Human Activity Vibrations

Sakdirat Kaewunruen, Jessada Sresakoolchai, Junhui Huang, Satoru Harada and Wisinee Wisetjindawat
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Sakdirat Kaewunruen: School of Engineering, University of Birmingham, Birmingham B15 2TT, UK
Jessada Sresakoolchai: School of Engineering, University of Birmingham, Birmingham B15 2TT, UK
Junhui Huang: School of Engineering, University of Birmingham, Birmingham B15 2TT, UK
Satoru Harada: Hitachi Europe Limited, ERD Office, 12th Floor, 125 London Wall, London EC2Y 5AL, UK
Wisinee Wisetjindawat: Hitachi Europe Limited, ERD Office, 12th Floor, 125 London Wall, London EC2Y 5AL, UK

Data, 2021, vol. 6, issue 10, 1-9

Abstract: We present a unique, comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus, or a taxi. The measurements are carried out by embedded sensor accelerometers in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they perform the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertically stored in an Excel Macro-enabled Workbook (xlsm) format that can be used to train an AI model in a smartphone which has the potential to collect people’s vibration data and decide what movement is being conducted. Moreover, with more data received, the database can be updated and used to train the model with a larger dataset. The prevalence of the smartphone opens the door to crowdsensing, which leads to the pattern of people taking public transport being understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transport, services and schedules can be planned perceptively.

Keywords: human activity; smartphone; accelerometer (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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