MuscleSense: Making Muscle Effort Audible for Eyes-Free In-the-Loop Regulation

Strength training often demands attention to form, balance, and pacing, leaving little bandwidth to notice muscle-level deviations such as asymmetric loading or compensatory coordination. We present MuscleSense , a deployable audio-first closed-loop biofeedback system that makes muscle effort perceivable and actionable during movement without requiring visual attention. Using a minimal wearable setup (bilateral front-thigh IMU + EMG) and on-device inference, MuscleSense estimates a feedback-aligned effort state designed for control rather than signal reconstruction: overall intensity ( I ) plus three descriptors capturing symmetry ( S ), co-contraction ( C ), and temporal stability ( D ). This state drives a two-layer sonification policy: continuous pitch supports moment-to-moment effort grounding, while rate-limited event cues highlight persistent deviations with bounded listening load.; AB@We evaluate MuscleSense on N =17 participants, using multi-channel reference EMG only to construct and evaluate labels. Results show strong agreement with EMG-derived ground truth across users and activities, stable smartphone operation with bounded end-to-end latency, and a deployable rate-limited cue policy designed to reduce listening burden. A short within-subject closed-loop squat study further shows significant condition effects on pacing-related outcomes and favorable MuscleSense trends compared with no-feedback, mirror, and IMU-only audio baselines. A supplementary lunge study provides initial subjective evidence that participants perceived deviation-related feedback as useful for noticing inter-limb effort differences, instability, and compensation. Together, these results suggest that audio-first muscle-effort feedback can support eyes-free in-the-loop regulation, while dedicated behavioral validation of individual deviation cues remains an important next step.