Just find it: The Mymo approach to recommend running shoes

Wearing inappropriate running shoes may lead to unnecessary injury through continued strain upon the lower extremities; potentially damaging a runner’s performance. Many technologies have been developed for accurate shoe recommendation, which centre on running gait analysis. However, these often require supervised use in the laboratory/shop or exhibit too high a cost for personal use. This work addresses the need for a deployable, inexpensive product with the ability to accurately assess running shoe-type recommendation. This was achieved through quantitative analysis of the running gait from 203 individuals through use of a tri-axial accelerometer and tri-axial gyroscope-based wearable (Mymo). In combination with a custom neural network to provide the shoe-type classifications running within the cloud, we experience an accuracy of 94.6 in classifying the correct type of shoe across unseen test data

Gait Data Augmentation using Physics-Based Biomechanical Simulation

This paper focuses on addressing the problem of data scarcity for gait analysis. Standard augmentation methods may produce gait sequences that are not consistent with the biomechanical constraints of human walking. To address this issue, we propose a novel framework for gait data augmentation by using OpenSIM, a physics-based simulator, to synthesize biomechanically plausible walking sequences. The proposed approach is validated by augmenting the WBDS and CASIA-B datasets and then training gait-based classifiers for 3D gender gait classification and 2D gait person identification respectively. Experimental results indicate that our augmentation approach can improve the performance of model-based gait classifiers and deliver state-of-the-art results for gait-based person identification with an accuracy of up to 96.11% on the CASIA-B dataset.Comment: 30 pages including references, 5 Figures submitted to ESW

Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively

Learning architecture for the recognition of walking and prediction of gait period using wearable sensors

This work presents a novel learning architecture for the recognition and prediction of walking activity and gait period, respectively, using wearable sensors. This approach is composed of a Convolutional Neural Network (CNN), a Predicted Information Gain (PIG) module and an adaptive combination of information sources. The CNN provides the recognition of walking and gait periods. This information is used by the proposed PIG method to estimate the next most probable gait period along the gait cycle. The outputs from the CNN and PIG modules are combined by a proposed adaptive process, which relies on data from the source that shows to be more reliable. This adaptive combination ensures that the learning architecture provides accurate recognition and prediction of walking activity and gait periods over time. The learning architecture uses data from an array of three inertial measurement units attached to the lower limbs of individuals. The validation of this work is performed by the recognition of level-ground walking, ramp ascent and ramp descent, and the prediction of gait periods. The recognition of walking activity and gait period is 100% and 98.63%, respectively, when the CNN model is employed alone. The recognition of gait periods achieves a 99.9% accuracy, when the PIG method and adaptive combination are also used. These results demonstrate the benefit of having a system capable of predicting or anticipating the next information or event over time. Overall, the learning architecture offers an alternative approach for accurate activity recognition, which is essential for the development of wearable robots capable of reliably and safely assisting humans in activities of daily living