Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Motion pattern analysis uses a variety of methods to recognise physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. Real cycling experiments were recorded in a hilly area with routes of about 12 km long. Signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data, including the detection of heart rate recovery delay as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The classification of downhill and uphill cycling based upon accelerometric data achieved an accuracy of 93.9 % and 95.0 % for the training and testing data sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. These techniques may also be applied to wide ranging applications in rehabilitation and in the diagnostics of neurological disorders.