# Tracking agitation in people living with dementia in a care environment

arXiv: https://arxiv.org/abs/2104.09305 · HTML: https://mlsanity.com/paper/2104.09305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)

## Abstract (arXiv)

Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk. In a long term care (LTC) environment, care staff track and document these symptoms as a way to detect when there has been a change in resident status to assess risk, and to monitor for response to interventions. However, this documentation can be time-consuming, and due to staffing constraints, episodes of agitation may go unobserved. This brings into question the reliability of these assessments, and presents an opportunity for technology to help track and monitor behavioural symptoms in dementia. In this paper, we present the outcomes of a 2 year real-world study performed in a dementia unit, where a multi-modal wearable device was worn by 20 PwD. In line with a commonly used clinical documentation tool, this large multi-modal time-series data was analyzed to track the presence of episodes of agitation in 8-hour nursing shifts. The development of a baseline classification model (AUC=0.717) on this dataset and subsequent improvement (AUC= 0.779) lays the groundwork for automating the process of annotating agitation events in nursing charts.