Indoor Location Data for Tracking Human Behaviours: A Scoping Review
Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/1220 |
| _version_ | 1797484541324034048 |
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| author | Leia C. Shum Reza Faieghi Terry Borsook Tamim Faruk Souraiya Kassam Hoda Nabavi Sofija Spasojevic James Tung Shehroz S. Khan Andrea Iaboni |
| author_facet | Leia C. Shum Reza Faieghi Terry Borsook Tamim Faruk Souraiya Kassam Hoda Nabavi Sofija Spasojevic James Tung Shehroz S. Khan Andrea Iaboni |
| author_sort | Leia C. Shum |
| collection | DOAJ |
| description | Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken. |
| first_indexed | 2024-03-09T23:06:52Z |
| format | Article |
| id | doaj.art-61dfc668521344fbb1dcf30efc8900f4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:06:52Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-61dfc668521344fbb1dcf30efc8900f42023-11-23T17:52:27ZengMDPI AGSensors1424-82202022-02-01223122010.3390/s22031220Indoor Location Data for Tracking Human Behaviours: A Scoping ReviewLeia C. Shum0Reza Faieghi1Terry Borsook2Tamim Faruk3Souraiya Kassam4Hoda Nabavi5Sofija Spasojevic6James Tung7Shehroz S. Khan8Andrea Iaboni9KITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaDepartment of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaKITE—Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, CanadaReal-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.https://www.mdpi.com/1424-8220/22/3/1220computational intelligencedata analyticsdigital phenotypinghealth monitoring technologieshuman behaviourreal-time location systems |
| spellingShingle | Leia C. Shum Reza Faieghi Terry Borsook Tamim Faruk Souraiya Kassam Hoda Nabavi Sofija Spasojevic James Tung Shehroz S. Khan Andrea Iaboni Indoor Location Data for Tracking Human Behaviours: A Scoping Review Sensors computational intelligence data analytics digital phenotyping health monitoring technologies human behaviour real-time location systems |
| title | Indoor Location Data for Tracking Human Behaviours: A Scoping Review |
| title_full | Indoor Location Data for Tracking Human Behaviours: A Scoping Review |
| title_fullStr | Indoor Location Data for Tracking Human Behaviours: A Scoping Review |
| title_full_unstemmed | Indoor Location Data for Tracking Human Behaviours: A Scoping Review |
| title_short | Indoor Location Data for Tracking Human Behaviours: A Scoping Review |
| title_sort | indoor location data for tracking human behaviours a scoping review |
| topic | computational intelligence data analytics digital phenotyping health monitoring technologies human behaviour real-time location systems |
| url | https://www.mdpi.com/1424-8220/22/3/1220 |
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