
    1. Eriksson L, Lindström B, Ekenberg L. Patients' experiences of telerehabilitation at home after shoulder joint replacement. J Telemed Telecare. 2011;17(1):25–30. doi: 10.1258/jtt.2010.100317.
    1. Budziszewski P. A low cost virtual reality system for rehabilitation of upper limb. VAMR 2013: Virtual, Augmented and Mixed Reality. Systems and Applications; VAMR 2013; 2013 July 21-26; Las Vegas. Berlin, Heidelberg: Springer; 2013. pp. 32–39.
    1. Carbonaro N, Lucchesi I, Lorusssi F, Tognetti A. Tele-monitoring and tele-rehabilitation of the shoulder muscular-skeletal diseases through wearable systems. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:4410–4413. doi: 10.1109/EMBC.2018.8513371.
    1. Lucchesi I, Lorussi F, Bellizzi M, Carbonaro N, Casarosa S, Trotta L, Tognetti A. Daily life self-management and self-treatment of musculoskeletal disorders through SHOULPHY. MobiHealth 2017: Wireless Mobile Communication and Healthcare. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 247; MobiHealth 2017; 2017 November 14-15; Vienna. Cham: Springer; 2018. pp. 233–241.
    1. Chang C, Chang Y, Hsiao B. The design of a shoulder rehabilitation game system. IET International Conference on Frontier Computing. Theory, Technologies and Applications; 2010 Aug 4-6; Taichung. London: 2010.
    1. Chang C, Chang Y, Chang H, Chou L. An interactive game-based shoulder wheel system for rehabilitation. Patient Prefer Adherence. 2012;6:821–8. doi: 10.2147/PPA.S37190. doi: 10.2147/PPA.S37190.
    1. Chiensriwimol N, H. Chan J, Mongkolnam P, Mekhora K. Monitoring frozen shoulder exercises to support clinical decision on treatment process using smartphone. Procedia Computer Science. 2017;111:129–136. doi: 10.1016/j.procs.2017.06.019.
    1. Chiensriwimol N, Mongkolnam P, Chan J. Frozen shoulder rehabilitationxercise simulation and usability study. Proceedings of the Ninth International Symposium on Information and Communication Technology - SoICT; SoICT 2018: The Ninth International Symposium on Information and Communication Technology; 2018 Dec; Danang City Viet Nam. 2018. pp. 257–264.
    1. Chung C, Chen C. The app game interface design for frozen shoulder rehabilitation. In: Soares M, Falcão C, Ahram T, editors. Advances in Ergonomics Modeling, Usability & Special Populations. Cham: Springer; 2017. pp. 507–516.
    1. Pinto J, Carvalho H, Chambel G, Ramiro J, Goncalves A. Adaptive gameplay and difficulty adjustment in a gamified upper-limb rehabilitation. 6th International Conference on Serious Games and Applications for Health; 2018 May 16-18; Vienna. 2018. pp. 2573–3060.
    1. Postolache O, Cary F, Girão P, Duarte N. Physiotherapy assessment based on Kinect and mobile APPs. 6th International Conference on Information, Intelligence, Systems and Applications (IISA); 2015 July 6-8; Corfu, Greece. 2015. pp. 1–6.
    1. Postolache O, Teixeira L, Cordeiro J, Lima L, Arriaga P, Rodrigues M, Girão P. Tailored Virtual Reality for Smart Physiotherapy. 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE); 2019 March 28-30; Bucharest, Romania. 2019. pp. 1–6.
    1. Rahman M, Wadhwa B, Kankanhalli A, Hua Y, Kei C, Hoon L, Jayakkumar S, Lin C. GEAR analytics: A clinician dashboard for a mobile game assisted rehabilitation system. 4th International Conference on User Science and Engineering (i-USEr); 2016 Aug 23-25; Maleka. 2016.
