Wrist sensors estimate whole-body movement with hybrid physics ai model
Hybrid Physics-AI Framework of Body Center of Mass Dynamics from Wrist-Worn Sensors
Artificial Intelligence
Summary
Measuring whole-body movement is tricky when only using wrist sensors, which don’t capture the entire body’s motion. The authors developed a simpler physics-based model to estimate body center of mass movement from wrist sensors. They improved this by combining the physics model with neural networks to better handle real data and noisy measurements. This hybrid approach led to more accurate and robust estimates of whole-body motion during walking and standing up. Their work shows that mixing physics knowledge with AI can help wearable sensors better understand body dynamics.
What this means in practice
- •For wearable device engineers: Improve algorithms for wrist-worn devices to accurately estimate body center of mass movements during daily activities.
- •For fitness technology developers: Develop more reliable real-time motion tracking in fitness trackers by combining physics-based models with AI for noisy sensor data.$Commercial implications: Enables wearable fitness trackers to provide precise whole-body movement metrics, enhancing product accuracy and attractiveness.
Authors
Shuhao Que, Valentina Breschi, Ying Wang
Abstract
Wrist-worn IMU has been widely used for daily-life health monitoring. Yet, it does not fully represent whole-body dynamics, for which the body center of mass (COM) is considered the physiological reference standard. Therefore, this work proposes a simplified kinematic model (KM), which is designed to map the wrist IMU to the COM acceleration. It is built upon several reductive assumptions that enable the solvability of the dynamic equations based on wrist IMU measurements alone. This work further proposes three types of hybrid AI modeling methods, namely human kinematic model-based neural network (HKM-NN) models, to leverage the power of both grey-box and black-box modeling. The HKM-NN methods include serial learning (ser-) and two approaches of simultaneous learning (sim1- and sim2-). The proposed models are trained and tested using our dataset, which includes wrist IMU measurements and ground-truth COM measurements from 10 healthy volunteers during six gait activities and sit-to-stand (SS) transitional movement. The results demonstrate the feasibility of estimating COM acceleration from wrist IMU measurements. Our KM model yields satisfactory results, with an error ranging from 6.7% to 12.5% for gait activities and 5.6% for the SS. In comparison with the KM model, our HKM-NN models significantly enhance the performance, achieving 5.3% to 9.3% errors for gait activities, and the best error of 3.9% for the SS. In addition, the HKM-NN models demonstrate distinct robustness characteristics under noisy test conditions, with sim1-/sim2- generally maintaining greater robustness under Gaussian perturbations, while the KM model exhibits comparatively strong robustness under salt-and-pepper noise. These findings highlight the importance of combining biomechanical structure with data-driven learning for wearable sensing applications operating under imperfect and noisy measurement conditions.