[P1–619]: EFFECTS OF GROUND LIGHTING UNIFORMITY AND CLUTTER ON NAVIGATIONAL ABILITY IN POSTERIOR CORTICAL ATROPHY AND TYPICAL ALZHEIMER's DISEASE

Ocal, Dilek; Yong, Keir; McCarthy, Ian; Suzuki, T.; Suzuki, Ayako; Boampong, Derrick; Papadosifos, Nikolaos N.; Tyler, Nick; Crutch, Sebastian J.

doi:10.1016/j.jalz.2017.06.635

Cited by 1 publication

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“…Secondly, while some previous studies have analyzed different types of dementia (Mc Ardle et al, 2020), previous studies ignore the differences between types of dementia and either focus on one type of dementia (Wittwer et al, 2013;Cedervall et al, 2014;Callisaya et al, 2017) or consider dementia without looking at its type (Marquis et al, 2002;Wang et al, 2006). Furthermore, to our knowledge, gait of people with PCA has only been analyzed by previous research in this line of investigation (Carton et al, 2016;Ocal et al, 2017;Yong et al, 2018Yong et al, , 2020McCarthy et al, 2019;McCarthy et al, Unpublished 1 ). This research has found that some patients with dementia show a consistent pattern of hesitation (which can be identified from step times) when navigating complex routes (McCarthy et al, 2019;Yong et al, 2020).…”

Section: Dementia Detectionmentioning

confidence: 99%

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

Holloway

Bhot

Yong

et al. 2022

Front. Comput. Sci.

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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.

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