Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1455-1460· 0 citations· 28 references
Abstract
Human Activity Recognition (HAR) is a fast-growing research area that focuses on identifying human actions using data collected from sensors and vision-based devices. It plays an important role in applications like health monitoring, smart homes, surveillance, sports analysis, and human-computer interaction. In recent years, several methods have been developed to improve the performance of HAR systems using machine learning, deep learning, and hybrid models. This paper presents a detailed review of different methods used in HAR. The study is divided into three main categories: vision-based methods, sensor-based methods, and hybrid approaches that combine both types. Each method is discussed with examples from recent research, along with their advantages and limitations. A comparison is also provided in the form of a table to highlight the performance and challenges of each approach. Although HAR systems have achieved good results in controlled environments, several challenges still remain. These include poor generalization to new users or unknown environments, difficulty in recognizing complex or overlapping activities, dependence on large datasets, and lack of real-time performance. This paper also discusses these research gaps based on recent findings. The future of HAR depends on building more accurate, reliable, and real-time systems that can adapt to different situations. The paper concludes by suggesting possible directions for future work, such as the development of lightweight models, use of standard datasets, better handling of real-time data, and making models more interpretable.
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
Human action recognition (HAR) plays a crucial role in safety monitoring, intelligent surveillance systems, and human-computer interaction applications. In this study, we evaluate and compare several deep learning architectures for HAR using the Weizmann dataset, using a YOLO-based preprocessing, including CNN, CNN with attention mechanism, MobileNetV2, and InceptionV3. The proposed YOLO-based preprocessing method was specifically designed to enhance feature extraction efficiency by isolating human subjects from background clutter, thereby reducing noise and improving spatial focus. Experimental results demonstrate that the YOLO-based CNN achieved state-of-the-art performance with an accuracy of 99.6%, significantly outperforming the CNN-Attention model (98.6%), MobileNetV2 (96.1%), and InceptionV3 (93.7%). These findings underscore the importance of robust preprocessing techniques and highlight the superiority of the proposed YOLO-based method in handling complex real-world scenarios.
M. Faris, Al Hakim, Regina Ayumi Ulayyaa et al.· 2026 7th International Confe...· 0 citations
The article compares the performance of traditional machine learning techniques with recent deep learning architectures such as CNNs, RNNs, TCNs, and Transformers, based on accuracy, computational cost, and suitability for real-world disorderly plotting.
Disha Deotale, M. Verma, P. Suresh et al.· Discover Artificial Intellig...· 0 citations
Human activity recognition (HAR) is the identification of daily human activities using wearable sensor data. In this study, we evaluate 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. The proposed pipeline integrates convolutional feature extraction, bidirectional long short-term memory modeling, and an additive attention mechanism to capture temporal dependencies in the sensor data. The model is evaluated using performance matrices and leave-one-subject-out cross-validation (LOSO-CV) to assess subject-independent generalization. Performance is reported using accuracy, precision, recall, F1-score, and 95% confidence intervals, and statistical significance testing. Our experimental results show that under subject-exclusive splitting, the proposed model achieves 98% accuracy. Under strict LOSO-CV, the model achieves a performance of 78% ± 0.1130, providing a more realistic assessment of subject-independent generalization across unseen individuals. The dataset does not include clinical or patient populations. The findings are limited to non-clinical settings and should be interpreted within this scope. The results primarily contribute methodological insights into wearable-based HAR systems. The potential of this work for healthcare applications is discussed as a direction for future research, subject to validation on clinically representative datasets.
F. Naveed, Hamza Khan, Zaki Uddin et al.· Scientific Reports· 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
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