Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Newly-Developed Smart Insole System with Instant Reminder: Paves the Way towards Integrating Artificial Intelligence (AI) Technology to Improve Balance and Prevent Falls
Jönköping University, School of Health and Welfare, HHJ. Prosthetics and Orthotics. The Hong Kong Polytechnic University.ORCID iD: 0000-0001-6507-2329
The Hong Kong Polytechnic University.
The Hong Kong Polytechnic University.
The Hong Kong Polytechnic University.
Show others and affiliations
2019 (English)In: Age and Ageing, ISSN 0002-0729, E-ISSN 1468-2834, Vol. 48, no Issue Supplement_4, p. iv28-iv33, article id 121Article in journal, Meeting abstract (Refereed) [Artistic work] Published
Abstract [en]

Background

Falls in senior people have high incidence& lead to severe injuries [1]. Application of smart wearable systems (with sensors to monitor user’s balance and corresponding instant reminder to let tusers adjust posture/motion) can effectively improve static standing balance [2], reduce reaction time and body sway in response to balance perturbation [3], improve walking pattern [4], and reduce the risk of falls [5, 6]. However, previous systems have not considered the daily monitor of user’s balance and falling risks, and the personalized reminder. Artificial intelligence (AI) and big data analytics have been widely used to monitor the daily physical activity [7], while few studies have utilized them to improve balance/gait and prevent falls.

Methods

This study has optimized previous devices by integrating AI technology and developed a new smart insole system. The system consisted of insoles with embedded sensors that can capture the foot motion and plantar pressure, smart watch that connected with insoles wirelessly and then transmitted the foot motion and force data to Cloud server via Wi-Fi, central Cloud server for big data transmission and storage, workstation for big data analytics and machine learning, and user interface for data visualization (e.g. smartphone, tablet, and/or laptop).

Results & Discussion

The system transmission rate was up to 30 Hz. The collected big data contained all sensor signals captured before and after delivering reminder, and from day-to-day monitoring of users. The customized reminder varied in the type, frequency, magnitude, and amount/dosage. This AI smart insole system enabled the monitor of daily balance and falling risks and the provision of timely-updated and customized reminder to users, which could potentially reduce the risk of falls and slips. It can also act as a balance-training device.

Place, publisher, year, edition, pages
Kuala Lumpur, 2019. Vol. 48, no Issue Supplement_4, p. iv28-iv33, article id 121
Keywords [en]
smart wearable, artificial intelligence (AI), balance, fall, elderly
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:hj:diva-47477DOI: 10.1093/ageing/afz164.121OAI: oai:DiVA.org:hj-47477DiVA, id: diva2:1387310
Conference
1st World Congress on Falls and Postural Stability
Available from: 2020-01-21 Created: 2020-01-21 Last updated: 2020-01-21

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full texthttps://doi.org/10.1093/ageing/afz164.121

Search in DiVA

By author/editor
Ma, Christina Zong-Hao
By organisation
HHJ. Prosthetics and Orthotics
In the same journal
Age and Ageing
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 13 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf