Lightweight Shoulder Physiotherapy Exercise Recognition via Efficient Channel Attention and Depthwise Separable Residual Networks on Wrist-Worn IMU
TL;DR
ECA-ResNet1D-Lite is presented, a lightweight one-dimensional depthwise separable residual network augmented with efficient channel attention (ECA), trained and evaluated on the SPARS9x dataset comprising six shoulder exercises recorded from 20 subjects using a commercial wrist-worn smartwatch at 50 Hz, establishing that subject-independent shoulder physiotherapy monitoring is computationally feasible on current smartwatch hardware.
Abstract
Accurate and subject-independent recognition of shoulder physiotherapy exercises from wrist-worn inertial measurement unit (IMU) signals is essential for automated home-based rehabilitation monitoring, yet existing deep learning models are too parameter-intensive to deploy on resource-constrained smartwatch hardware. This paper presents ECA-ResNet1D-Lite, a lightweight one-dimensional depthwise separable residual network augmented with efficient channel attention (ECA), trained and evaluated on the SPARS9x dataset comprising six shoulder exercises recorded from 20 subjects using a commercial wrist-worn smartwatch at 50 Hz. Because 50% window overlap allows adjacent windows to share samples, we report three protocols—window-level five-fold, recording-level grouped five-fold, and leave-one-subject-out (LOSO) cross-validation—with model selection performed throughout on validation data disjoint from the test partition. Under LOSO, the primary protocol, the model attains 99.1 ± 1.0% accuracy while requiring only 13,612 parameters and 0.46 M multiply–accumulate operations per window—the highest accuracy and the lowest between-subject dispersion of the seven architectures trained under an identical protocol, ahead of the strongest unconstrained baseline (InceptionTime, 98.8 ± 1.4%) at 36.3× fewer parameters and 213× fewer operations, and ahead of the parameter-efficient designs TinyHAR (97.4 ± 2.5%) and TinierHAR (97.4 ± 2.1%). An ablation over four attention variants (SE, ECA, CBAM, and multi-head self-attention) shows ECA to be the cheapest, adding six parameters (0.04% overhead), while delivering the largest LOSO gain over the same backbone without attention (+0.2 percentage points) and reducing the cross-subject standard deviation from 1.4% to 1.0%. Per-class analysis further reveals that shoulder girdle stabilization is the most challenging exercise under LOSO (F1-score: 96.4%), despite being the most represented class, attributable to its quasi-static, low-amplitude IMU signature. Deployed to an Apple Watch Ultra 2, the model classifies a 4-s window in 0.24 ms (duty cycle 0.012%), establishing that subject-independent shoulder physiotherapy monitoring is computationally feasible on current smartwatch hardware.