用优化的可穿戴传感器+深度学习，精准估算帕金森步态的地面反作用力。
Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data
- 用混合CNN-BiLSTM模型，基于13个IMU数据估计双侧垂直地面反作用力。
- 在患者和健康人中分别达到0.98（个体内）和0.91~0.93（跨个体）的预测精度。
- 发现帕金森患者只需两个最优位置的传感器即可实现可靠估计，适合日常监测。
帕金森病（PD）的精准步态分析通常依赖实验室设备获取生物力学数据，如地面反作用力（GRFs）。利用惯性测量单元（IMUs）估算GRFs是一种可行替代方案，但在帕金森病等病理步态中仍具挑战性，因其高度变异性与复杂性。现有监测方法常需多个体表传感器，限制实用性并降低患者依从性。迄今为止，尚无研究探讨深度学习在该问题中的应用。本研究首次提出一种深度学习框架，使用优化配置的可穿戴IMUs估计帕金森病患者的双侧垂直地面反作用力（vGRFs）。采用混合CNN-BiLSTM模型，在61名帕金森患者与65名健康对照者的13个IMU数据上分别训练。模型在个体内部表现出极高精度（R² = 0.98），跨个体泛化能力良好（健康人：R² = 0.93，帕金森患者：R² = 0.91）。传感器布局显著影响精度，帕金森患者与健康人的最优位置不同；减少至单个传感器时精度明显下降。帕金森患者最优配置为4个传感器，但仅需2个传感器即可实现稳健估计。该紧凑方案具备实际推广价值。总体而言，该方法支持发展基于可穿戴vGRF的帕金森步态分析系统，适用于临床评估、远程监测及个性化康复。
原文摘要 · Abstract (English)
Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction forces (GRFs). Estimating GRFs using inertial measurement units (IMUs) provides a feasible alternative. However, this approach remains challenging in pathological gait like PD due to its high variability and complexity. Moreover, existing monitoring approaches often require multiple body-mounted sensors, which limit practicality and reduce patient compliance. To date, no study has investigated the application of deep learning approaches to address this challenge. This study proposes, for the first time, a deep learning framework to estimate bilateral vertical GRFs (vGRFs) in PD using an optimized set of wearable IMUs. A hybrid CNN-BiLSTM model was trained separately on data from 61 PD patients and 65 healthy controls (HC) using 13 IMUs. The model achieved high intra-subject accuracy ($R^2$ = 0.98) and strong inter-subject generalization ($R^2$ = 0.93 for HC, $R^2$ = 0.91 for PD). Sensor configuration was found to significantly influence estimation accuracy, with optimal sensor placement varying between PD patients and HC. For PD patients, estimation accuracy dropped markedly when reducing to a single IMU. The optimal configuration for PD used four IMUs. We identified a minimal setup with only two IMUs still enabled robust estimation. This compact setup offers a practical and scalable solution. Overall, the proposed approach supports the development of wearable vGRF-based gait analysis systems for Parkinsonian gait and potentially other pathological conditions, enabling accessible clinical assessments, remote monitoring, and personalized rehabilitation.
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