Privacy-Preserving Sensor-Based Human Activity Recognition for Low-Resource Healthcare Using Classical Machine Learning

2026-01-29

AI Summary

This study addresses the challenges of insufficient activity monitoring and poor treatment adherence among elderly individuals undergoing home-based rehabilitation in resource-limited healthcare settings. To this end, the authors propose a low-cost, privacy-preserving human activity recognition method leveraging wearable inertial sensors—specifically accelerometers and gyroscopes. The key innovation lies in the adoption of a Support Tensor Machine (STM) model, which effectively preserves the spatiotemporal dynamics of human actions through tensor representation, enabling robust classification under low-resource conditions. Experimental results demonstrate that the STM achieves accuracies of 96.67% on the test set and 98.50% in cross-validation, significantly outperforming classical approaches such as logistic regression, random forest, support vector machines, and k-nearest neighbors. These findings underscore the STM’s promising potential for telehealth and in-home health monitoring applications.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation
• Machine Learning: Active Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Limited access to medical infrastructure forces elderly and vulnerable patients to rely on home-based care, often leading to neglect and poor adherence to therapeutic exercises such as yoga or physiotherapy. To address this gap, we propose a low-cost and automated human activity recognition (HAR) framework based on wearable inertial sensors and machine learning. Activity data, including walking, walking upstairs, walking downstairs, sitting, standing, and lying, were collected using accelerometer and gyroscope measurements. Four classical classifiers, Logistic Regression, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN), were evaluated and compared with the proposed Support Tensor Machine (STM). Experimental results show that SVM achieved an accuracy of 93.33 percent, while Logistic Regression, Random Forest, and k-NN achieved 91.11 percent. In contrast, STM significantly outperformed these models, achieving a test accuracy of 96.67 percent and the highest cross-validation accuracy of 98.50 percent. Unlike conventional methods, STM leverages tensor representations to preserve spatio-temporal motion dynamics, resulting in robust classification across diverse activities. The proposed framework demonstrates strong potential for remote healthcare, elderly assistance, child activity monitoring, yoga feedback, and smart home wellness, offering a scalable solution for low-resource and rural healthcare settings.

Problem

• Human Activity Recognition
• Low-Resource Healthcare
• Wearable Sensors
• Privacy-Preserving
• Elderly Care

Innovation

• Support Tensor Machine
• tensor representation
• privacy-preserving HAR
• low-resource healthcare
• inertial sensors