    1. Rahman M, Kankanhalli A, Wadhwa B, Hua Y, Kei C, Hoon L, Jayakkumar S, Lin C. GEAR: A Mobile Game-Assisted Rehabilitation System. 2016 IEEE International Conference on Healthcare Informatics; 2016 Oct 4-7; Chicago. 2016. pp. 4–7.
    1. Symeonidis I, Kavallieratou E. Development and assessment of a physiotherapy system based on serious games. Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing; MEDICON 2016; 2016 March 31st-April 2nd; Paphos. 2016. pp. 592–559.
    1. Viegas V, Postolache O, Pereira J, Girão P. NUI therapeutic serious games with metrics validation based on wearable devices. 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings; IEEE International Instrumentation and Measurement Technology Conference; 2016 May 23-26; Taipei, Taiwan. 2016. pp. 1–6.
    1. Yeh SC, Lee SH, Fank YJ, Gong YH, Lin JH, Hsieh YC. A cloud-based tele-rehabilitation system for frozen shoulder. AMR. 2013 Jul;717:766–771. doi: 10.4028/.
    1. Ying W, Aimin W. Augmented reality based upper limb rehabilitation system. 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI); 2017 Oct 20-22; Yangzhou. 2017. pp. 426–430.
    1. Huang M, Lee S, Yeh S, Chan R, Rizzo A, Xu W, Han-Lin W, Shan-Hui L. Intelligent frozen shoulder rehabilitation. IEEE Intell Syst. 2014 May;29(3):22–28. doi: 10.1109/mis.2014.35.
    1. Mangal N, Pal S, Khosla A. Frozen Shoulder Rehabilitation Using Microsoft Kinect. International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT); 2017 March 16-18; Coimbatore. 2017. pp. 1–6.
    1. Yeh S, Lee S, Fan Y. The development of interactive shoulder joint rehabilitation system using virtual reality in association with motion-sensing technology. In: Huang YM, Chao HC, Deng DJ, Park J, editors. Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Dordrecht: Springer; 2014. pp. 1073–1082.
    1. McGirr K, Harring SI, Kennedy TSR, Pedersen MFS, Hirata RP, Thorborg K, Bandholm T, Rathleff MS. An elastic exercise band mounted with a bandcizer™ can differentiate between commonly prescribed home exercises for the shoulder. Int J Sports Phys Ther. 2015 Jun;10(3):332–40.
    1. Da Cunha Neto JS, Filho PP, Da Silva GP, Da Cunha Olegario NB, Duarte JB, De Albuquerque VH. Dynamic evaluation and treatment of the movement amplitude using kinect sensor. IEEE Access. 2018;6:17292–17305. doi: 10.1109/access.2018.2811720.
    1. Uttarwar P, Mishra D. Development of a kinect-based physical rehabilitation system. Third International Conference on Image Information Processing (ICIIP); 2015; Waknaghat. 2015. pp. 387–392.
    1. Shieh C, Kao C, Weng S, Lin Y, Horng M. An intelligent flexbar for upper-limb rehabilitation based on wireless sensor network. Proceedings of the 2nd International Conference on Medical and Health Informatics; 2nd International Conference on Medical and Health Informatics - ICMHI '18; 2018 June; Tsukuba Japan. New York, NY, United States: Association for Computing Machinery; 2018. pp. 160–164.
    1. Choi Y, Nam J, Yang D, Jung W, Lee H, Kim SH. Effect of smartphone application-supported self-rehabilitation for frozen shoulder: a prospective randomized control study. Clin Rehabil. 2019 Apr;33(4):653–660. doi: 10.1177/0269215518818866.
    1. Cubukcu B, Yuzgec U. A physiotherapy application with MS kinect for patients with shoulder joint, muscle and tendon damage. Proceedings - 9th International Conference on Computational Intelligence and Communication Networks; 9th International Conference on Computational Intelligence and Communication Networks (CICN); 2017 Sept. 16-17; Girne. IEEE; 2017. pp. 225–228.
