Contact
Need help?
For technical questions about your Moticon products
Want to say hello?
Drop us a message for general questions or inquiries
Want a live demo?
See our products live and ask your questions
Interested in prices?
Get an individual quote with the items you need
Always just a call away
+49 89 2000 301 60

Automation in Construction

| 2021

Automated detection and classification of construction workers’ loss of balance events using wearable insole pressure sensors

Fall on the same level is the leading cause of non-fatal injuries in construction workers; however, identifying loss of balance events associated with specific unsafe surface conditions in a timely manner remain challenging. The objective of the current study was to develop a novel method to detect and classify loss of balance events that could lead to falls on the same level by using foot plantar pressure distributions data captured from wearable insole pressure sensors. Ten healthy volunteers participated in experimental trials, simulating four major loss of balance events (e.g., slip, trip, unexpected step-down, and twisted ankle) to collect foot plantar pressure distributions data. Supervised machine learning algorithms were used to learn the unique foot plantar pressure patterns, and then to automatically detect loss of balance events. We compared classification performance by varying window sizes, feature groups and types of classifiers, and the best classification accuracy (97.1%) was achieved when using the Random Forest classifier with all feature groups and a window size of 0.32 s. This study is important to researchers and site managers because it uses foot plantar pressure distribution data to objectively distinguish various potential loss of balance events associated with specific unsafe surface conditions. The proposed approach can allow practitioners to proactively conduct automated fall risk monitoring to minimize
the risk of falls on the same level on sites.

Keywords

Construction workers, Falls on the same level, Insole pressure sensors Loss of balance, Supervised machine learning

Author/s

Maxwell Fordjour Antwi-Afari, Heng Lia, JoonOh Seo, Arnold Yu Lok Wong

Institution / Department

Dept. of Building and Real Estate, Hong Kong Polytechnic University

The form was sent successfully.

You will be contacted shortly.

moticon-rego-sensor-insole-live-event

Stay one step ahead!

Subscribe to our newsletter for the latest information on case studies, webinars, product updates and company news

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.