Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview
The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/14/3213 |
| _version_ | 1828113936676814848 |
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| author | Wesllen Sousa Lima Eduardo Souto Khalil El-Khatib Roozbeh Jalali Joao Gama |
| author_facet | Wesllen Sousa Lima Eduardo Souto Khalil El-Khatib Roozbeh Jalali Joao Gama |
| author_sort | Wesllen Sousa Lima |
| collection | DOAJ |
| description | The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones. |
| first_indexed | 2024-04-11T12:17:49Z |
| format | Article |
| id | doaj.art-01e4f66a1e6446c98253545c00c2fb8b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:17:49Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-01e4f66a1e6446c98253545c00c2fb8b2022-12-22T04:24:12ZengMDPI AGSensors1424-82202019-07-011914321310.3390/s19143213s19143213Human Activity Recognition Using Inertial Sensors in a Smartphone: An OverviewWesllen Sousa Lima0Eduardo Souto1Khalil El-Khatib2Roozbeh Jalali3Joao Gama4Universidade Federal do Amazonas, Manaus 69080-900, BrazilUniversidade Federal do Amazonas, Manaus 69080-900, BrazilUniversity of Ontario Institute of Technology, Oshawa ON L1H 7K4, CanadaUniversity of Ontario Institute of Technology, Oshawa ON L1H 7K4, CanadaInstitute for Systems and Computer Engineering, Technology and Science—INESCTEC, Porto 4200-465, PortugalThe ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.https://www.mdpi.com/1424-8220/19/14/3213human activity recognitionsmartphonesinertial sensorsfeatures extraction |
| spellingShingle | Wesllen Sousa Lima Eduardo Souto Khalil El-Khatib Roozbeh Jalali Joao Gama Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview Sensors human activity recognition smartphones inertial sensors features extraction |
| title | Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview |
| title_full | Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview |
| title_fullStr | Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview |
| title_full_unstemmed | Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview |
| title_short | Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview |
| title_sort | human activity recognition using inertial sensors in a smartphone an overview |
| topic | human activity recognition smartphones inertial sensors features extraction |
| url | https://www.mdpi.com/1424-8220/19/14/3213 |
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