Review of Cycling Bio-mechanics Acquisition Modality and Measuring Parameters

Cycling bio-mechanics measurement is important to enable data collection on the limbs movement affecting the way a person move or cycle. This paper presents the review of cycling bio-mechanics acquisition modality related to the cyclist posture and movement, bicycle fit and configuration. The technological development in machine learning approach to effectively monitor and measure the cycling performance is also reviewed to give the insight of data-driven approach. The cycling bio-mechanics are discussed to highlight joint parameters detection using sensing modalities. It can be generally divided into two approaches of the cyclist’s joint motion acquisition, i.e. the wearable sensors and non-wearable sensors. The recent development using machine learning approaches in sports are reviewed to provide some insights on the involvement of technology to monitor and classify limb movement in sports activity including cycling performance.