Unobtrusive camera-based monitoring of behavioural disturbances in patients with dementia living in long term care facilities

East, Ryan; Mbabaali, Sophia; Sivelle, Camille; Laguelle, Noemie; Choukou, Mohamed-Amine

doi:10.1145/3396339.3396395

Cited by 3 publications

(2 citation statements)

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“…Elderly suffering from dementia [3], [24], [29], [36], [45], [51] Rule-based/ threshold indices [19], [31]- [33], [38], [41], [70] Loss of appetite and urinary tract infection [37] Elderly suffering from Alzheimer [62]…”

Section: Nonaccidentalmentioning

confidence: 99%

A Review of Abnormal Behavior Detection in Activities of Daily Living

et al. 2023

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Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend. INDEX TERMS abnormal behavior detection, activities of daily living

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Section: Nonaccidentalmentioning

confidence: 99%

A Review of Abnormal Behavior Detection in Activities of Daily Living

et al. 2023

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“…A brief description, time and location of the event is included in the message sent to the healthcare professional (e.g., fall/8:00 pm/room 9). East et al (2020) have described the design and development of the platform [ 74 ].…”

Section: Introductionmentioning

confidence: 99%

Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

Choukou

Mbabaali

East

2021

IJERPH

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The number of Canadians with dementia is expected to rise to 674,000 in the years to come. Finding ways to monitor behavioural disturbance in patients with dementia (PwDs) is crucial. PwDs can unintentionally behave in ways that are harmful to them and the people around them, such as other residents or care providers. Current practice does not involve technology to monitor PwD behaviours. Events are reported randomly by nonstaff members or when a staff member notices the absence of a PwD from a scheduled event. This study aims to explore the potential of implementing a novel detector of behavioural disturbances (DBD) in long-term care homes by mapping the perceptions of healthcare professionals and family members about this technology. Qualitative information was gathered from a focus group involving eight healthcare professionals working in a tertiary care facility and a partner of a resident admitted in the same facility. Thematic analysis resulted in three themes: (A) the ability of the DBD to detect relevant dementia-related behavioural disturbances that are typical of PwD; (B) the characteristics of the DBD and clinical needs and preferences; (C) the integration of the DBD into daily routines. The results tend to confirm the adequacy of the DBD to the day-to-day needs for the detection of behavioural disturbances and hazardous behaviours. The DBD was considered to be useful and easy to use in the tertiary care facility examined in this study. The participants intend to use the DBD in the future, which means that it has a high degree of acceptance.

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