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Journal Paper

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Assistive & Accessible Technology

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

Catherine Holloway, William Bhot, Keir X. X. Yong, Ian McCarthy, Tatsuto Suzuki, Amelia Carton, Biao Yang, Robin Serougne, Derrick Boampong, Nick Tyler, Sebastian J. Crutch, Nadia Berthouze and Youngjun Cho

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.

Frontiers in Computer Science

Abstract

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

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.

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