Because to smart technology and the Internet of Things (IoT), previously stationary items such as cars and walls, as well as previously movable objects such as phones and watches, can now contain sensors. partly because of privacy concerns about cameras in intimate areas Sensors benefit from its pervasiveness. Sensor-based gadgets are used to track human behaviour. On-body or ambient sensors can be used to capture the deceased's movement patterns and past activity. The algorithm recognises human behaviours automatically 6. An successful HAR system can identify patients in medical emergencies, provide them with the care they require, and keep an eye on them to promote good behaviour 5. It enables computers to aid people in medical, rehabilitative, creative, sports, and other domains 3, 4. "Activity recognition" relates to how people use their senses to identify and classify various types of activities 2. Human Activity Recognition (HAR) has grown in popularity as a study issue over the last two decades 1. "Activity" refers to changes in the position of the body or limbs over time and against gravity. From these experiments, the results can be obtained and showed the efficacy of the proposed model. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. The important features are then post-processed using the scatter plot matrix method. After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). At first, the input data is pre-processed. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities.
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