Edge-First Ground Reaction Force Estimation with Consumer Smartwatches

Abstract

Ground reaction force (GRF) measurement remains largely confined to instrumented laboratories, limiting longitudinal monitoring in daily life. This article presents an edge-first wearable system for estimating vertical GRF from consumer smartwatches. Two Apple Watch Series 6 devices worn at the wrist and waist stream 12-channel inertial data at 100 Hz to an iPhone, where preprocessing, storage, and inference occur locally without cloud dependence. The proposed GRFNet-MultiScale model is a compact temporal convolutional network with four dilated residual blocks and a global context branch. Under leave-one-subject-out evaluation on 539 stance windows from 10 healthy participants, the dual-sensor system achieved a mean Pearson correlation of 0.798 with an RMSE of 257 N, while a wrist-only configuration retained 82.5% of dual-sensor correlation. Temporal attribution remained stable across validation folds and identified early-stance wrist acceleration as the dominant reproducible signal. The system is strongest for cyclic locomotion.