Aug 2026· Internet Technology Letters· Vol 9· 0 citations· 3 references
TL;DR
A hybrid LiDAR‐wearable sensor framework that combines spatial and motion data for accurate activity recognition and the integration of deep learning algorithm and the usage of LiDAR sensing together with wearable sensor data increases activity recognition systems' accuracy and reliability.
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.
This study reviews pre-2018 developments and proposes an improved HAR framework incorporating advanced feature engineering, sensor fusion, and ensemble learning techniques, which shows strong potential in healthcare, fitness, and smart environments.
Silvia Diallo, F. Z. Idrissi· International Journal of Mod...· 0 citations
Textile-based sensors have emerged as key components for wearable healthcare, intelligent sports analytics, and industrial monitoring, yet their practical deployment remains constrained by electromagnetic interference, motion artifacts, nonlinear multimodal coupling, and limited computational efficiency in dynamic environments. This study proposes a hybrid AI framework integrating CNN– Transformer architectures to address these challenges through adaptive wavelet denoising, GAN-based data augmentation, and attention-guided multimodal feature fusion for heterogeneous sensing signals. A lightweight deployment strategy combining model pruning and quantization reduces computational overhead by 70%, enabling real-time inference with latency below 50 ms on edge devices. Experimental validation demonstrates that the proposed framework achieves 95.3% motion recognition accuracy using an 8 × 8 pressure sensor array, attains an AUC of 0.95 for respiratory anomaly detection under low signal-to-noise conditions, and predicts fabric remaining useful life with an RMSE of 6.2 hours through physics-informed learning. By improving robustness against environmental disturbances and enhancing multimodal information extraction, the proposed method provides an effective computational solution for intelligent textile sensing systems and offers methodological support for electromagnetic interference mitigation, wearable sensing networks, and advanced smart sensing platforms involving wireless signal acquisition and integrated electromagnetic environments.
A deep convolutional neural network (DCNN) based method for recognizing human activities using body-worn sensors’ time-series data after an enormous data analysis on the data.
S. Islam, Kamrul Hasan Talukder· International Journal of Int...· 0 citations
Human Activity Recognition (HAR) plays a significant role in various applications, from learning a discipline to physical rehabilitation. In older adults, activity patterns can indicate levels of frailty, which helps inform the design of physical training programs to prevent falls and maintain mobility. HAR sensing ranges from wearable sensors such as IMUs and RGB cameras to video, specialized gait laboratories, perturbation units, VR, and other modalities. This paper presents a comprehensive study of depth-based, skeleton-driven HAR and age group recognition (AGR) using data collected from real-world nursing home environments. Depth sensors offer a privacy-preserving and non-invasive alternative to wearable and RGB-based systems, enabling continuous 24-h monitoring without requiring user compliance. We systematically evaluate multiple modeling paradigms, including classical machine learning models (DT, RF, KNN, SVM, HMM, HMM+SVM), sequence-based models (LSTM, TCN, ARNN), and graph-based approaches, using skeletal joint data extracted from depth images. Experiments are conducted on two heterogeneous datasets: NTU RGB+D (younger adults) and ETAP-DID (older adults). We analyze the impact of different joint subset configurations (full-body, limb-only, leg-only, and torso-only) and compare raw joint representations with handcrafted time-series features (TSFEL) for frame-based HAR. Beyond activity recognition, we introduce an AGR pipeline to distinguish younger from older adults based on skeletal motion patterns. We investigate multiple feature representations, including absolute joint positions, root-relative coordinates, bone vectors, and joint velocities, and provide interpretability through feature importance and saliency analysis to identify age-discriminative joints and motion cues. Our study provides a comprehensive analysis of various HAR models applied to depth data, examining model performance and the contribution of joint-based features to HAR and AGR. Our study highlights the potential for personalized privacy-preserved monitoring and intervention in nursing homes.
R. Paul, Alp Göktug Tanman, Yale Hartmann et al.· Italian National Conference...· 0 citations
A deep learning–based HAR framework utilizing hip-mounted accelerometer and gyroscope signals from the USC-HAD dataset, which contains readings from healthy participants only, is evaluated, providing a more realistic assessment of subject-independent generalization across unseen individuals.
F. Naveed, Hamza Khan, Zaki Uddin et al.· Scientific Reports· 0 citations
Mobile health has become a popular option for patients to monitor and analyze their body activities and vital signs using their mobile devices, such as smartphones and smartwatches. In addition, the healthcare community has begun using artificial intelligence (AI) models to automate the diagnosis of abnormal conditions and diseases, particularly in real‐time emergency scenarios. This article introduces a new deep learning model to automatically identify daily body activities. We propose a hybrid AI model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU) for robust human activity classification. Key contributions include (1) the design of an end‐to‐end CNN‐GRU model that jointly extracts local spatiotemporal features and models long‐term temporal dependencies from raw sensor data, and (2) a comprehensive benchmarking framework validating the developed model against previous machine learning and deep learning classifiers. A public mobile health dataset (MHEALTH) has been used in this study. This dataset includes 12 physical activities, for example, knee bending, walking, and running. Key findings demonstrate that the CNN‐GRU model achieves a state‐of‐the‐art classification accuracy of 99.50% on the publicly available MHEALTH dataset, which encompasses 12 distinct physical activities. It significantly outperforms CNN‐LSTM (98.83%), 1‐D CNN (96.89%), and traditional ensemble methods while maintaining high precision, recall, and F1‐scores across all activity classes. Therefore, our developed model can be implemented in a cloud computing system to monitor senior patients as a critical healthcare application.
Raed Alotaibi, O. Reyad, M. E. Karar· International Journal of Tel...· 0 citations
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