Ghaffarzadeh, P.; Chakraborty, D.; Aslansefat, K.; Dostan, A.; Papadopoulos, Y.

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.