# Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data

arXiv: https://arxiv.org/abs/2007.03615 · HTML: https://mlsanity.com/paper/2007.03615/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices](https://mlsanity.com/paper/2412.09289/) (2024)
- [Transfer Learning of RSSI to Improve Indoor Localisation Performance](https://mlsanity.com/paper/2412.09292/) (2024)

## Abstract (arXiv)

There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This paper specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the textitearly stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset of longitudinal sensor data from persons with MCI and AD. We present preliminary findings that show the relationship between levels of sleep quality and wandering can be subtly different between patients in the early stages of dementia and healthy cohabiting controls.