Detecting Depression-related Movement Changes in Older Adults using Smart Home Motion Sensors - A Feasibility Study.

Smart home sensor data is being increasingly used to identify health risks through passive tracking of specific behaviours and activity patterns. This study explored the feasibility of using motion sensor data to track changes in daytime movement patterns within the home, and their potential association with depression in older adults. This study analysed the motion sensor data collected during a one-year smart home trial, and explored their association with Geriatric Depression Scale (GDS) scores collected at three different time points during the trial (i.e., baseline, mid-trial, and end-trial). Our results showed that movement patterns are generally reduced when older adults are in a depressed state compared to when being in a not-depressed state. In particular, the reduced movement activity in depressed states was significant (p<.05) when the participant's GDS state changed between depressed and not-depressed for the first time during the three time points of the trial when GDS was collected.Clinical relevance- Our results establish the feasibility and potential use of motion sensor data from ambient sensors in a smart home for passive and remote assessment of older adults' depression status, that is comparable to their GDS scores, through changes in their in-home day-time movement patterns. Also since reduced movement activity may be a general indicator of potential health risks, this study provides preliminary evidence for using in-home movement activity monitoring as an general indicator of health risks.

The Significance and Limitations of Sensor-based Agitation Detection in People Living with Dementia.

Agitation, a commonly observed behaviour in people living with dementia (PLwD), is frequently interpreted as a response to physiological, environmental, or emotional stress. Agitation has the potential to pose health risks to both individuals and their caregivers, and can contribute to increased caregiver burden and stress. Early detection of agitation can facilitate with timely intervention, which has the potential to prevent escalation to other challenging behaviors. Wearable and ambient sensors are frequently used to monitor physiological and behavioral conditions and the collected signals can be engaged to detect the onset of an agitation episode. This paper delves into the current sensor-based methods for detecting agitation in PLwD, and reviews the strengths and limitations of existing works. Future directions to enable real-time agitation detection to empower caregivers are also deliberated, with a focus on their potential to reduce caregiver burden by facilitating early support, assistance and interventions to timely manage agitation episodes in PLwD.

Sensor-Based Assessment of Social Isolation and Loneliness in Older Adults: A Survey.

Social isolation (SI) and loneliness are 'invisible enemies'. They affect older people's health and quality of life and have significant impact on aged care resources. While in-person screening tools for SI and loneliness exist, staff shortages and psycho-social challenges fed by stereotypes are significant barriers to their implementation in routine care. Autonomous sensor-based approaches can be used to overcome these challenges by enabling unobtrusive and privacy-preserving assessments of SI and loneliness. This paper presents a comprehensive overview of sensor-based tools to assess social isolation and loneliness through a structured critical review of the relevant literature. The aim of this survey is to identify, categorise, and synthesise studies in which sensing technologies have been used to measure activity and behavioural markers of SI and loneliness in older adults. This survey identified a number of feasibility studies using ambient sensors for measuring SI and loneliness activity markers. Time spent out of home and time spent in different parts of the home were found to show strong associations with SI and loneliness scores derived from standard instruments. This survey found a lack of long-term, in-depth studies in this area with older populations. Specifically, research gaps on the use of wearable and smart phone sensors in this population were identified, including the need for co-design that is important for effective adoption and practical implementation of sensor-based SI and loneliness assessment in older adults.