BIBLIOS

Document type
Journal articles

Document subtype
Full paper

Title
Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

Participants in the publication
Yiyuan Zhang (Author)
Ine D Haeseleer (Author)
José Coelho (Author)
Dep. Informática
LASIGE
Vero Vanden Abeele (Author)
Bart Vanrumste (Author)

Summary
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.


Institution
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Where published
Sensors

Publication Identifiers
eISSN - 1424-8220

Publisher
MDPI AG



Document Identifiers
DOI - https://doi.org/10.3390/s21062176
URL - http://dx.doi.org/10.3390/s21062176
URL - https://www.mdpi.com/1424-8220/21/6/2176

Rankings
SCIMAGO Q1 (2021) - 0.653 - Instrumentation

Keywords
older adults activity recognition bathroom activities wearable sensors machine learning techniques ADL