PhD

| 2022

Detecting and Assessing Older Adults’ Stressful Interactions with the Built Environment: An Elderly-Centric and Wearable Sensing-Based Approach

Globally, one in six people is expected to age 65 years by 2050. Not only is the global population ageing, but also the built environment infrastructure in many cities and communities are approaching their design life. This phenomenon is referred to as “double ageing”. Ageing built environment infrastructure with defects are likely to result in environmental barriers with excessive demands; humans experience stress and/or their mobility is limited when the environmental demands exceed their functional capability. Given that human’s functional capability declines with ageing, there is more likelihood for older adults to experience stressful environmental interactions that could limit their mobility than the average person. Current approaches to detect environmental barriers are inefficient, time-consuming, and costly, which may limit the frequency and scope of the built environment assessment. In order to promote active ageing in cities and communities, urban planners and municipal decision-makers need a more efficient approach to assess and detect excessively demanding environmental conditions. The aim of this research is to promote older adults’ mobility by reducing environmental demands. The overall goal of this research is in two folds: (1) to enable practitioners to detect stressful older adults-environment interactions in near real-time and (2) to bring to the limelight the influence of urban environment configurations on older adults’ stress response. To achieve this goal, this research harnessed the current advances in wearable sensing technologies to collect older adults’ bodily responses (i.e., physiological, behavioural, and cognitive responses) to their interaction with the environment as a means of assessing and detecting environmental barriers.
Specifically, a methodological framework was developed for researchers and practitioners to determine the relevance and informativeness of people’s bodily responses in the context of their study. Based on this framework, it was identified that older adults’ physiological response is more informative than the cognitive and behavioural responses. The informativeness of the cognitive response was affected by the walking activity, and the gait abnormality among older adults affected their behavioural responses. A statistical, spatial and space-time pattern mining was conducted to understand the relationships in older adults’ physiological responses to the built environment. The results demonstrate that the relationships between older adults’ physiological response and the environmental condition are less apparent at the individual level. However, using collective sensing (i.e., aggregating multiple participants’ physiological responses) can accommodate the individual variability and capture any normality in the data, which is indicative of an environment’s condition. An optimised environmental stress hot spot detection framework was developed using an Ensemble bagged tree algorithm that achieved 98% accuracy. A simulation-based approach was used to examine areas within the study area that are sufficiently powered to detect stress hot spots that pose high risk to older adults. The results demonstrate that urban planners and municipal decision-makers can use this approach to detect and alleviate stressful environmental conditions more efficiently; as a result, improving older adult’s mobility in the built environment. An integrated methodology based on machine learning and an evolutionary rule-based system was developed to further understand the influence of visuospatial configurations (specifically, isovist indicators) of urban space on older adults’ physiological stress. The results demonstrate that isovist minimum visibility, occlusivity and isovist area are the most influential determinants of older adults’ physiological stress and non-stress response. Older adults prefer urban configurations where they can be seen. The generated visuospatial configurations can be used to inform urban design
and planning.

Keywords

elderly, wearables,

Author/s

Alex Torku

Institution / Department

The Hong Kong Polytechnic University, Department of Building and Real Estate

Upcoming Events
Need help?
Want a live demo?
Interested in prices?
Want to say hello?
Always just a call away
+49 89 2000 301 60

Wireless Data Transmission


The wireless data transmission from OpenGo Sensor Insoles to the mobile device uses intelligent handshake technology to avoid the loss of data packages, even when the connection quality is below 100 %.

Typical wireless range of the Bluetooth Low Energy connection in indoor settings is ≥ 10 m, in in-field settings up to 20 m.

Get support

Check our FAQ database for answers to frequently asked questions!


Describe your issue in as much detail as possible. Include screenshots or files if applicable.


Have a general inquiry?

Write us a message for general questions about products and solutions or if you’d like to discuss other topics.


Select your desired system

moticon-rego-logo

The cutting edge test based outcome assessment system for health professionals and trainers

opengo-logo-orange

The most versatile toolkit for free data acquisition and comprehensive analytics in research

Select your desired system

moticon-rego-logo

The cutting edge test based outcome assessment system for health professionals and trainers

opengo-logo-orange

The most versatile toolkit for free data acquisition and comprehensive analytics in research

The form was sent successfully.

You will be contacted shortly.

Get an individual quote

Please provide the quantity of sensor insoles, software licenses or other items for your individual quote. Sensor insole sizes can be chosen upon final order (not relevant for your quote).

A problem was detected in the following Form. Submitting it could result in errors. Please contact the site administrator.

Article/s

Amount

ReGo App

More details >

Free of charge

ReGo Sensor Insoles

1 pair - all sizes same price

More details >

ReGo Accessories

More details >

Product Bag

For 6 pairs of sensor insoles

Get an individual quote

Please provide the quantity of sensor insoles, software licenses or other items for your individual quote. Sensor insole sizes can be chosen upon final order (not relevant for your quote).

A problem was detected in the following Form. Submitting it could result in errors. Please contact the site administrator.

Article/s

Amount

OpenGo App

More details >

Free of charge

OpenGo Sensor Insoles

1 pair - all sizes same price

More details >

OpenGo Software

More details >

BASIC Module

ANALYZE Module

VIDEO Module

GAIT Module

BALANCE Module

JUMP Module

OpenGo Accessories

More details >

Coin Cell Batteries

1 pair for 1 pair of sensor insoles

Coin Cell Charger

Battery Lid

For sensor insole

Product Bag

For 6 pairs of sensor insoles

Coin Cell Bag

For 20 pairs of coin cells

Sensor Insoles Sack

For 1 pair of sensor insoles