Abstract
Automated daily living activity recognition is a relevant task since it allows to assess the health status of a subject both objectively and remotely. Having a reliable measure is important since it gives precise indications to doctors and researchers interested in evaluating the effectiveness of treatments or drugs (e.g., in the context of clinical studies). The possibility to perform this task remotely is more convenient for the patients and acquired increasing importance not only due to the current pandemic, but also because of the regularly growing population of elderly people that could benefit from remote monitoring.
In this paper, first, we describe a novel wearable-device-based dataset that contains data (1) of a high number of daily life activities, coming from a real-life scenario, (2) recorded by applying multiple devices on different parts of the body, and (3) recorded with medical-grade devices at a high sampling frequency. Then, second, we describe a machine learning-based method for activity recognition. Our approach takes in input a dataset and through multiple phases allows to recognise the activities performed by the subjects with a good degree of accuracy (up to 0.92 expressed as F1 score depending on the location).
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Notes
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ActiGraph, LLC (Pensacola, FL, USA), https://actigraphcorp.com/.
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Regarding the possible variation of the subjects behaviour while performing activities by knowing they were participating in an experiment (also known as Hawthorne effect), it is important to note that (1) there have not been judgements on how well subjects were performing activities, therefore they could behave in any way they preferred, (2) due to the short amount of time spent in recording data, possible variations in subjects behaviour happened in the entire recording (i.e. no effect on possible train and test data sets).
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“WHO: How to handwash? With soap and water”, https://www.youtube.com/watch?v=3PmVJQUCm4E.
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Actigraph GT9X Link - https://actigraphcorp.com/actigraph-link/, Actigraph Centrepoint Insight Watch - https://actigraphcorp.com/cpiw/.
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Acknowledgement
This work is an activity of the Software Engineering for Healthcare Lab, a joint laboratory between the University of Genova and Janssen Pharmaceuticals (Johnson & Johnson group). We want to show our gratitude to Massimo Raineri e Giacomo Ricca of Janssen Pharmaceuticals for the support provided.
Disclaimer. Responsibility for any information and result reported in this paper lies entirely with the Authors. Janssen Pharmaceuticals was not involved in any form of data collection or analysis.
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Leotta, M., Fasciglione, A., Verri, A. (2021). Daily Living Activity Recognition Using Wearable Devices: A Features-Rich Dataset and a Novel Approach. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12662. Springer, Cham. https://doi.org/10.1007/978-3-030-68790-8_15
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