Internet of things centric based multi-activity recognition in smart home environment

In recent times, numerous human activity recognition schemes have been proposed with embedding sensors, wearable devices, smart phones, vision and ambient sensors. Though the systems have shown better performance they are mostly standalone and still lack the ability to share, host and perform real-time analysis and visualization of activity data. The Internet of Things (IoT) paradigm has a solution to render the limitations and this will pave the way for HAR in the smart home environment. Thus in this paper, an Internet of Things (IoT) centric multi-activity recognition system is proposed and deployed on the cloud platform for activity data tracking in the smart home environment. The proposed system collects the real-time data collected using IMU sensors and transmitted to IoT-Edge server via Wi-Fi where the data has been fused and classified using light-weight deep learning models. This system has a provision of a web-based dashboard which is helpful for the home dwellers to monitor the activities in the remote. The performance evaluation justified that the developed system can measure IoT-based activity recognition with greater efficiency in terms of accuracy and F1-score in a shorter response time as of deployment in cloud platform to detect the activity.

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