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.

翻译：医疗基础设施的有限可及性迫使老年人和弱势患者依赖家庭护理，这常常导致忽视和不良依从性（如瑜伽或物理治疗等治疗性锻炼）。为弥补这一不足，我们提出了一种基于可穿戴惯性传感器和机器学习的低成本自动化人体活动识别框架。活动数据（包括行走、上楼、下楼、坐、站和躺）通过加速度计和陀螺仪测量收集。我们评估了四种经典分类器——逻辑回归、随机森林、支持向量机（SVM）和k近邻（k-NN），并将其与提出的支持张量机（STM）进行比较。实验结果表明，SVM的准确率达到93.33%，而逻辑回归、随机森林和k-NN的准确率为91.11%。相比之下，STM显著优于这些模型，测试准确率达到96.67%，最高交叉验证准确率达98.50%。与传统方法不同，STM利用张量表示来保留时空运动动态，从而实现对多样化活动的鲁棒分类。所提出的框架在远程医疗、老年辅助、儿童活动监测、瑜伽反馈和智能家居健康等领域展现出强大潜力，为低资源和农村医疗环境提供了一种可扩展的解决方案。