SwimCoachAI: A Multimodal Wearable System for Swimming Rhythm Sensing and Coaching
Abstract
Swimming performance depends on coordinated breathing, arm strokes, kicking, and physiological response. Existing wearable systems primarily report isolated metrics such as stroke count or heart rate, while camera-based biomechanical analysis requires dedicated infrastructure and is typically performed offline. This paper presents the design of SwimCoachAI, a three-point wearable sensing and coaching system that integrates smart goggles, a wrist-worn hub, and an ankle sensor to capture breathing, arm-stroke, and kick events together with heart rate and SpO2. The nodes communicate over a 915 MHz LoRa link using event-driven transmission, in which each node detects and timestamps semantic events locally, decoupling event time from packet arrival time. A lightweight classifier at the wrist hub is designed to recognize representative breathing–stroke–kick rhythms and to drive goggle LED and ankle-vibration cues toward a configurable target pattern. As an initial feasibility study, synthetic analyses show that event-driven transmission reduces nominal communication load by more than an order of magnitude relative to raw IMU streaming. Under the stated synthetic packet-loss model, buffering locally timestamped events improves four-class rhythm classification from a macro-F1 of 0.757 to 0.996 at an 80% initial packet delivery ratio. A retrospective Garmin case study of one 800-unit freestyle workout further examines wrist-derived heart rate, stroke rate, and SWOLF across manually annotated 2-beat, 4-beat, and 4–6-beat kick regimes, illustrating both measurable within-session variation and the inability of a wrist-only record to directly resolve breathing–kick coordination. Prototype implementation, in-water communication measurements, and controlled swimmer evaluation remain planned validation.Citation
Xiaochan Xue, Haofan Cai, Yanjun Pan, Shuqiong Wu, Yao Zheng, “SwimCoachAI: A Multimodal Wearable System for Swimming Rhythm Sensing and Coaching,” 2026 IEEE Annual Congress on Artificial Intelligence of Things (AIoT), 2026 (accepted).
Status: Accepted; DOI pending publication.