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Using Deep Learning to Identify Persons by their Movement on a Sensor Floor

Published: 11 October 2023 Publication History

Abstract

We present an approach to identify persons based on their movement on a sensor floor. Three types of deep learning neural networks were trained on five subjects’ sensor data collected during ordinary working days in a test room. A Transformer network architecture proved to be the most successful, achieving a recognition rate of over 90% in the task of assigning just one minute of movement data to the correct person. Since the sensor floor can be installed invisibly under normal flooring, the findings result in new applications, e.g. for security systems or in the early detection of health problems that are reflected in the gait pattern.

References

[1]
[1] 2018. https://future-shape.com
[2]
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. 2000. Learning to forget: Continual prediction with LSTM. Neural computation 12, 10 (2000), 2451–2471.
[3]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
[4]
Raoul Hoffmann, Christl Lauterbach, Jörg Conradt, and Axel Steinhage. 2018. Estimating a person’s age from walking over a sensor floor. Computers in Biology and Medicine 95 (2018), 271–276.
[5]
Raoul Hoffmann, Christl Lauterbach, Axel Techmer, Jörg Conradt, and Axel Steinhage. 2016. Recognising gait patterns of people in risk of falling with a multi-layer perceptron. In Information Technologies in Medicine: 5th International Conference, ITIB 2016 Kamień Śląski, Poland, June 20-22, 2016 Proceedings, Volume 2. Springer, 87–97.
[6]
Raoul Hoffmann, A Steinhage, and C Lauterbach. 2015. Increasing the reliability of applications in AAL by distinguishing moving persons from pets by means of a sensor floor. In Proceedings SENSOR. 436–440.
[7]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[8]
Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Lukas Köping, and Marcin Grzegorzek. 2018. Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors 18, 2 (2018), 679.
[9]
Laura Liebenow. 2021. Identifying Individuals using Sensor Floor Data and Probabilistic Classification. Master’s thesis. Universität zu Lübeck, Germany, Lübeck.
[10]
Laura Liebenow, Jasmin Walter, Raoul Hoffmann, Axel Steinhage, and Marcin Grzegorzek. 2022. Classifying Changes in Motion Behaviour Due to a Hospital Stay Using Floor Sensor Data–A Single Case Study. In Information Technology in Biomedicine: 9th International Conference, ITIB 2022 Kamień Śląski, Poland, June 20–22, 2022 Proceedings. Springer, 3–14.
[11]
Axel Steinhage and Christl Lauterbach. 2011. SensFloor and NaviFloor: Large-Area Sensor Systems Beneath Your Feet. In Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives, Nak-Young Chong and Fulvio Mastrogiovanni (Eds.). IGI Global, Hershey, PA, USA, 41–55.
[12]
Miika Valtonen, Jaakko Mäentausta, and Jukka Vanhala. 2009. Tiletrack: Capacitive human tracking using floor tiles. In 7th Annual IEEE international conference on pervasive computing and communications (PerCom 2009). IEEE, Galveston, TX, USA, 1–10.
[13]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).

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iWOAR '23: Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence
September 2023
171 pages
ISBN:9798400708169
DOI:10.1145/3615834
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 October 2023

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Author Tags

  1. Identification
  2. LSTM
  3. Movement
  4. Neural Network
  5. Sensor floor
  6. Transformer
  7. Walk

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iWOAR 2023

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Overall Acceptance Rate 46 of 73 submissions, 63%

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