Smart Insole Human Activity Recognition for Continuous Monitoring in Elderly Care

2026-09-16

AI Summary

本文通过无线智能鞋垫平台和机器学习方法，利用足底压力和惯性信号识别老年人的坐、站、行走及不稳定行走状态，以监测活动并预防跌倒。

Abstract

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.

Problem

• Smart Insole
• Human Activity Recognition
• Elderly Care
• Fall Prevention

Innovation

• wireless smart insole
• machine-learning workflow
• Histogram-Based Gradient Boosting (HGB)
• plantar-pressure and inertial signals