2018· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
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.
Abstract
Wearable sensor-based Human Activity Recognition (HAR) has emerged as a key area in pervasive computing, healthcare monitoring, and smart environments. With the advancement of low-cost, energy-efficient sensors such as accelerometers and gyroscopes, continuous human motion tracking has become more feasible. Traditional HAR systems relied on manual feature extraction and classical machine learning models like SVM, Decision Trees, and k-NN, but faced challenges such as noise, variability, and computational constraints. This study reviews pre-2018 developments and proposes an improved HAR framework incorporating advanced feature engineering, sensor fusion, and ensemble learning techniques. The system follows key stages including data acquisition, preprocessing, segmentation, feature extraction, and classification. By combining multi-sensor data and hybrid models, the framework enhances classification accuracy and robustness. Evaluation using benchmark datasets like UCI HAR and WISDM demonstrates improved performance over conventional methods. The paper also highlights key challenges such as energy efficiency, scalability, real-time processing, and privacy, while emphasizing the future role of deep learning and adaptive systems for personalized activity recognition. Overall, wearable sensor-based HAR shows strong potential in healthcare, fitness, and smart environments.
Human Activity Recognition (HAR) has become one of the essential research areas due to the blistering development of wearable sensory devices, smartphones or the Internet of Things (IoT). HAR aims at recognizing human physical actions like walking, sitting, standing, running and lying down automatically based on the data acquired by motion sensors and physiological sensors. The ability to scale and flexibility have gradually seen the replacement of traditional rule-based systems by data-driven systems. Machine learning models and deep learning provide data science approaches that can robustly extract features, classify, and infer in real time using complicated sensor signal data streams. In the current paper, the use of data science methods in HAR is evaluated and summed up in detail. It examines data collection techniques, preprocessing techniques, feature engineering techniques and classification models. Moreover, it reviews benchmark datasets and assessment measures that are prevalent in HAR studies. The HAR methodology based on data science pipelines is offered and tested on the example of standard datasets. Findings have shown that novel machine learning and deep learning neural networks are much more effective in recognition accuracy than the classical methods of statistics. The main issues that have been identified by the study include sensor noise, user variability and computational constraints and future prospects of the study is given on context-aware systems and edge intelligence. The results lead to the realization of efficient HAR to track healthcare, intelligent environments, and human-computer interface.
Zainab Abdullahi· International Journal of App...· 0 citations
Automatic Human activity Recognition has many applications in smart environments such as in smart homes, smart cities, smart industries, smart healthcare centers, etc. While performing the activities by the participants, ambient or body-worn sensors can measure physical movements and those data can be used to develop machine learning models for recognizing those activities. In this study, we have proposed 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. The quality data is produced and balanced using a preprocessing chain for human activity recognition based on data analysis. The Preprocessed data is segmented using a constant-size sliding window. We developed several different DCNN models using random searches and based on validation accuracy we selected the best one for further training and testing. The outcomes of the selected model are carried out as the final predicted activities. We assessed our method on three popular and standard datasets: PAMAP2, WISDM_ar_v1.1, and UCI-HAR, and achieved 98.11%, 98.48%, and 93.25% accuracies for subject-dependent case and 90.27%, 94.51%, and 98.67% accuracies for subject-independent case. The performances of the experimental results are measured using several evaluation metrics and measures that institute the strength of the proposed model over the state-of-the-art.
S. Islam, Kamrul Hasan Talukder· International Journal of Int...· 0 citations
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.
A. Alzurfi, Rabi Noori Hammudi, Basma Salim Bazel Albrge et al.· Internet Technology Letters· 0 citations
The Human Activity Recognition (HAR) methodology serves essential functions within healthcare settings as well as sports disciplines and applications in both human-computer communication and smart system environments. This paper presents invasive human activity recognition based on smart phone datasets. In this study, we used accelerometer and gyroscope data to train the proposed SVM and Random Forest for human activity recognition. The Human Activity Recognition with smartphone public dataset is utilized to estimate the accuracy, F1 score, precision, recall and visualize confusion matrix of the proposed HAR model. The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The experiments have been carried out with a group of 30 volunteers within an age range of 19-48 years. Each person performed six activities (walking, walking upstairs, walking downstairs, sitting, standing, laying) wearing a smartphone (Samsung galaxy s ii) on the waist. Training SVM with RBF kernel confusion matrix with Accuracy: 0.9884, F1 Score: 0.9884, Recall: 0.9884 and Precision: 0.9885Training Random Forest, confusion matrix with Accuracy: 0.9742, F1 Score: 0.9741, Recall: 0.9742 and Precision: 0.9745 was achieved.
Unknown authors· JSE Journal of Science and E...· 0 citations
Discerning user activities with wearable devices is important to monitor people's behavior and even for developing personalized strategies to prevent some diseases, which has significant implications for healthcare systems. The metabolic equivalent of the task (MET), a measure of the energy cost of physical activities, is a good indicator to discern different activities. However, there is no established formula for calculating the MET. Some works have used several features to approximate the MET, such as the filtered acceleration (ACCfil) or the percentage of the heart rate reserve (%HRR). However, none of them have studied the importance of each of these features in the accuracy of activity classification. This study investigates the impact of different preprocessing techniques on activity recognition accuracy, such as filtering the acceleration to obtain the feature ACCfil. To that end, we performed several experiments with different features and machine learning algorithms to detect the activities. Our results indicate that incorporating features such as ACCfil, HRR, and ratio of unfiltered to filtered acceleration (RUF) improves activity recognition accuracy in some cases up to 166.67%. These features are particularly useful for challenging activities such as stair climbing or household activities. These findings, with their direct implications for developing more accurate activity recognition models with wearable devices, underscore the importance of preprocessing techniques and feature integration.
Unknown authors· International Journal of Int...· 0 citations
Multimodal sensing and data fusion using artificial intelligence have transformed wearable health technologies by integrating various physiological signals for continuous health monitoring. This study provides a comprehensive perspective on wearable technologies, such as fusion hierarchies, machine learning interpretation, and deployment in wearable body area networks. The research offers a performance assessment and structural issues affecting reliability, including sensor diversity, dominance, noise, and intermittent data loss due to motion artifacts and dropouts. As a result, it explores multimodal fusion failure modes, demonstrating how asynchronous failure and partial observability can cause instability in multimodal representations. The study also highlights the transition from continuous to event- and window-based processing to improve energy, computational, and clinical efficiency in edge computing. The study explores emerging approaches like self-supervised learning, multimodal foundation models, and digital twin-based personalized health models for enhancing robustness and generalization. The research also considers advances in functional materials, such as mechanochromic materials and biointegrated sensing platforms, to enable intuitive and seamless physiological monitoring. Finally, the work considers the regulatory, energy, and system constraints on these innovations and notes that future wearable systems must combine computational intelligence with physical form factors, interpretability, and safety, with robustness and adaptability being the guiding design principles.
Received: 20 March 2026 | Revised: 22 May 2026 | Accepted: 1 July 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Author Contribution Statement
Najeem Olawale Adelakun: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Matthew Babatunde Olajide: Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Supervision, Project administration. Samuel Adeniyi Omolola: Formal analysis, Investigation, Resources, Data curation, Project administration.
N. O. Adelakun, M. Olajide, S. A. Omolola· Smart Wearable Technology· 0 citations