Assessing the performance of winter footwear using a new maximum achievable incline method

Hsu, Jennifer; Li, Yue; Dutta, Tilak; Fernie, Geoff

doi:10.1016/j.apergo.2015.03.016

Cited by 14 publications

(11 citation statements)

References 39 publications

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“…In 2015, researchers at KITE Research Institute, Toronto Rehabilitation Institute (TRI) established a new testing method that measures the maximum achievable incline during walking on icy surfaces. This angle was used to measure the Coefficient of Friction (COF) values for each footwear [2,9]. This human-oriented protocol is run in WinterLab, which has an ice floor, a snow maker, and air and ground temperature controllers.…”

Section: Testing Methodologymentioning

confidence: 99%

“…One of the most prevalent perturbations is slipping, which occurs at the shoe and floor interface when the required walking friction exceeds the existing friction provided by the interface [6][7][8]. Slip-related falls can lead to serious medical complications such as hip fractures and traumatic brain injuries, which contribute to limitations in independent living and lower quality of life [2,9,10]. Slip-resistant footwear can play an important role in preventing slip-induced falls [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Slip-resistant footwear can play an important role in preventing slip-induced falls [5][6][7][8]. However, the current standards for measuring the slip resistance of winter footwear are inadequate and have poor biofidelity [2,9,10]. This makes it difficult for footwear manufacturers to optimize their outsole designs and materials properly and to inform clients of footwear performance.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network for Slip Detection on Ice Surface

Fernie

et al. 2020

Sensors

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Slip-induced falls are among the most common causes of major occupational injuries and economic loss in Canada. Identifying the risk factors associated with slip events is key to developing preventive solutions to reduce falls. One factor is the slip-resistance quality of footwear, which is fundamental to reducing the number of falls. Measuring footwear slip resistance with the recently developed Maximum Achievable Angle (MAA) test requires a trained researcher to identify slip events in a simulated winter environment. The human capacity for information processing is limited and human error is natural, especially in a cold environment. Therefore, to remove conflicts associated with human errors, in this paper a deep three-dimensional convolutional neural network is proposed to detect the slips in real-time. The model has been trained by a new dataset that includes data from 18 different participants with various clothing, footwear, walking directions, inclined angles, and surface types. The model was evaluated on three types of slips: Maxi-slip, midi-slip, and mini-slip. This classification is based on the slip perception and recovery of the participants. The model was evaluated based on both 5-fold and Leave-One-Subject-Out (LOSO) cross validation. The best accuracy of 97% was achieved when identifying the maxi-slips. The minimum accuracy of 77% was achieved when classifying the no-slip and mini-slip trials. The overall slip detection accuracy was 86% with sensitivity and specificity of 81% and 91%, respectively. The overall accuracy dropped by about 2% in LOSO cross validation. The proposed slip detection algorithm is not only beneficial for footwear manufactures to improve their footwear slip resistance quality, but it also has other potential applications, such as improving the slip resistance properties of flooring in healthcare facilities, commercial kitchens, and oil drilling platforms.

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Section: Testing Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network for Slip Detection on Ice Surface

Fernie

et al. 2020

Sensors

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“…This method measures the coefficient of friction by applying a specified normal force pressing the test footwear onto a test surface and then moving the test surface horizontally at a set constant speed using the SATRA STM 603 Slip Resistance Testing machine. Unfortunately, this test has been found to have poor ecological validity [ 15 , 16 , 17 ] likely because it is unable to simulate the complex dynamics of human walking. Ecological validity for a test refers to whether the test results are representative of performance in real-life settings [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Slip Detection Algorithm for Walking on Level and Inclined Ice Surfaces

Cen

Dutta

2022

Sensors

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Slip-resistant footwear can prevent fall-related injuries on icy surfaces. Winter footwear slip resistance can be measured by the Maximum Achievable Angle (MAA) test, which measures the steepest ice-covered incline that participants can walk up and down without experiencing a slip. However, the MAA test requires the use of a human observer to detect slips, which increases the variability of the test. The objective of this study was to develop and evaluate an automated slip detection algorithm for walking on level and inclined ice surfaces to be used with the MAA test to replace the need for human observers. Kinematic data were collected from nine healthy young adults walking up and down on ice surfaces in a range from 0° to 12° using an optical motion capture system. Our algorithm segmented these data into steps and extracted features as inputs to two linear support vector machine classifiers. The two classifiers were trained, optimized, and validated to classify toe slips and heel slips, respectively. A total of approximately 11,000 steps from 9 healthy participants were collected, which included approximately 4700 slips. Our algorithm was able to detect slips with an overall F1 score of 90.1%. In addition, the algorithm was able to accurately classify backward toe slips, forward toe slips, backward heel slips, and forward heel slips with F1 scores of 97.3%, 54.5%, 80.9%, and 86.5%, respectively.

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“…However, the existing human-centred studies have typically involved only subjective ratings of slip resistance (Gard and Lundborg 2000 ; Gao and Abeysekera 2002 ), or short walkways (less than 3 m) and limited surfaces and contaminants (Skiba, Wieder, and Cziuk 1986 ; Jung 1989 ; Gao, Oksa et al 2008 ). More recently, we have proposed a new test method for assessing footwear slip resistance using the maximum slope angle that users are able to achieve while walking over wet ice (Hsu et al 2015 ). To the authors’ knowledge, this study, which builds on our previous work, is the first study to incorporate more comprehensive environmental conditions (such as simulated snow conditions) in biomechanical testing of winter footwear.…”

Section: Introductionmentioning

confidence: 99%

Slip resistance of winter footwear on snow and ice measured using maximum achievable incline

et al. 2015

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Protective footwear is necessary for preventing injurious slips and falls in winter conditions. Valid methods for assessing footwear slip resistance on winter surfaces are needed in order to evaluate footwear and outsole designs. The purpose of this study was to utilise a method of testing winter footwear that was ecologically valid in terms of involving actual human testers walking on realistic winter surfaces to produce objective measures of slip resistance. During the experiment, eight participants tested six styles of footwear on wet ice, on dry ice, and on dry ice after walking over soft snow. Slip resistance was measured by determining the maximum incline angles participants were able to walk up and down in each footwear–surface combination. The results indicated that testing on a variety of surfaces is necessary for establishing winter footwear performance and that standard mechanical bench tests for footwear slip resistance do not adequately reflect actual performance. Practitioner Summary: Existing standardised methods for measuring footwear slip resistance lack validation on winter surfaces. By determining the maximum inclines participants could walk up and down slopes of wet ice, dry ice, and ice with snow, in a range of footwear, an ecologically valid test for measuring winter footwear performance was established.

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Assessing the performance of winter footwear using a new maximum achievable incline method

Cited by 14 publications

References 39 publications

Deep Neural Network for Slip Detection on Ice Surface

Deep Neural Network for Slip Detection on Ice Surface

Development and Evaluation of a Slip Detection Algorithm for Walking on Level and Inclined Ice Surfaces

Slip resistance of winter footwear on snow and ice measured using maximum achievable incline

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