Machine Learning Assisted Gait Analysis for the Determination of Handedness in Able-bodied People
Gallagher, Hugh · Arrow - TU Dublin (Technological University Dublin) · 2020
This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female samples; Male – only the male samples; Uninjured – samples declaring no injury within the last year. The initial phase of this study involved a data collection scheme, as a suitable, pre-existing dataset could not be found to be available. This data collection resulted in 45 participants (7 left-handed, and 38 right-handed. 0 identified as ambidextrous), which resulted in 180 sample videos for use in transfer learning and testing the five produced models. The video samples were recorded to obtain the volunteers’ walking pattern, head to toe, in profile rather than head on. This was to allow the models to obtain as much information about arm and leg movement as possible when it came to analysis.