AIMC Topic: Shoes
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Deep learning analysis and age prediction from shoeprints.
Forensic science international
Aug 30, 2021
Human gaits are the patterns of limb movements which involve both the upper and lower body parts. These patterns in terms of step rate, gait speed, stance widening, stride, and bipedal forces are influenced by different factors including environmenta...
Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning.
Sensors (Basel, Switzerland)
Feb 23, 2021
Vertical ground reaction force (vGRF) can be measured by force plates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefo...
Deep Neural Network for Slip Detection on Ice Surface.
Sensors (Basel, Switzerland)
Dec 2, 2020
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...
Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.
Human movement science
Oct 22, 2020
There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable se...
Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?
Sensors (Basel, Switzerland)
Jul 18, 2020
Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As rese...
Gait Segmentation of Data Collected by Instrumented Shoes Using a Recurrent Neural Network Classifier.
Physical medicine and rehabilitation clinics of North America
Mar 11, 2019
The authors present a Recurrent Neural Network classifier model that segments the walking data recorded with instrumented footwear. The signals from 3 piezoresistive sensors, a 3-axis accelerometer, and Euler angles are used to generate temporal gait...
The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning.
IEEE transactions on bio-medical engineering
Feb 21, 2019
OBJECTIVE: This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This a...
Artificial neural networks in the selection of shoe lasts for people with mild diabetes.
Medical engineering & physics
Jan 14, 2019
This research addressed the selection of shoe lasts for footwear design to help relieve the pain associated with diabetic neuropathy and foot ulcers. A reverse engineering (RE) technique was used to convert point clouds corresponding to scanned shoe ...
Exoskeleton plantarflexion assistance for elderly.
Gait & posture
Nov 28, 2016
Elderly are confronted with reduced physical capabilities and increased metabolic energy cost of walking. Exoskeletons that assist walking have the potential to restore walking capacity by reducing the metabolic cost of walking. However, it is unclea...
Defining functional groups based on running kinematics using Self-Organizing Maps and Support Vector Machines.
Journal of biomechanics
Mar 20, 2015
A functional group is a collection of individuals who react in a similar way to a specific intervention/product such as a sport shoe. Matching footwear features to a functional group can possibly enhance footwear-related comfort, improve running perf...
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