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An Efficient and Innovative IoT-Based Intelligent Real-Time Staff Assessment Wearable

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Advances in Computing and Network Communications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 735))

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Abstract

The traditional method for assessing the performance of the workers in industrial workspaces is by measuring their inertial movements. Presently in industries at the workspaces, every 20–30 workers will have an in-charge to monitor their work. Although workers are being assessed by those in-charges, there are still many mishaps happening in the industrial workspaces. Workers with poor work performance are earning the wages that are same as that of a worker with good performance. These expenditures may seem to be minimal in number, but recent statistics reveal that this affects the company's total productivity in a disastrous way. Some studies state that on an average, companies are losing $3,156 on the workers due to their idleness (Duffy J, Productivity report|bridging research and practice on personal productivity). Forbes magazines revealed that 31% of the workers are roughly wasting 1 h of time per day at their work apart from their allotted leisure times (Wasting time at work: the epidemic continues—the fobs report). Many companies in the UK with industrial workspaces claim that they lost 15.4 billion dollars annually only due to worker illness and their maintenance (Number of workplace injury and work-related ill health cases|Page 8—HSE, UK report). However, unfortunately, besides having distinct inertial measurement unit (IMU) systems in place, companies are facing many difficulties in identifying and estimating the worker performances and the health of the worker. Therefore, we have developed a system for overcoming the difficulties faced by using these IMUs using the power of IoT and Android application.

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Correspondence to Shriram K. Vasudevan .

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Anudeep, J., Vasudevan, S.K., Kowshik, G., Vineeth, C., Nair, P.R. (2021). An Efficient and Innovative IoT-Based Intelligent Real-Time Staff Assessment Wearable. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 735. Springer, Singapore. https://doi.org/10.1007/978-981-33-6977-1_33

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  • DOI: https://doi.org/10.1007/978-981-33-6977-1_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6976-4

  • Online ISBN: 978-981-33-6977-1

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