An Efficient Deep Learning Method Based on a Hybrid LiDAR and Wearable Sensor Framework for Healthcare-Oriented Human Activity Recognition

Abstract

Human activity recognition (HAR) is one of the most important components of modern healthcare monitoring systems, particularly for providing assistance to the elderly, rehabilitating patients, and continuously monitoring their health. The traditional method of activity recognition often relies on only a single sensing modality, limiting its accuracy and robustness when used in the real world. A hybrid LiDAR-wearable sensor framework is proposed in this study to address this challenge. Using LiDAR sensors and wearable devices to collect motion-related signals, the proposed system integrates spatial information and motion-related data. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are used to learn spatial and temporal features from fusion sensor data. Activity classification is improved by extracting features from sensor signals and fusing multimodal features. A wide variety of human activities are accurately detected by the proposed model in experimental tests, such as walking, sitting, standing, jogging, and climbing stairs. CNN, LSTM, and CNN-Bi-LSTM are outperformed by the model, which achieves 99.3% of classification accuracy. Accordingly, the proposed method based on the integration of deep learning algorithm and the usage of LiDAR sensing together with wearable sensor data increases activity recognition systems' accuracy and reliability. Human Activity Recognition (HAR) plays an important role in modern healthcare monitoring, especially for elderly care, rehabilitation, and continuous patient supervision. This study proposes a hybrid LiDAR and wearable sensor framework that combines spatial and motion data for accurate activity recognition. CNN and LSTM models are used to extract spatial and temporal features from fused sensor data. Experimental results show that the proposed model accurately recognizes activities such as walking, sitting, standing, jogging, and stair climbing, achieving 99.3% classification accuracy and outperforming existing CNN, LSTM, and CNN-Bi-LSTM methods. The integration of LiDAR and wearable sensors with deep learning improves the accuracy, reliability, and efficiency of healthcare-oriented HAR system.