Behavioral modeling and cognitive assessment in smart textiles
- Authors: Oatley, Giles , Choudhury, Tanveer , Buckman, Paul
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 229-231
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- Description: Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). We have developed a prototype smart textile system that uses capacitive sensing to loosely couple the textile overlay from the underlying technology layer. This inclusion of technology adds to the user experience and quality of life is increased. Additionally, by using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors we can represent and design a range of sophisticated memory and reasoning diagnostic/ assessment tools, which are detailed in this paper. © 2022 ACM.
Exploring the relationship between testosterone and diabetes within the UK Biobank data
- Authors: Oatley, Giles
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 244-247
- Full Text: false
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- Description: The UK Biobank (UKB) cohort data aims to improve the prevention, diagnosis, and treatment of a wide range of serious diseases, including diabetes. Presented is a population-based retrospective cohort study to explore the relationship between steroid hormones and the prevalence of diabetes. In particular, free testosterone is calculated from available serum biochemical markers in the UKB data, prevalent diabetes is calculated across a range of UKB data fields and ICD10 codes are generalized to their top-level classifications. It is then possible to explore relationships between testosterone levels, diabetes presence, and associated morbidities. © 2023 ACM.
Non-invasive smartphone use monitoring to assess cognitive impairment
- Authors: Thang, Nguyen , Oatley, Giles , Stranieri, Andrew , Walker, Darren
- Date: 2021
- Type: Text , Conference paper
- Relation: 13th International Conference on Computer and Automation Engineering, ICCAE 2021 p. 64-67
- Full Text: false
- Reviewed:
- Description: Background: There are many tests for the early detection of Mild Cognitive Impairment (MCI) to prevent or delay the development of dementia, particularly amongst the elderly. However, many tests are complex and are required to be performed repeatedly. Cognitive assessment apps for a smartphone have emerged, but like other tests, they require the user to perform complex tasks like drawing time on a clock. Few studies have explored non-invasive ways of tracking and assessing MCI without having the user perform specific tests. Objective: This research ultimately aims to develop an app that runs in the background and collects smartphone usage data that correlates well with MCI test results. The focus of this preliminary study was to develop an app that collects usage data and common MCI questionnaires to see if usage data between people varied, and to establish associations between phone usage and cognitive tests results. Method: An android application was developed to gather data over three weeks by three volunteers (authors). Usage data collected included: SMS and call log, accelerometer, location, app usage, self-report. Cognitive tests implemented were Stroop, Go/No Go tests and absent-mindedness questionnaires. Due to the small sample size and Covid-19 restrictions (October 2020), location data was not reliable. SMS text was not collected for privacy reasons. Results: App categories can differentiate people, but the app usage cannot be used to distinguish people. © 2021 IEEE.