Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features

Abstract

Reliable monitoring of one’s response to exercise intensity is imperative to effectively plan and manage training, but not always practical in impact sports settings. This study aimed to evaluate if an inexpensive mobile cardio-respiratory monitoring system can achieve similar performance to a metabolic cart in estimating rated perceived exertion. Eight adult men volunteered to perform treadmill tests under different conditions. Cardiorespiratory data were collected using a metabolic cart and an instrumented oral-cavity device, as well as their ratings of perceived exertion. Pearson correlation corrected for repeated measurements and stepwise regression analysis were used to observe the relationship between the cardiorespiratory features and the ratings of perceived exertion and determine the proportion of the variance of exertion that could be explained by the measurements. Minute ventilation was found to be the most associated variable to perceived exertion, closely followed by a novel metric called the audio minute volume, which can be collected by the oral-cavity device. A generalised linear model combining minute ventilation, audio minute volume, heart rate and respiration rate accounted for 64% of the variance in perceived exertion, whilst a model with only audio minute volume accounted for 56%. Our study indicates that minute ventilation is key to estimating perceived exertion during indoor running exercises. Audio minute volume was also observed to perform comparably to a lab-based metabolic cart in estimating perceived exertion. This research indicates that mobile techniques offer the potential for real-world data collection of an athlete’s physiological load and estimation of perceived exertion.

Performance of Oral Cavity Sensors: A Systematic Review

“…One study (1/85, 1%) used a microphone embedded in a mouthguard to estimate the perceived exertion of athletes in running exercises. The study reported a normalised root-mean-squared error of 16.20% [ 106 ].…”

Determining the performance of a temperature sensor embedded into a mouthguard

“…The oral monitoring device, based on the device used in ref. [ 15 ], consisted of a flexible design that continuously measures several intra-oral signals (including temperature) at the same time. It was vacuum formed using ethylene-vinyl acetate (EVA).…”

A Case for an Oral Cavity-Based Respiratory Rate Sensor System

“…The clips of continuous breathing were then pre-processed through a series of filters. A 4th order Butterworth band-pass filter of 100Hz-400Hz was applied forward and back on the data to get rid of low and high-frequency noises outside of the range of breathing [3]. Following that, the data were downsampled to 1600Hz.…”

“…It has been shown that breathing frequency, but not heart-rate, has a strong relationship to RPE [2]. Respiratory rate correlates well to physical exertion [3], and can potentially be used to monitor recovery from exertion better than heart rate [4], thus presenting a promising additional on-field physiological metric to help improve the health and well-being of athletes.…”