In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Demrozi, Florenc;Pravadelli, Graziano;
2020

Abstract

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.
Accelerometer
Available Datasets
Deep Learning (DL)
Human Activity Recognition (HAR)
Machine Learning (ML)
Sensors
File in questo prodotto:
File Dimensione Formato  
09257355.pdf

accesso aperto

Tipologia: Versione dell'editore
Licenza: Creative commons
Dimensione 3.75 MB
Formato Adobe PDF
3.75 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1034331
Citazioni
  • ???jsp.display-item.citation.pmc??? 23
  • Scopus 77
  • ???jsp.display-item.citation.isi??? ND
social impact