Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 6 Articles
Human Activity Recognition (HAR) using wearable sensors is an increasingly relevant area for applications in healthcare, rehabilitation, and human–computer interaction. However, publicly available datasets that provide multi-sensor, synchronized data combining inertial and orientation measurements are still limited. This work introduces a publicly available dataset for Human Activity Recognition, captured using wearable sensors placed on the chest, hands, and knees. Each device recorded inertial and orientation data during controlled activity sessions involving participants aged 20 to 70. A standardized acquisition protocol ensured consistent temporal alignment across all signals. The dataset was preprocessed and segmented using a sliding window approach. An initial baseline classification experiment, employing a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) model, demonstrated an average accuracy of 93.5% in classifying activities. The dataset is publicly available in CSV format and includes raw sensor signals, activity labels, and metadata. This dataset offers a valuable resource for evaluating machine learning models, studying distributed HAR approaches, and developing robust activity recognition pipelines utilizing wearable technologies....
Environmental monitoring and early disaster prediction require sensor networks that can dynamically reconfigure their operation based on environmental conditions and potential threats. Moving beyond traditional management requires autonomous and adaptive control systems with the ability for intelligent decision-making at the network edge. This paper presents an intelligent agent-based system for autonomous control and optimization of large-scale, distributed electronic sensor networks used for environmental monitoring and disaster prediction. The approach aims at promoting the accuracy and timeliness of disaster prediction by using sensor characteristics knowledge, environmental processes, and network control protocols. The paper presents the architecture and decision-making with potential applications....
In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure distributed web system architecture that integrates real-time sensor data with a custom customer relationship management (CRM) module to optimize patient monitoring and clinical decision-making. The architecture leverages IoT-enabled medical sensors to capture physiological signals, which are transmitted through secure communication channels and stored in a modular EHR system. Security mechanisms such as data encryption, role-based access control, and distributed authentication are embedded to address threats related to unauthorized access and data breaches. The CRM system enables personalized healthcare management while respecting strict privacy constraints defined by current healthcare standards. Experimental simulations validate the scalability, latency, and data protection performance of the proposed system. The results confirm the potential of combining CRM, sensor data, and distributed technologies to enhance healthcare delivery while ensuring privacy and security compliance....
This paper introduces a novel sensor fusion approach for the detection of multiple static and mobile targets by autonomous mobile agents. Unlike previous studies that rely on theoretical sensor models, which are considered as independent, the proposed methodology leverages real-world sensor data, which is transformed into sensor-specific probability maps using object detection estimation for optical data and converting averaged point-cloud intensities for LIDAR based on a dedicated deep learning model before being integrated through a large language model (LLM) framework. We introduce a methodology based on LLM transfer learning (LLM-TLFT) to create a robust global probability map enabling efficient swarm management and target detection in challenging environments. The paper focuses on real data obtained from two types of sensors, light detection and ranging (LIDAR) sensors and optical sensors, and it demonstrates significant improvement in performance compared to existing methods (Independent Opinion Pool, CNN, GPT-2 with deep transfer learning) in terms of precision, recall, and computational efficiency, particularly in scenarios with high noise and sensor imperfections. The significant advantage of the proposed approach is the possibility to interpret a dependency between different sensors. In addition, a model compression using knowledge-based distillation was performed (distilled TLFT), which yielded satisfactory results for the deployment of the proposed approach to edge devices....
Prolonged poor sitting posture increases the risk of musculoskeletal disorders and chronic diseases. We developed a smart cushion system that integrated pressure sensing and machine learning for posture recognition. Nine FSR406 sensors were used to measure pressure distribution on the system. A calibration and normalization process improves data consistency, and a heatmap visualizes the result. Among the five machine learning models evaluated, the narrow neural network achieved the best performance, with a validation accuracy of 97.63% and a test accuracy of 91.73%. When body mass index (BMI) was included as an additional input feature, the test accuracy improved to 95.49%, indicating that BMI positively impacts recognition performance....
Wireless sensor networks (WSNs) consist of distributed sensor nodes deployed for realtime monitoring and data collection. Optimizing sensor energy consumption is critical for extending the overall network lifespan. In large-scale WSNs, clustering techniques are required to reduce energy consumption. Many effective clustering methods have been proposed, but finding the optimal number of clusters in an energy-efficient manner remains challenging. Swarm intelligence (SI) algorithms help solve this problem, but testing all possible cluster configurations is computationally expensive. Neural networks excel in identifying hidden patterns in data, making them a promising tool for this task. However, training an AI agent to accurately predict both the number of cluster heads (CHs) and their locations is difficult. In this study, we developed a synergic method by employing a reinforcement learning (RL) model to predict the number of CHs while utilizing an SI algorithm to identify the most appropriate nodes to become CHs. This approach minimizes transmission energy and prolongs the lifespan of WSNs and their services....
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