article · Scientific Reports
Monitoring physical and biological activity in healthcare increasingly relies on wireless body area networks and wearable sensors, bypassing camera-related challenges such as background interference and visual obstruction. A new human activity recognition system processes sensor signals by first applying artifact removal and median filtering. The filtered time-series data is then converted into two-dimensional images using the Gramian angular field algorithm. Following this image transformation, a DenseNet architecture automatically processes and integrates data collected across diverse body sensors. Experimental testing demonstrates strong performance, with the model attaining an accuracy of 97.83 per cent, an F-measure of 97.83 per cent, and a Matthews correlation coefficient of 97.64. By turning raw sensor readings into structured visual representations, the pipeline supports automated, privacy-preserving tracking of patient movements.
Accurate tracking of patient movement is vital for healthcare monitoring, yet video cameras raise privacy concerns and suffer from physical obstructions. Wearable sensor networks provide a discreet alternative. Converting complex bodily signals into images for deep learning enables precise, automated activity detection while preserving patient privacy, which can assist medical staff in continuously evaluating patient physical status.
This technology could support wearable health monitors, remote patient tracking platforms, and clinical assessment tools that require non-invasive, continuous activity recognition. Intended users include digital health developers, care providers, and medical device manufacturers. The research remains at an early-stage, experimental phase, having validated the algorithmic pipeline on sensor data without demonstrating deployment on physical wearable hardware or in live clinical trials.
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In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).
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DOI: 10.1038/s41598-024-53069-1
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