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Improved Coyote Optimization Algorithm and Deep Learning Driven Activity Recognition in Healthcare
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DOI：10.1109/ACCESS.2024.3357989.png)
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Abstract
En 中文
Healthcare is an area of concern where the application of human-centred design practices and principles can enormously affect well-being and patient care. The provision of high-quality healthcare services requires a deep understanding of patients' needs, experiences, and preferences. Human activity recognition (HAR) is paramount in healthcare monitoring by using machine learning (ML), sensor data, and artificial intelligence (AI) to track and discern individuals' behaviours and physical movements. This technology allows healthcare professionals to remotely monitor patients, thereby ensuring they adhere to prescribed rehabilitation or exercise routines, and identify falls or anomalies, improving overall care and safety of the patient. HAR for healthcare monitoring, driven by deep learning (DL) algorithms, leverages neural networks and large quantities of sensor information to autonomously and accurately detect and track patients' behaviors and physical activities. DL-based HAR provides a cutting-edge solution for healthcare professionals to provide precise and more proactive interventions, reducing the burden on healthcare systems and improving patient well-being while increasing the overall quality of care. Therefore, the study presents an improved coyote optimization algorithm with a deep learning-assisted HAR (ICOADL-HAR) approach for healthcare monitoring. The purpose of the ICOADL-HAR technique is to analyze the sensor information of the patients to determine the different kinds of activities. In the primary stage, the ICOADL-HAR model allows a data normalization process using the Z-score approach. For activity recognition, the ICOADL-HAR technique employs an attention-based long short-term memory (ALSTM) model. Finally, the hyperparameter tuning of the ALSTM model can be performed by using ICOA. The stimulation validation of the ICOADL-HAR model takes place using benchmark HAR datasets. The wide-ranging comparison analysis highlighted the improved recognition rate of the ICOADL-HAR method compared to other existing HAR approaches in terms of various measures.
Keywords:
Human activity recognition
hyperparameter tuning
coyote optimization algorithm
wearable sensor
deep learning
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3.6
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9.8W
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29.4W
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