Tracking Upper Limb Motion via Wearable Solutions: Systematic Review of Research from 2011-2023 (Preprint)

The development of wearable solutions for tracking upper limb motion has gained research interest over the past decade. This paper provides a systematic review of related research on the type, feasibility, signal processing techniques, and feedback of wearable systems for tracking upper limb motion, mostly in rehabilitation applications, to understand and monitor human movement. The aim of this article is to investigate how wearables are used to capture upper limb functions, especially related to clinical and rehabilitation applications. A systematic literature search identified 27 relevant studies published in English from 2011 to 2023, across 4 databases: ACM Digital Library, IEEE Xplore, PubMed, and ScienceDirect. We included papers focusing on motion or posture tracking for the upper limbs, wearable devices, feedback given to end users, and systems having clinical or rehabilitation purposes. We excluded papers focusing on exoskeletons, robotics, prosthetics, orthoses, or activity recognition systems; reviews; and books. The results from this research focus on wearable devices that are designed to monitor upper limb movement. More specifically, studies were divided into 2 distinct categories: clinical motion tracking (15/27, 56%) and rehabilitation (12/27, 44%), involving healthy individuals and patients, with a total of 439 participants. Among the 27 studies, the majority (19/27) used inertial measurement units to track upper limb movement or smart textiles embedded with sensors. These devices were attached to the body with straps (mostly Velcro), providing flexibility and stability. The developed wearable devices positively influenced user motivation through the provided feedback, with visual feedback being the most common owing to the high level of independence provided. Moreover, a variety of signal processing techniques, such as Kalman and Butterworth filters, were applied to ensure data accuracy. However, limitations persist and include sensor positioning, calibration, and battery life, as well as a lack of clinical data on the effectiveness of these systems. The sampling rate of the data collection ranged from 50 Hz to 2000 Hz, which notably affected data quality and battery life. In addition, several findings were inconclusive, and thus, further future research is needed to understand and improve upper limb posture to develop progressive wearable systems. This paper offers a comprehensive overview of wearable monitoring systems, with a focus on upper limb motion tracking and rehabilitation. It emphasizes the various types of available solutions; their efficacy, wearability, and feasibility; and proposed processing techniques. Finally, it presents robust findings regarding feedback accuracy derived from experiments and outlines potential future research directions.