Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Journal:
arXiv
Published Date:
Jun 12, 2025
Abstract
Human gait analysis with wearable sensors has been widely used in various
applications, such as daily life healthcare, rehabilitation, physical therapy,
and clinical diagnostics and monitoring. In particular, ground reaction force
(GRF) provides critical information about how the body interacts with the
ground during locomotion. Although instrumented treadmills have been widely
used as the gold standard for measuring GRF during walking, their lack of
portability and high cost make them impractical for many applications. As an
alternative, low-cost, portable, wearable insole sensors have been utilized to
measure GRF; however, these sensors are susceptible to noise and disturbance
and are less accurate than treadmill measurements. To address these challenges,
we propose a Time-aware Knowledge Distillation framework for GRF estimation
from insole sensor data. This framework leverages similarity and temporal
features within a mini-batch during the knowledge distillation process,
effectively capturing the complementary relationships between features and the
sequential properties of the target and input data. The performance of the
lightweight models distilled through this framework was evaluated by comparing
GRF estimations from insole sensor data against measurements from an
instrumented treadmill. Empirical results demonstrated that Time-aware
Knowledge Distillation outperforms current baselines in GRF estimation from
wearable sensor data.