    1. Dahl-Popolizio S, Loman J, Cordes CC. Comparing outcomes of kinect videogame-based occupational/physical therapy versus usual care. Games Health J. 2014 Jun;3(3):157–61. doi: 10.1089/g4h.2014.0002.
    1. Du J, Wang Q, Baets L, Markopoulos P. Supporting shoulder pain prevention and treatment with wearable technology. Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare; PervasiveHealth '17; 2017 May; Barcelona, Spain. New York, NY, United States: Association for Computing Machinery; 2017. pp. 235–243.
    1. Stütz T, Emsenhuber G, Huber D, Domhardt M, Tiefengrabner M, Oostingh GJ, Fötschl U, Matis N, Ginzinger S. Mobile phone-supported physiotherapy for frozen shoulder: feasibility assessment based on a usability study. JMIR Rehabil Assist Technol. 2017 Jul 20;4(2):e6. doi: 10.2196/rehab.7085.
    1. Wang Q, De Baets L, Timmermans A, Chen W, Giacolini L, Matheve T, Markopoulos P. Motor control training for the shoulder with smart garments. Sensors (Basel) 2017 Jul 22;17(7) doi: 10.3390/s17071687.
    1. Quevedo W, Ortiz J, Velasco P, Sánchez J, Álvarez VM, Rivas D, Andaluz V. Assistance system for rehabilitation and valuation of motor skills. In: De Paolis L, Bourdot P, Mongelli A, editors. Augmented Reality, Virtual Reality, and Computer Graphics. Cham: Springer; 2017. pp. 166–174.
    1. Chen C. Multimedia virtualized environment for shoulder pain rehabilitation. J Phys Ther Sci. 2016 Apr;28(4):1349–54. doi: 10.1589/jpts.28.1349.
    1. Arif A, Maulidevi N, Dharma D, Alimansyah M, Prabowo T. An Interactive Kinect-Based Game Development for Shoulder Injury Rehabilitation. 5th International Conference on Data and Software Engineering; 2018 Nov 7-8; Senggigi Beach. 2018.
    1. Da Gama A, Chaves T, Figueiredo L, Teichrieb V. Guidance and Movement Correction Based on Therapeutics Movements for Motor Rehabilitation Support Systems. 14th Symposium on Virtual and Augmented Reality; 2012 May 28-31; Rio Janiero. 2012.
    1. Da Gama A, Chaves T, Figueiredo L, Teichrieb V. Improving motor rehabilitation process through a natural interaction based system using Kinect sensor. IEEE Symposium on 3D User Interfaces (3DUI); 2012 March 4-5; Costa Mesa, CA. 2012. pp. 145–146.
    1. Da Gama AEF, Chaves TM, Figueiredo LS, Baltar A, Meng M, Navab N, Teichrieb V, Fallavollita P. MirrARbilitation: A clinically-related gesture recognition interactive tool for an AR rehabilitation system. Comput Methods Programs Biomed. 2016 Oct;135:105–14. doi: 10.1016/j.cmpb.2016.07.014.
    1. Du Y, Shih C, Fan S, Lin H, Chen P. An IMU-compensated skeletal tracking system using Kinect for the upper limb. Microsyst Technol. 2018 Feb 13;24(10):4317–4327. doi: 10.1007/s00542-018-3769-6.
    1. Fernandez-Cervantes V, Stroulia E, Castillo C, Oliva L, Gonzalez F. Serious rehabilitation games with Kinect. IEEE Games Entertainment Media Conference (GEM); 2015 Oct 14-16; Toronto. 2015.
    1. Fikar P, Schoenauer C, Kaufmann H. The Sorcerer's Apprentice A serious game aiding rehabilitation in the context of Subacromial Impingement Syndrome. 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops; 2013 May 5-8; Venice. 2013.
