Aug 2026· International Journal of Intelligent Systems and Applications· 0 citations· 57 references
TL;DR
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.
Abstract
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.
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.
Zainab Abdullahi· International Journal of App...· 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
This study used accelerometer and gyroscope data to train the proposed SVM and Random Forest for human activity recognition and confirmed the accuracy, F1 score, precision, recall and visualize confusion matrix of the proposed HAR model.
Taiwo Samuel Aina, B. Iyaomolere· JSE Journal of Science and E...· 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
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
Tracking everyday activities is vital for detecting changes in older adults’ health, allowing timely support to promote well-being. Wearable sensors and deep learning provide continuous monitoring, making them a supportive tool in detecting such changes. However, a more refined method is needed to recognise precise activities with a minimal set of sensors. This study aimed to develop a method to recognise everyday activities among older adults by utilising wearable sensors and a deep learning model. This is a small-scale home lab experiment to develop a method to recognise 14 everyday activities. We compared five models that recognised everyday activities with different sensor signal counts and accuracy. Our results showed that sensor placement is important. Based on the results, we proposed a two-sensor method (pelvis and right hand) to collect and correctly recognise everyday activities among older adults. This model, which utilises two sensors, classified 12 activities with an accuracy of 89.3%. Another model recognised all 14 activities with a lower accuracy of 88.2% using five sensors. We also explored a one-sensor approach, which showed low recognition performance and struggled to distinguish activity variability. The two-sensor-based system will allow for large-scale data collection on everyday activities of older adults.
FrameworkWilliam Son Galanza, Steven M Schmidt, S. Fristedt et al.· Scientific Reports· 0 citations
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