Comparison of Feature Selection and Classification for Human Activity and Fall Recognition using Smartphone Sensors

Vong, Chanvichet; Theptit, Thitiphoom; Watcharakonpipat, Virinya; Chanchotisatien, Passara; Laitrakun, Seksan

doi:10.1109/ectidamtncon51128.2021.9425742

Cited by 1 publication

(2 citation statements)

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“…Our benchmark regroups head-to-head 17 articles (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) by sharing their approach concerning the human activity recognition task evaluated on the UniMiB-SHAR dataset. Deep learning was the method of choice in almost every case (21-26, 28-32, 34, 35) to try to achieve state-of-the-art results.…”

Section: Related Work From Benchmarkmentioning

confidence: 99%

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The use of deep learning for smartphone-based human activity recognition

Stampfler

Elgendi²,

Fletcher³

et al. 2023

Front. Public Health

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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.

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Section: Related Work From Benchmarkmentioning

confidence: 99%

“…Deep learning was the method of choice in almost every case (21-26, 28-32, 34, 35) to try to achieve state-of-the-art results. Only Vong et al (33) employed a more standard machine learning and feature engineering approach. When choosing deep learning, the focus of studies was split into two parts.…”

Section: Related Work From Benchmarkmentioning

confidence: 99%