发表机构
University of Twente; Eindhoven University of Technology（特文特大学; 埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于腕戴IMU的简化运动学模型及三种混合物理-AI神经网络方法，用于估计身体质心加速度，在步态和坐站活动中显著降低误差，并展现出不同噪声下的鲁棒性优势。
AI 中文摘要
腕戴式惯性测量单元（IMU）已被广泛用于日常健康监测。然而，它并不能完全代表全身动力学，而身体质心（COM）被认为是生理参考标准。因此，本研究提出了一种简化的运动学模型（KM），旨在将腕部IMU映射到质心加速度。该模型基于若干简化假设，使得仅凭腕部IMU测量即可求解动力学方程。本研究进一步提出了三种混合人工智能建模方法，即基于人体运动学模型的神经网络（HKM-NN）模型，以利用灰盒和黑盒建模的优势。HKM-NN方法包括串行学习（ser-）和两种并行学习方法（sim1-和sim2-）。所提出的模型使用我们的数据集进行训练和测试，该数据集包含10名健康志愿者在六种步态活动和坐站（SS）过渡运动中的腕部IMU测量值和真实质心测量值。结果表明，从腕部IMU测量估计质心加速度是可行的。我们的KM模型取得了令人满意的结果，步态活动的误差范围为6.7%至12.5%，SS的误差为5.6%。与KM模型相比，我们的HKM-NN模型显著提升了性能，步态活动的误差达到5.3%至9.3%，SS的最佳误差为3.9%。此外，HKM-NN模型在噪声测试条件下表现出不同的鲁棒性特征，sim1-/sim2-在高斯扰动下通常保持更强的鲁棒性，而KM模型在椒盐噪声下表现出相对较强的鲁棒性。这些发现强调了将生物力学结构与数据驱动学习相结合对于在不完美和噪声测量条件下运行的可穿戴传感应用的重要性。
英文摘要
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.
CommentsThe abstract of this manuscript has been partially presented at the NaturePitor2026 conference (Redefining Healthcare in the Age of AI: a Nature Conference). A supplementary material file is included in the source file
