Application of Home-Based Wearable Technologies in Physical Rehabilitation for Stroke: A Scoping Review

被引:16
作者
Toh, Sharon Fong Mei [1 ]
Fong, Kenneth N. K. [1 ]
Gonzalez, Pablo Cruz [1 ]
Tang, Yuk Ming [2 ]
机构
[1] Hong Kong Polytech Univ, Dept Rehabil Sci, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China
关键词
Wearable technology; self-directed rehabilitation; stroke; home-based intervention; MOTOR RECOVERY; HAND; STIMULATION; GAIT; INJURIES; THERAPY; PHASE; SPORT; LIMB;
D O I
10.1109/TNSRE.2023.3252880
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Using wearable technologies in the home setting is an emerging option for self-directed rehabilitation. A comprehensive review of its application as a treatment in home-based stroke rehabilitation is lacking. This review aimed to 1) map the interventions that have used wearable technologies in home-based physical rehabilitation for stroke, and 2) provide a synthesis of the effectiveness of wearable technologies as a treatment choice. Electronic databases of the Cochrane Library, MEDLINE, CINAHL, and Web of Science were systematically searched for work published from their inception to February 2022. This scoping review adopted Arksey and O'Malley's framework in the study procedure. Two independent reviewers screened and selected the studies. Twenty-seven were selected in this review. These studies were summarized descriptively, and the level of evidence was assessed. This review identified that most research focused on improving the hemiparetic upper limb (UL) function and a lack of studies applying wearable technologies in home-based lower limb (LL) rehabilitation. Virtual reality (VR), stimulation-based training, robotic therapy, and activity trackers are the interventions identified that apply wearable technologies. Among the UL interventions, "strong " evidence was found to support stimulation-based training, "moderate " evidence for activity trackers, "limited " evidence for VR, and "inconsistent evidence " for robotic training. Due to the lack of studies, understanding the effects of LL wearable technologies remains "very limited. " With newer technologies like soft wearable robotics, research in this area will grow exponentially. Future research can focus on identifying components of LL rehabilitation that can be effectively addressed using wearable technologies.
引用
收藏
页码:1614 / 1623
页数:10
相关论文
共 87 条
  • [1] Design, development and deployment of a hand/wrist exoskeleton for home-based rehabilitation after stroke - SCRIPT project
    Amirabdollahian, F.
    Ates, S.
    Basteris, A.
    Cesario, A.
    Buurke, J.
    Hermens, H.
    Hofs, D.
    Johansson, E.
    Mountain, G.
    Nasr, N.
    Nijenhuis, S.
    Prange, G.
    Rahman, N.
    Sale, P.
    Schaetzlein, F.
    van Schooten, B.
    Stienen, A.
    [J]. ROBOTICA, 2014, 32 (08) : 1331 - 1346
  • [2] [Anonymous], 2015, REV MAN S
  • [3] [Anonymous], 2013, EndNote X9 ed
  • [4] Effectiveness of telerehabilitation in the management of adults with stroke: A systematic review
    Appleby, Emma
    Gill, Sophie Taylor
    Hayes, Lucinda Kate
    Walker, Tessa Lauren
    Walsh, Matt
    Kumar, Saravana
    [J]. PLOS ONE, 2019, 14 (11):
  • [5] Arskey H., 2005, INT J SOC RES METHOD, V8, P19, DOI [10.1080/1364557032000119616, DOI 10.1080/1364557032000119616, https://doi.org/10.1080/1364557032000119616]
  • [6] Bellomo R. G. P., 2020, J CENT NERV SYST DIS, V12, P1
  • [7] Advances in wearable technology and applications in physical medicine and rehabilitation
    Bonato P.
    [J]. Journal of NeuroEngineering and Rehabilitation, 2 (1)
  • [8] Bowen A., 2016, CLIN GUIDELINE STROK
  • [9] Home-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME II)
    Casas, Rafael
    Sandison, Melissa
    Nichols, Diane
    Martin, Kaelin
    Phan, Khue
    Chen, Tianyao
    Lum, Peter S.
    [J]. FRONTIERS IN NEUROROBOTICS, 2021, 15
  • [10] Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study
    Chae, Sang Hoon
    Kim, Yushin
    Lee, Kyoung-Soub
    Park, Hyung-Soon
    [J]. JMIR MHEALTH AND UHEALTH, 2020, 8 (07):