Open access
Date
2023-02-28Type
- Journal Article
ETH Bibliography
yes
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Abstract
The emerging field of digital phenotyping leverages the numerous sensors embedded in a smartphone to better understand its user's current psychological state and behavior, enabling improved health support systems for patients. As part of this work, a common task is to use the smartphone accelerometer to automatically recognize or classify the behavior of the user, known as human activity recognition (HAR). In this article, we present a deep learning method using the Resnet architecture to implement HAR using the popular UniMiB-SHAR public dataset, containing 11,771 measurement segments from 30 users ranging in age between 18 and 60 years. We present a unified deep learning approach based on a Resnet architecture that consistently exceeds the state-of-the-art accuracy and F1-score across all classification tasks and evaluation methods mentioned in the literature. The most notable increase we disclose regards the leave-one-subject-out evaluation, known as the most rigorous evaluation method, where we push the state-of-the-art accuracy from 78.24 to 80.09% and the F1-score from 78.40 to 79.36%. For such results, we resorted to deep learning techniques, such as hyper-parameter tuning, label smoothing, and dropout, which helped regularize the Resnet training and reduced overfitting. We discuss how our approach could easily be adapted to perform HAR in real-time and discuss future research directions. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000606179Publication status
publishedExternal links
Journal / series
Frontiers in Public HealthVolume
Pages / Article No.
Publisher
Frontiers MediaSubject
digital health; deep learning; data science; public health; smartphone; activity recognition; physical activity; wearable technologyOrganisational unit
09715 - Menon, Carlo / Menon, Carlo
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ETH Bibliography
yes
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