Ground Reaction Force Estimation via Time-aware Knowledge Distillation

2025-06-12

IEEE Internet of Things Journal

AI Summary

To address the low accuracy and poor noise robustness of portable insole sensors in estimating ground reaction forces (GRFs), this paper proposes a temporal-aware knowledge distillation framework. Unlike conventional approaches, our method introduces temporal awareness into knowledge distillation for the first time, explicitly modeling intra-batch temporal similarity and feature complementarity to enhance the generalization capability of lightweight student models—such as TCNs or LSTMs—toward dynamic gait patterns. Evaluated on the treadmill-insole synchronized dataset, the proposed method reduces mean GRF estimation error by 23.6% and achieves an RMSE < 0.15 BW, meeting clinical-grade accuracy and outperforming existing state-of-the-art methods. The core contribution lies in the design and application of the temporal-aware distillation mechanism, establishing a novel paradigm for low-cost, high-accuracy wearable gait analysis.

Technology Category

• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Estimating Ground Reaction Force from noisy insole sensors
• Improving accuracy of wearable-based gait analysis
• Overcoming portability limitations of treadmill GRF measurements

Innovation

• Time-aware Knowledge Distillation framework
• Leverages similarity and temporal features
• Outperforms baselines in GRF estimation