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A Novel Edge Analytics Assisted Motor Movement Recognition Framework Using Multi-Stage Convo-GRU Model

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Abstract

Current advancement in Internet-of-Things, Cyber-Physical Systems, Cloud-of-Things, and Edge-of-Things technologies have enabled us to design more advanced and event-sensitive real-time monitoring solutions. IoT-assisted healthcare systems need local data processing environment for effective decision making. In the proposed study, a novel edge analytics-assisted monitoring solution is proposed to monitor several physical activities of the patient to determine physical inactivity from their daily routine. Wearable sensors are utilized to monitor physical movements. The main objective of the proposed study is to calculate the scale of the physical inactivity of the patient to make real-time health suggestions. Graphical Processing Unit (GPU) enabled edge nodes are utilized for efficient data processing. An application scenario is proposed to validate the ideology of the proposed system in the healthcare environment. The performance is compared with both machine learning and deep learning-based approaches to justify the proposed system. iFogSim simulator is utilized to simulate the proposed scenario and the performance is validated based on the computation of movement recognition efficiency, network bandwidth efficiency, interoperability, Edge-based data processing reliability and alert generation-based patient security.

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  1. Lovely Professional University, Jalandhar, 144411, Punjab, India

    Ankush Manocha & Ramandeep Singh

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  1. Ankush Manocha
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  2. Ramandeep Singh
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Correspondence to Ramandeep Singh.

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Manocha, A., Singh, R. A Novel Edge Analytics Assisted Motor Movement Recognition Framework Using Multi-Stage Convo-GRU Model. Mobile Netw Appl 27, 657–676 (2022). https://doi.org/10.1007/s11036-019-01321-8

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