Wearable Internet-of-Things platform for human activity recognition and health care

Asif Iqbal, Farman Ullah, Hafeez Anwar, Ata Ur Rehman, Kiran Shah, Ayesha Baig, Sajid Ali, Sangjo Yoo and Kyung Sup Kwak

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 6, 1550147720911561

Abstract: We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

Keywords: Wearable Internet-of-Things; activity recognition; health and well-being; accelerometer; gyroscope; magnetometer; temperature sensor; pressure sensor (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:6:p:1550147720911561

DOI: 10.1177/1550147720911561

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