Abstract-Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in an outdoor training environment. We firstly present a system that automatically classifies a large range of training activities using the Discrete Wavelet Transform (DWT) in conjunction with a Random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Secondly, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the thigh and shank of a subject, from which the flexion-extension knee angle is calculated. Finally, a curve shift registration technique is applied to both generate normative data and determine if a subject's movement technique differed to the normative data in order to identify potential injury related factors. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments.
Ahmadi et al. REF propose to use the discrete wavelet transform in conjunction with a random forest (RF) algorithm based on flexion/extension of the knees to classify six sports actions performed in an outdoor training environment.
Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors
2014 11th International Conference on Wearable and Implantable Body Sensor Networks
2014 11th International Conference on Wearable and Implantable Body Sensor Networks
Computer Science