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Open-Source Data Collection for Activity Studies at Scale

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Sensor- and Video-Based Activity and Behavior Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 291))

Abstract

Activity studies range from detecting key indicators such as steps, active minutes, or sedentary bouts, to the recognition of physical activities such as specific fitness exercises. Such types of activity recognition rely on large amounts of data from multiple persons, especially with deep learning. However, current benchmark datasets rarely have more than a dozen participants. Once wearable devices are phased out, closed algorithms that operate on the sensor data are hard to reproduce and devices supply raw data. We present an open-source and cost-effective framework that is able to capture daily activities and routines and which uses publicly available algorithms, while avoiding any device-specific implementations. In a feasibility study, we were able to test our system in production mode. For this purpose, we distributed the Bangle.js smartwatch as well as our app to 12 study participants, who started the watches at a time of individual choice every day. The collected data was then transferred to the server at the end of each day.

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Funding

This publication is part of the project ActiVAtE\(\_\)prevention which is funded by the Ministry for Science and Culture of the federal state of Lower Saxony in Germany (VW-ZN3426).

Author information

Authors and Affiliations

  1. Ubiquitous Computing, University of Siegen, Hoelderlinstr. 3, 57076, Siegen, Germany

    Alexander Hoelzemann & Kristof Van Laerhoven

  2. Institute of Sports Science, University of Vechta, Driverstr. 22, 49377, Vechta, Germany

    Jana Sabrina Pithan

Authors
  1. Alexander Hoelzemann
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  2. Jana Sabrina Pithan
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  3. Kristof Van Laerhoven
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Corresponding author

Correspondence to Alexander Hoelzemann .

Editor information

Editors and Affiliations

  1. University of East London, London, UK

    Md Atiqur Rahman Ahad

  2. Kyushu Institute of Technology, Fukuoka, Japan

    Sozo Inoue

  3. Sussex University, Brighton, UK

    Daniel Roggen

  4. Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan

    Kaori Fujinami

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Hoelzemann, A., Pithan, J.S., Van Laerhoven, K. (2022). Open-Source Data Collection for Activity Studies at Scale. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Sensor- and Video-Based Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-19-0361-8_2

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