    1. Muñoz G, Casero J, Cárdenas R. Usability study of a kinect-based rehabilitation tool for the upper limbs. In: Rocha Á, Adeli H, Reis L, Costanzo S, editors. New Knowledge in Information Systems and Technologies. Cham: Springer; 2019. pp. 755–763.
    1. Nava W, Mejia C, Uribe-Quevedo A. Prototype of a shoulder and elbow occupational health care exergame. In: Stephanidis C, editor. HCI International 2015 - Posters’ Extended Abstracts. Cham: Springer; 2015. pp. 467–472.
    1. Pekyavas NO, Ergun N. Comparison of virtual reality exergaming and home exercise programs in patients with subacromial impingement syndrome and scapular dyskinesis: Short term effect. Acta Orthop Traumatol Turc. 2017 May;51(3):238–242. doi: 10.1016/j.aott.2017.03.008. doi: 10.1016/j.aott.2017.03.008.
    1. Powell V, Powell W. Therapy-led design of home-based virtual rehabilitation. IEEE 1st Workshop on Everyday Virtual Reality (WEVR); 2015 Mar 23; Arles. 2015.
    1. Rizzo J, Thai P, Li EJ, Tung T, Hudson TE, Herrera J, Raghavan P. Structured Wii protocol for rehabilitation of shoulder impingement syndrome: A pilot study. Ann Phys Rehabil Med. 2017 Nov;60(6):363–370. doi: 10.1016/j.rehab.2016.10.004.
    1. Shi Y, Peng Q. A VR-based user interface for the upper limb rehabilitation. Procedia CIRP. 2018;78:115–120. doi: 10.1016/j.procir.2018.08.311.
    1. Wiederhold B, Wiederhold M. Evaluation of virtual reality therapy in augmenting the physical and cognitive rehabilitation of war veterans. Int J Disabil Hum Dev. 2006;5(3):211–216. doi: 10.1515/ijdhd.2006.5.3.211.
    1. Yin Z, Xu H. A wearable rehabilitation game controller using IMU sensor. IEEE International Conference on Applied System Invention (ICASI); 2018 Apr 13-17; Chiba. 2018. pp. 1060–1062.
    1. Arman N, Tarakci E, Tarakci D, Kasapcopur O. Effects of video games–based task-oriented activity training (Xbox 360 Kinect) on activity performance and participation in patients with juvenile idiopathic arthritis. Am J Phys Med Rehabil. 2019;98(3):174–181. doi: 10.1097/phm.0000000000001001.
    1. Chen P, Du Y, Shih C, Yang L, Lin H, Fan S. Development of an upper limb rehabilitation system using inertial movement units and kinect device. International Conference on Advanced Materials for Science and Engineering (ICAMSE); 2016; Tainan. 2016. pp. 275–278.
    1. Gorsič M, Novak D. Design and pilot evaluation of competitive and cooperative exercise games for arm rehabilitation at home. Conf Proc IEEE Eng Med Biol Soc. 2016 Dec;2016:4690–4694. doi: 10.1109/EMBC.2016.7591774.
    1. Goršič M, Cikajlo I, Novak D. Competitive and cooperative arm rehabilitation games played by a patient and unimpaired person: effects on motivation and exercise intensity. J Neuroeng Rehabil. 2017 Mar 23;14(1):23. doi: 10.1186/s12984-017-0231-4.
    1. Gutiérrez C, Papamija F, Rojas L, Medina R. Physical Rehabilitation of Upper Limb in Children and Young People Through Ludic Technology. IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES); 2018; Sarawak, Malaysia. 2018. pp. 588–593.
    1. Kanbe A, Ishihara S, Nagamachi M. Development and evaluation of ankle mobility VR rehabilitation game. In: Chung W, Shin C, editors. Advances in Affective and Pleasurable Design. AHFE 2017. Advances in Intelligent Systems and Computing, vol 585. Cham: Springer; 2018.
