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Deep Neural Network for Slip Detection on Ice Surface.

Kent Wu¹, Suzy He¹, Geoff Fernie^1,2

¹The Kite Research Institute, Toronto Rehabilitation Institute-University Health Network, University of Toronto, Toronto, ON M5G 2A2, Canada.

Sensors (Basel, Switzerland)

|December 5, 2020

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A new deep learning model accurately detects slips in real-time, crucial for improving footwear slip resistance and preventing workplace injuries. This AI approach minimizes human error in slip analysis.

Area of Science:

Occupational Safety and Health
Artificial Intelligence in Engineering
Biomechanics of Human Motion

Background:

Slip-induced falls are a major cause of occupational injuries and economic loss in Canada.
Footwear slip resistance is a key factor in preventing falls, but current testing methods like the Maximum Achievable Angle (MAA) test are prone to human error.
Limited human information processing capacity and environmental factors like cold can impact accurate slip event identification.

Purpose of the Study:

To develop a real-time slip detection algorithm using a deep three-dimensional convolutional neural network.
To eliminate human error and subjectivity in identifying slip events during footwear slip resistance testing.
To create a robust system for evaluating footwear performance in simulated winter conditions.

Keywords:

convolution deep neural network injury prevention slip detection spatiotemporal feature extraction

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Main Methods:

A deep three-dimensional convolutional neural network was proposed and trained on a novel dataset.
The dataset included data from 18 participants with variations in clothing, footwear, walking direction, incline, and surface type.
The model was evaluated on three slip classifications (Maxi, Midi, Mini) using 5-fold and Leave-One-Subject-Out cross-validation.

Main Results:

The model achieved a best accuracy of 97% for maxi-slip identification.
Overall slip detection accuracy was 86%, with 81% sensitivity and 91% specificity.
Minimum accuracy was 77% for classifying no-slip and mini-slip trials, with a 2% drop in accuracy during Leave-One-Subject-Out cross-validation.

Conclusions:

The proposed deep learning algorithm effectively detects slips in real-time, offering an objective alternative to human-based assessments.
This technology can aid footwear manufacturers in enhancing product slip resistance and reducing workplace injuries.
Potential applications include improving flooring safety in high-risk environments like healthcare facilities and industrial platforms.