Inertial Sensor-Based Slip Detection in Human Walking

Trkov, Mitja; Chen, Kuo; Yi, Jingang; Liu, Tao

doi:10.1109/tase.2018.2884723

Cited by 38 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No numerical accuracy was reported in this paper. Trkov et al [15] also recently proposed another threshold-based method for slip detection and slipping distance estimation using force sensors and five IMUs. Eight participants were recruited to walk on a wooden platform covered by a soap film to generate slippery conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Neural Network for Slip Detection on Ice Surface

Fernie

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Deep Neural Network for Slip Detection on Ice Surface

Fernie

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Another work, Ref. [ 91 ] presented an improvement of Kalman filter-based slip estimation for characterizing slipping distance. The very impressive thing about the algorithm is the detection of accurate slip onset in a fast manner along with the cost-effective and non-intrusive features of the sensor.…”

Section: Research Publicationsmentioning

confidence: 99%

Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Tanwar

Nandal²,

Zamani

et al. 2022

Healthcare

View full text Add to dashboard Cite

Falling is one of the most serious health risk problems throughout the world for elderly people. Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall. Fall risks and their effects would be substantially reduced if a fall is predicted or detected accurately on time and prevented by providing timely help. Various methods have been proposed to prevent or predict falls in elderly people. This paper systematically reviews all the publications, projects, and patents around the world in the field of fall prediction, fall detection, and fall prevention. The related works are categorized based on the methodology which they used, their types, and their achievements.

show abstract

“…All of these shoes are designed for gait analysis or detecting falls. Few studies have focused on slip detection for preventing from SRFs [9] but the system includes heavy and complex sensor modules.…”

Section: Introductionmentioning

confidence: 99%

Estimation of coefficient of static friction with one-axis force sensor by observing peripheral local slips on elastic hemisphere

Tagawa

Okatani

Oda

2021

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

With the development of aging societies, medical costs due to slip-related-falls (SRFs) continue to increase. In order to monitor the accidents, wearable shoe systems to detect slips are promising tools to prevent SRFs in daily activities. However, a complex and heavy construction is a bottleneck of the shoe systems. In this paper, a simple sensor mechanism capable of detecting static coefficient of friction (SCOF) only by a one-axis force sensor with elastic hemisphere is proposed. In order to investigate design guidelines for the slip sensor, we experimentally evaluated (i) the Young's modulus of the elastic hemisphere and (ii) the ratio of the hemisphere diameter to the one-axis force sensor diameter. These results indicate that the proposed sensor detects the SCOF by only one-axis force sensor with soft elastic hemisphere. Furthermore, the best diameter ratio of one-axis force sensor and elastic hemisphere is 1/3 with a measurement range from 70 to 230 kPa, which range is suitable for sensor-embedded systems.

show abstract

Inertial Sensor-Based Slip Detection in Human Walking

Cited by 38 publications

References 36 publications

Deep Neural Network for Slip Detection on Ice Surface

Deep Neural Network for Slip Detection on Ice Surface

Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Estimation of coefficient of static friction with one-axis force sensor by observing peripheral local slips on elastic hemisphere

Contact Info

Product

Resources

About