    1. Sveistrup H, McComas J, Thornton M, Marshall S, Finestone H, McCormick A, Babulic K, Mayhew A. Experimental studies of virtual reality-delivered compared to conventional exercise programs for rehabilitation. Cyberpsychol Behav. 2003 Jun;6(3):245–9. doi: 10.1089/109493103322011524.
    1. Ar I, Akgul YS. A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera. IEEE Trans Neural Syst Rehabil Eng. 2014 Nov;22(6):1160–1171. doi: 10.1109/tnsre.2014.2326254.
    1. Chen Y, Liu C, Yu C, Lee P, Kuo Y. An upper extremity rehabilitation system using efficient vision-based action identification techniques. Applied Sciences. 2018 Jul 17;8(7):1161. doi: 10.3390/app8071161.
    1. Chiang S, Kan Y, Chen Y, Tu Y, Lin H. Fuzzy computing model of activity recognition on WSN movement data for ubiquitous healthcare measurement. Sensors (Basel) 2016 Dec 03;16(12) doi: 10.3390/s16122053.
    1. Tekriwal R, Pandian BJ. ANN based assistance for exercise patterns using accelerometer data. Energy Procedia. 2017 Jun;117:424–431. doi: 10.1016/j.egypro.2017.05.163.
    1. Milgram P, Takemura H, Utsumi A, Kishino F. Augmented reality: a class of displays on the reality-virtuality continuum. Proc SPIE 2351, Telemanipulator and Telepresence Technologies. 1995 Dec 21; doi: 10.1117/12.197321.
    1. Jones MA. Clinical reasoning in manual therapy. Phys Ther. 1992 Dec;72(12):875–84. doi: 10.1093/ptj/72.12.875.
    1. Wulf G, Shea C, Lewthwaite R. Motor skill learning and performance: a review of influential factors. Med Educ. 2010 Jan;44(1):75–84. doi: 10.1111/j.1365-2923.2009.03421.x.
    1. Skjaerven LH, Kristoffersen K, Gard G. An eye for movement quality: a phenomenological study of movement quality reflecting a group of physiotherapists' understanding of the phenomenon. Physiother Theory Pract. 2008;24(1):13–27. doi: 10.1080/01460860701378042.
    1. Kleynen M, Braun SM, Bleijlevens MH, Lexis MA, Rasquin SM, Halfens J, Wilson MR, Beurskens AJ, Masters RS. Using a Delphi technique to seek consensus regarding definitions, descriptions and classification of terms related to implicit and explicit forms of motor learning. PLoS One. 2014;9(6):e100227. doi: 10.1371/journal.pone.0100227.
    1. Mortazavi F, Nadian-Ghomsheh A. Stability of Kinect for range of motion analysis in static stretching exercises. PLoS One. 2018;13(7):e0200992. doi: 10.1371/journal.pone.0200992.
    1. Wang Q, Kurillo G, Ofli F, Bajcsy R. Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect. International Conference on Healthcare Informatics; 2015 Oct. 21-23; Dallas, TX. IEEE; 2015. pp. 380–389.
    1. Otte K, Kayser B, Mansow-Model S, Verrel J, Paul F, Brandt AU, Schmitz-Hübsch T. Accuracy and reliability of the Kinect version 2 for clinical measurement of motor function. PLoS One. 2016;11(11):e0166532. doi: 10.1371/journal.pone.0166532.
    1. Cai L, Ma Y, Xiong S, Zhang Y. Validity and reliability of upper limb functional assessment using the Microsoft Kinect v2 sensor. Appl Bionics Biomech. 2019;2019:7175240. doi: 10.1155/2019/7175240. doi: 10.1155/2019/7175240.
    1. European Parliament and the Council of the European Union Regulation (EU) 2017/745 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC: Regulation (EU) 2017/745. 2017. Apr 05, [2020-06-05]. .

Source: PubMed

3
Sottoscrivi