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BACKGROUND Regular self-administered exercise after stroke can improve upper limb function. For proper self-administered exercise, an appropriate guide is required to match the exercise level to the patient’s upper limb disability. OBJECTIVE This study aimed to develop a mobile health application to support self-administered graded upper limb exercises for patients with hemiplegia using deep learning model. METHODS To develop a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, we used a 5-step design thinking process. The exercise programs in the application were safe and easy to follow in the sitting position. A deep learning model was proposed to automatically evaluate the patients’ upper limb function for recommendation of exercise according to each patient’s disability. The model captured videos of patients reaching and grasping objects on a table as input and predicted the exercise level. Potential end-users, including patients, therapists, and physicians, evaluated the prototype and provided feedback for the usability and quality of the application using a mobile application rating scale. RESULTS A total of six features, my information, evaluation section, daily exercise, list of exercises, my current exercise status, and notification, were incorporated into the mobile application. The application consisted of 58 exercises categorized as postural and balance, range of motion, strengthening, and task-oriented training exercises with three levels of difficulty. A deep-learning-based model was proposed for automated upper limb function evaluation. The overall accuracy of classification of exercised level was 84.58% and inference time was 9~10 seconds. CONCLUSIONS We successfully developed a mobile application to support self-administered graded upper limb exercises for patients with hemiplegia based on individual ability, including automatic evaluation using a deep learning model.

INTRODUCTION/AIMS The posterior antebrachial cutaneous nerve (PACN) is one of the cutaneous branches of the radial nerve, and receives sensory input from the posterior arm and forearm. The aim of this study is to describe the ultrasonographic anatomy of the PACN. METHODS PACN and its branches were scanned using ultrasonography in 30 healthy volunteers. The distances between the epicondylar line and the bifurcation site of each branch of PACN (A) point of separation of the radial nerve and the PACN, (B) point of separation of the anterior and posterior divisions, (C) point of bifurcation of the posterior division into the epicondylar and anconeus branches), cross-sectional areas and depths from the skin surface to each nerve at positions A, B, and C were measured. RESULTS The mean distance from the epicondylar line to A, B, and C was 10.01 ± 0.82 cm, 7.46 ± 0.98 cm, and 4.02 ± 1.16 cm, respectively. The mean depth from the skin surface at A, B, and C was 1.00 ± 0.19 cm, 0.72 ± 0.19 cm, and 0.51 ± 0.12 cm, respectively. DISCUSSION Ultrasonographic visualization of the PACN and its major branches was feasible. The reference values in this study may be helpful for more accurate sonographic assessment and electrophysiologic study, and contribute to safer interventions around the elbow region.