Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations
This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live inde...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/6/2176 |
| _version_ | 1797540662361456640 |
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| author | Yiyuan Zhang Ine D’Haeseleer José Coelho Vero Vanden Abeele Bart Vanrumste |
| author_facet | Yiyuan Zhang Ine D’Haeseleer José Coelho Vero Vanden Abeele Bart Vanrumste |
| author_sort | Yiyuan Zhang |
| collection | DOAJ |
| description | This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity. |
| first_indexed | 2024-03-10T13:04:23Z |
| format | Article |
| id | doaj.art-ef0d458d2de54c9d84638d68598ba573 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:04:23Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ef0d458d2de54c9d84638d68598ba5732023-11-21T11:17:06ZengMDPI AGSensors1424-82202021-03-01216217610.3390/s21062176Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and RecommendationsYiyuan Zhang0Ine D’Haeseleer1José Coelho2Vero Vanden Abeele3Bart Vanrumste4KU Leuven, e-Media Research Lab, 3000 Leuven, BelgiumKU Leuven, e-Media Research Lab, 3000 Leuven, BelgiumLaSIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, PortugalKU Leuven, e-Media Research Lab, 3000 Leuven, BelgiumKU Leuven, e-Media Research Lab, 3000 Leuven, BelgiumThis article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.https://www.mdpi.com/1424-8220/21/6/2176older adultsactivity recognitionbathroom activitieswearable sensorsmachine learning techniques |
| spellingShingle | Yiyuan Zhang Ine D’Haeseleer José Coelho Vero Vanden Abeele Bart Vanrumste Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations Sensors older adults activity recognition bathroom activities wearable sensors machine learning techniques |
| title | Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations |
| title_full | Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations |
| title_fullStr | Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations |
| title_full_unstemmed | Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations |
| title_short | Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations |
| title_sort | recognition of bathroom activities in older adults using wearable sensors a systematic review and recommendations |
| topic | older adults activity recognition bathroom activities wearable sensors machine learning techniques |
| url | https://www.mdpi.com/1424-8220/21/6/2176 |
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