Abstract
Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.
Highlights
We sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and Breathing pattern (BP)
Group Breathing rate (BR) ranged from 28.4 ± 7.0 breaths per minute at test start up to 53.1±6.4 bpm at test end
BR estimation can be reliably performed with other methods such as frequency-domain analysis, this event-based approach allows for enhanced BP analysis including breath ratio, entrainment phenomena, and an estimation of BR variability (BRV) [21]
Summary
Breathing pattern (BP) is a comprehensive conceptual framework that provides valuable insights for broad applications, such as sports performance and clinical medicine. BR is strongly correlated to physical workload, is easy to measure, and is information-rich in exercise settings [2]. Other components of BP, such as timing, drive, and coordination, contain additional information relevant to exercise, and their measurement may help to improve performance or identify BP disorders [3,4]. One additional component of BP of particular relevance to rhythmic exercise (e.g., running) is locomotor-respiratory coupling (LRC). LRC, as well as many other components of BP, are hard to measure in real-world conditions without specialized equipment and expert knowledge
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