How to process respiratory signals in sport
New technologies as wearable strain sensors have been intensively studied in recent years for applications in sport science to boost athletic performance and reach their personal best. The analysis of respiratory signals is a necessary process for quantifying the training and competitive match loads of players and optimizing both training and recovery strategies. This lecture focuses on the processing of the breathing waveform to estimate the respiratory frequency and assess the performance of wearable sport systems. Particular attention will be devoted to: signal smoothing and filtering for noise removal; spectral analysis to understand the frequency characteristics of signals and the contents related and unrelated to breathing, strategies for motion artifacts removal and respiratory frequency estimation, and methods for the assessment of wearables performance in comparison to benchmarks.
Daniela Lo Presti - Biosketch
Daniela Lo Presti (Ph.D. 2021) is a post-doctoral research fellow at the Unit of Measurement and Biomedical Instrumentation of University Campus Bio-Medico of Rome. Her main research activity focuses on the design, fabrication and assessment of wearables and smart systems for physiological measurements in clinical, occupational, and sport environments. She is currently involved in the H2020/ICT European Project “CONnected through roBOTS (CONBOTS): physically coupling humans to boost handwriting and music learning.” Her work is devoted to the development of wearables based on different technologies (e.g., magneto-inertial units, fiber optic systems, and piezoresistive sensors) and to data processing for the extraction of reliable information about the user physical, emotional, and mental state.