An analysis of wrist and forearm range of motion using the Dartfish motion analysis system

Telerehabilitation Technology Used for Remote Wrist/Finger Range of Motion Evaluation: A Scoping Review

Background

Monitoring finger/wrist range of motion (ROM) is an important component of routine hand therapy after surgery. Telerehabilitation is a field that may potentially address various barriers of in-person hand therapy appointments. Therefore, the purpose of this scoping review is to identify telerehabilitation technologies that can be feasibly used in a patient's home to objectively measure finger/wrist ROM.

Methods

Following PRISMA-ScR guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases using alternative word spellings for the following core concepts: "wrist/hand," "rehabilitation," and "telemedicine." Studies were imported into Covidence, and systematic two-level screening was done by two independent reviewers. Patient demographics and telerehabilitation information were extracted from the selected articles, and a narrative synthesis of the findings was done.

Results

There were 28 studies included in this review, of which the telerehabilitation strategies included smartphone angle measurement applications, smartphone photography, videoconference, and wearable or external sensors. Most studies measured wrist ROM with the most accurate technologies being wearable and external sensors. For finger ROM, the smartphone angle application and photography had higher accuracy than sensor systems. The telerehabilitation strategies that had the highest level of usability in a remote setting were smartphone photographs and estimation during virtual appointments.

Conclusions

Telerehabilitation can be used as a reliable substitute to in-person goniometer measurements, particularly the smartphone photography and motion sensor ROM measurement technologies. Future research should investigate how to improve the accuracy of motion sensor applications that are available on easy-to-access devices.

A mobile platform-based app to assist undergraduate learning of human kinematics in biomechanics courses

Whole-body biomechanics examines different physical characteristics of the human body movement by applying principles of Newtonian mechanics. Therefore, undergraduate biomechanics courses are highly demanding in mathematics and physics. While the inclusion of laboratory experiences can augment student comprehension of biomechanics concepts, the cost and the required expertise associated with experiment equipment can be a burden of offering laboratory sessions. In this study, we developed a mobile app to facilitate learning human kinematics in biomechanics curriculums. First, a mobile-based computer-vision algorithm that is based on Convolutional pose machine (CPM), MobileNet V2, and TensorFlow Lite framework is adopted to reconstruct 2D human poses from the images collected by a mobile device camera. Key joint locations are then applied to the human kinematics variable estimator for human kinematics analysis. Simultaneously, students can view various kinematics data for a selected joint or body segment in real-time through the user interface of the mobile device. The proposed app can serve as a potential instructional tool to assist in conducting human motion experiments in biomechanics courses.