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STEP-UP: Enabling Low-Cost IMU Sensors to Predict the Type of Dementia During Everyday Stair Climbing

Colour Image of Cathy Holloway

Professor Catherine Holloway

Co-founder, Academic Director of GDI Hub and Professor at UCL’s Interaction Centre.

Nadia Berthouze

Professor Affective Interaction (UCL Interaction Centre)

Dr Youngjun Cho

Associate Professor at UCL Computer Science & Interim Programme Director of MSc DDI & Computer Scientist

Abstract:

Posterior Cortical Atrophy is a rare but significant form of dementia which affects people's visual ability before their memory. This is often misdiagnosed as an eyesight rather than brain sight problem. This paper aims to address the frequent, initial misdiagnosis of this disease as a vision problem through the use of an intelligent, cost-effective, wearable system, alongside diagnosis of the more typical Alzheimer's Disease. We propose low-level features constructed from the IMU data gathered from 35 participants, while they performed a stair climbing and descending task in a real-world simulated environment. We demonstrate that with these features the machine learning models predict dementia with 87.02% accuracy. Furthermore, we investigate how system parameters, such as number of sensors, affect the prediction accuracy. This lays the groundwork for a simple clinical test to enable detection of dementia which can be carried out in the wild.