Internet of Things (IoT) Enables Robot-Assisted Therapy as a Home Program for Training Upper Limb Functions in
Li-Chieh Kuo1, Kang-Chin Yang2, Yu-Ching Lin3
1Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan; Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan.
Objective:
To compare the effects of using an Internet of things (IoT)-assisted tenodesis-induced-grip exoskeleton robot (TIGER) and task-specific motor training (TSMT) as home programs for the upper-limb (UL) functions of patients with chronic stroke to overturn conventional treatment modes for stroke rehabilitation.
Design:
A randomized 2-period crossover study.
Setting:
A university hospital.
Participants:
Eighteen chronic stroke patients were recruited and randomized to receive either the IoT-assisted TIGER first or TSMT first at the beginning of the experiment (N=18).
Intervention:
In addition to the standard hospital-based therapy, participants were allocated to receive a 30-minute home-based, self-administered program of either IoT-assisted TIGER first or TSMT first twice daily for 4 weeks, with the order of both treatments reversed after a 12-week washout period. The exercise mode of the TIGER training included continuous passive motion and the functional mode of gripping pegs. The TSMT involved various movement components of the wrist and hand.
Main Outcome Measures:
The outcome measures included the box and block test (BBT), the Fugl-Meyer assessment for upper extremity (FMA-UE), the motor activity log, the Semmes-Weinstein Monofilament test, the range of motion (ROM) of the wrist joint, and the modified Ashworth scale.
Results:
Significant treatment-by-time interaction effects emerged in the results for the BBT (F(1.31)=5.212 and P=.022), the FMA-UE (F(1.31)=6.807 and P=.042), and the ROM of the wrist extension (F(1.31)=8.618 and P=.009). The participants who trained at home with the IoT-assisted TIGER showed more improvement of their UL functions.
Conclusions:
The IoT-assisted TIGER training has the potential for restoring the UL functions of stroke patients.
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