Smart Insole Human Activity Recognition for Continuous Monitoring in Elderly Care
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Computer Science > Machine Learning
Title:Smart Insole Human Activity Recognition for Continuous Monitoring in Elderly Care
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.19359 [cs.LG] |
| (or arXiv:2609.19359v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.19359
arXiv-issued DOI via DataCite (pending registration)
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