Abstract
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.
Highlights
The discipline of motion recognition, using a range of measurement techniques to characterise the motion associated with different of physical activities, is an increasingly important topic
The goal of this study is to show how wearable sensors can be used for post-exercise analysis of cycling-related information to detect the relationship between physiological data, accelerometric signals acquired at the specific body position, and the route profile recorded by the global navigation satellite systems (GNSSs)
Positioning data recorded by the GNSS system, heart rate data, cycling cadence-derived data, and accelerometric data recorded by a sensor in the mobile phone were used to detect a number of motion patterns
Summary
The discipline of motion recognition, using a range of measurement techniques to characterise the motion associated with different of physical activities, is an increasingly important topic. Measurement techniques include the use of wearable sensors, smartphones, smartwatch-based VOLUME xx, 2021. Many motion tracking systems [9], [10] make use of global navigation satellite systems (GNSSs). They benefit from the increasing accuracy of GNSSs that are based on the use of new satellite systems, including Galileo [11] and the global positioning system (GPS). These systems may be used for monitoring cycling routes [12]. Associated studies include the assessment of road surface roughness [24] and three-dimensional modelling
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
