Abstract
Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.
Highlights
Wireless sensing is the state-of-the-art technique for generation health activity monitoring
Whilst the second set was conducted to measure the system’s accuracy in identifying different postures/activities of multiple people in the same room. Both types of experiments were performed under a train-and-test split strategy with 80% of the random data was considered as training data while the remaining 20% was taken as the testing data
The results presented earlier in the paper have shown that by combining Radio Frequency (RF)-sensing technology with standard machine learning algorithms such as Convolutional Neural Network (CNN), it is possible to detect different human activities including counting the number of people in the room with high accuracy
Summary
Wireless sensing is the state-of-the-art technique for generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Human Activity Recognition (HAR) has received increasing attention due to numerous applications in fields such as security, health care and smart utilisation of resources, but has not been introduced with 5G-based technologies. HAR and classification brings numerous benefits to elderly c are[2,3] and is a basic key in providing higher-level appropriate information such as positions, activities, occupancy, and identities in an indoor environment that can be useful for health care, source utilisation, security, and in energy saving. Between occupancy and levels of energy c onsumption[10], which makes a compelling case for occupancy monitoring systems and their positive impact on the environment
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