AquaTag: An Open-Source Wearable Platform Enabling Flexible Sensor Placement for Aquatic Sensing

Commercial swim wearables increasingly provide performance feedback but remain restricted by fixed, single-placement designs. When worn outside their intended location, the accuracy of motion recognition and swimming analysis drops sharply. Moreover, most existing devices are closed-source, limiting access to raw sensor data and hindering reproducibility and innovation in aquatic HCI research. To address these limitations, we present AquaTag, an open-source aquatic wearable platform that enables flexible placement and consistent motion analysis across different locations. The AquaTag integrates a motion sensor, barometer, and temperature sensor. Using a documented epoxy-potting workflow adapted from established underwater electronics practices, it remains functional during a controlled pressure test equivalent to approximately 14 m water depth for 30 minutes. To support underwater use, AquaTag offers NOR flash or microSD storage for offline logging and Bluetooth for above-water data synchronization. Its power system provides up to 4.4 hours of offline logging or 7.8 hours of non-submerged streaming. The power control and charging mechanisms are designed to meet the waterproofing requirements of the device. The hardware can be manufactured at about $40 per unit. A companion software suite is also developed to annotate and analyze motion data. Specifically, our contributions include: (1) a compact, robust sensing module with a documented, low-cost waterproof packaging workflow suitable for rapid in-house fabrication; and (2) a placement-adaptive machine learning pipeline that delivers accurate swimming analytics from a single motion sensor at flexible body locations. By conditioning a deep learning backbone on the sensor's mounting position, our model learns to infer swimmer kinematics when worn on the head, wrist, or lower back. In leave-one-subject-out evaluation, AquaTag achieved mean macro- F 1 scores of 91.7% for stroke-type classification and 90.6% for swim-state classification across the head, lower-back, and wrist placements; the corresponding best-placement scores were 96.5% and 92.2%, respectively. The AquaTag hardware designs, PCB files, bill of materials, firmware, enclosure CAD files, fabrication instructions, and companion application are released publicly.