Background Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist. Objective This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors. Methods The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system. Results The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03). Conclusions This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future. Trial Registration Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t

Background Mirror therapy (MT) has been used for functional recovery of the affected hand by providing the mirrored image of the unaffected hand movement, which induces neural activation of the cortical hemisphere contralateral to the affected hand. Recently, many wearable robots assisting the movement of the hand have been developed, and several studies have proposed robotic mirror therapy (RMT) that uses a robot to provide mirrored movements of the unaffected hand to the affected hand with the robot controlled by measuring electromyography or posture of the unaffected hand. In some cases of RMT a mirror is placed to allow the person to observe only the unaffected hand but in others users simply observe the robotically assisted hand performing the mirrored movements, as was the case in this study. There have been limited evaluations of the cortical activity during RMT compared to MT and robotic therapy (RT) providing passive movements despite the difference in the modality of sensory feedback and the involvement of motor intention, respectively. Methods This paper analyzes bilateral motor cortex activation in nine healthy subjects and five chronic stroke survivors during a pinching task performed in MT, RT, and RMT conditions using functional near infrared spectroscopy (fNIRS). In the MT condition, the person moved the unaffected hand and observed it in a mirror while the affected hand remained still. In RT condition passive movements were provided to the affected hand with a cable-driven soft robotic glove, while, in RMT condition, the posture of the unaffected hand was measured by a sensing glove and the soft robotic glove mirrored its movement on the affected hand. Results For both groups, the RMT condition showed the greatest mean cortical activation on the motor cortex contralateral to the affected (non-dominant for the healthy group) hand compared to other conditions. Individual results indicate that RMT induces similar or greater neural activation on the motor cortex compared to MT and RT conditions. The interhemispheric activations of both groups were balanced in RMT condition. In MT condition, significantly greater activation was shown on the hemisphere ipsilateral to the affected (dominant for the healthy group) hand for both subject groups, while the contralateral side showed significantly greater activation for the healthy group in RT condition. Conclusion The experimental results indicate that combining visual feedback, somatosensory feedback, and motor intention are important for greater stimulation on the contralateral motor cortex of the affected hand. RMT that includes these factors is hypothesized to achieve a more effective functional rehabilitation due to greater and more balanced cortical activation.

The human hand is one of the most complex and compact grippers that has arisen as a product of natural genetic engineering; it is highly versatile, as it handles power and precision tasks. Since proper contact points and force directions are required to ensure versatility and secure a stable grip on an object, there must be a large workspace and controllable tip force directions for the digits. Although they are important, many individuals with neuromuscular diseases experience loss of these features. Thus, we propose a high-degree-of-freedom (DOF) soft robotic glove inspired by the anatomical features of human hands. The mechanism for adjusting the position and force direction of each tip is based on the structure of the extrinsic and intrinsic muscle-tendon units. The large thumb workspace was achieved by assisting opposition/reposition and flexion/extension to enable various grasping postures. A bidirectional actuation control mechanism with a cable-actuated agonist and an elastomer antagonist increased the assisted DOF and maintained compactness. The kinematic and kinetic performances of our device were evaluated by performing tests with eight stroke survivors. The thumb workspace increased by 43%, 207%, and 248% in the distal-proximal, dorsal-palmar, and radial-ulnar directions, respectively. The pinching shear force decreased by 54% and 45% for the nonthumb digits and thumb, respectively. These device-assisted improvements allowed objects to be stably grasped and manipulated in various postures. The novel device can assist individuals with impaired hand function to improve their grasping performance. Clinical Research Information Service (CRIS) Registration Number: KCT0004855.

The relationship between arthritis or repetitive stress injuries (RSIs) in thumbs and rapidly increasing hours of smartphone usage is not fully elucidated. We evaluated axial joint reaction forces (AJRFs) and thumb torques in 19 healthy subjects performing typical smartphone tasks, which included tapping, tap game, and swiping. We measured force and torque when a subject tapped or swiped the panel of the smartphone and analyzed the motions of each joint using surface markers and motion capture systems. We calculated AJRFs and torques on each thumb joint using inverse dynamics. The results were then compared with representative activities such as computer keyboard typing and handwriting. The mean AJRFs/torques at the thumb carpometacarpal joint (CMCJ) while tapping the smartphone and tap gaming were 12.5 N/95.5 N mm and 21.1 N/187.21 N mm, respectively. AJRFs and torques were significantly higher during tap gaming activities than during simple tapping subtasks (p = 0.003 and p < 0.001, respectively). Compared with those during computer keyboard typing, the mean AJRFs and torques at the CMCJ during smartphone tapping was 3 (p = 0.075) and 1.4 times (p = 0.680) larger, respectively. Considering the rapidly increasing dependency on smartphones in our daily lives, long‐term exposure of the thumb to repetitive AJRFs and torques may lead to an acceleration of arthritis or aggravation of RSIs in thumbs. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2437–2444, 2019

A survey of 67 experienced orthopedic surgeons indicated that precise portal placement was the most important skill in arthroscopic surgery. However, none of the currently available virtual reality simulators include simulation / training in portal placement, including haptic feedback of the necessary puncture force. This study aimed to: (1) measure the in vivo force and stiffness during a portal placement procedure in an actual operating room and (2) implement active haptic simulation of a portal placement procedure using the measured in vivo data. We measured the force required for port placement and the stiffness of the joint capsule during portal placement procedures performed by an experienced arthroscopic surgeon. Based on the acquired mechanical property values, we developed a cable-driven active haptic simulator designed to train the portal placement skill and evaluated the validity of the simulated haptics. Ten patients diagnosed with rotator cuff tears were enrolled in this experiment. The maximum peak force and joint capsule stiffness during posterior portal placement procedures were 66.46 (±10.76N) and 2560.82(±252.92) N/m, respectively. We then designed an active haptic simulator using the acquired data. Our cable-driven mechanism structure had a friction force of 3.763 ± 0.341 N, less than 6% of the mean puncture force. Simulator performance was evaluated by comparing the target stiffness and force with the stiffness and force reproduced by the device. R-squared values were 0.998 for puncture force replication and 0.902 for stiffness replication, indicating that the in vivo data can be used to implement a realistic haptic simulator.