Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.

Ferrari, A., Micucci, D., Mobilio, M., Napoletano, P. (2021). Trends in human activity recognition using smartphones. JOURNAL OF RELIABLE INTELLIGENT ENVIRONMENTS, 7(3), 189-213 [10.1007/s40860-021-00147-0].

Trends in human activity recognition using smartphones

Micucci D.
;
Mobilio M.;Napoletano P.
2021

Abstract

Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.
Articolo in rivista - Review Essay
ADL; Deep learning; Human activity recognition; Machine learning; Smartphone
English
3-lug-2021
2021
7
3
189
213
open
Ferrari, A., Micucci, D., Mobilio, M., Napoletano, P. (2021). Trends in human activity recognition using smartphones. JOURNAL OF RELIABLE INTELLIGENT ENVIRONMENTS, 7(3), 189-213 [10.1007/s40860-021-00147-0].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/327257
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