发表机构
Worcester Polytechnic Institute; Universidad Tecnológica de Panamá（伍斯特理工学院; 巴拿马科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出无线智能鞋垫平台，利用足底压力和惯性信号，通过HGB等机器学习方法识别坐、站、行走等状态，在参与者独立验证中达到高F1分数，为老年人活动监测和跌倒预防奠定基础。
AI 中文摘要
老年人跌倒前往往会出现活动能力、平衡能力和姿势转换的变化。本文提出了一种无线智能鞋垫平台和机器学习工作流，用于从足底压力和惯性信号中识别坐、站、行走和不稳定行走。每只鞋垫集成了16个主动压力传感位置和一个六维IMU流，包括三轴加速度和角速度。数据从15名健康成年人处以80 Hz的频率采集，并分割为重叠窗口。首先使用分层10折交叉验证筛选窗口长度和候选模型族；随后通过参与者独立的5折分层组交叉验证获得主要性能估计，确保来自同一参与者的所有窗口保持在单个折中。在该协议下，基于直方图的梯度提升（HGB）在左脚和右脚上的宏F1分数分别为0.954和0.959，双侧传感时为0.980。使用相同参与者独立折评估的紧凑型1D-CNN并未显著优于HGB（p=0.0625）。结果表明，低剖面鞋类传感可以从压力和IMU测量中推断训练中未见参与者的活动状态，为老年人护理中的活动监测和跌倒预防奠定了基础。
英文摘要
Falls in older adults are often preceded by changes in mobility, balance, and postural transitions. This paper presents a wireless smart insole platform and machine-learning workflow for recognizing sitting, standing, walking, and unstable walking from plantar-pressure and inertial signals. Each insole integrates 16 active pressure-sensing locations and a six-dimensional IMU stream consisting of tri-axial acceleration and angular velocity. Data were collected from 15 healthy adults at 80~Hz and segmented into overlapping windows. Window length and candidate model families were first screened with stratified 10-fold cross-validation; the primary performance estimate was then obtained with participant-independent 5-fold Stratified Group cross-validation, ensuring that all windows from a participant remained in a single fold. Under this protocol, Histogram-Based Gradient Boosting (HGB) achieved macro-F1 scores of 0.954 and 0.959 for the left and right feet, respectively, and 0.980 with bilateral sensing. A compact 1D-CNN evaluated with the same participant-independent folds did not significantly outperform HGB ($p=0.0625$). The results show that low-profile footwear sensing can infer activity state from pressure and IMU measurements for participants unseen during training, establishing a basis for activity monitoring and fall prevention in elderly care.
