Discrimination of cycling patterns using accelerometric data and deep learning techniques

Abstract

The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the cotribution to this topic by analysis of accelerometric signals recorded by wearable sensors located as specitifc body positions and by implementation of deep searning methods to classify signatl features.This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system.

Keywords

• multimodal signal analysis
• computational intelligence
• machine learning
• deep neural networks
• accelerometers
• classification
• motion monitoring