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Sensors for Exercise and Sport Activities: From Health Promotion to Sports Performance Ⅱ

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 5264

Special Issue Editor

Prof. Dr. Justin Jeon

E-Mail Website
Guest Editor
Department of Sport Industry Studies, Yonsei University, Seoul 03722, Korea
Interests: physical activity; exercise physiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During exercise and sport activity, massive joint and muscle movements occur which result in hormonal, cardiopulmonary, and metabolic response. Repetitive participation of exercise and sport activity further results in the improvement of neuromuscular coordination, as well as increases in muscle mass, strength, power, and cardiopulmonary fitness. Traditionally, the assessment of physical activity and cardiopulmonary and musculoskeletal fitness require very expensive devices, such as metabolic carts, dynamometers (Cybex and Contrax), and accelerometers (Actigraph). The measurement of movement, physical activity, and fitness levels provides valuable information to predict a person’s health, physical function, and risk of different musculoskeletal and metabolic diseases (e.g., low back pain, osteoarthritis, shoulder joint problems, diabetes, cardiovascular disease, and even cancer). Furthermore, these types of information also provide postural, kinematic, and biomechanical information during sport activity, which may predict the sports performance of participants. On the other hand, the advances in artificial intelligence (AI) and machine learning (ML) provide new ways to interact and gain insights into the captured data, which enable the analysis, segmentation, classification, and recognition of human posture/movement and cardiopulmonary response to exercise and physical and sport activities.

This Special Issue will cover a wide range of topics around exercise and physical and sport activities, including new sensor technologies to capture movement, musculoskeletal/cardiopulmonary response to exercise/physical and sport activities, the application prospects of sensors in exercise and sport activities, and new algorithmic approaches to derive, analyze, and recognize exercise and physical activity sensor data.

Prof. Dr. Justin Jeon
Guest Editor

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Published Papers (2 papers)

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Research

12 pages, 457 KiB  
Article
Intelligent Estimation of Exercise Induced Energy Expenditure Including Excess Post-Exercise Oxygen Consumption (EPOC) with Different Exercise Intensity
by Junhyung Moon, Minsuk Oh, Soljee Kim, Kyoungwoo Lee, Junga Lee, Yoonkyung Song and Justin Y. Jeon
Sensors 2023, 23(22), 9235; https://doi.org/10.3390/s23229235 - 16 Nov 2023
Cited by 3 | Viewed by 1913
Abstract
The limited availability of calorimetry systems for estimating human energy expenditure (EE) while conducting exercise has prompted the development of wearable sensors utilizing readily accessible methods. We designed an energy expenditure estimation method which considers the energy consumed during the exercise, as well [...] Read more.
The limited availability of calorimetry systems for estimating human energy expenditure (EE) while conducting exercise has prompted the development of wearable sensors utilizing readily accessible methods. We designed an energy expenditure estimation method which considers the energy consumed during the exercise, as well as the excess post-exercise oxygen consumption (EPOC) using machine learning algorithms. Thirty-two healthy adults (mean age = 28.2 years; 11 females) participated in 20 min of aerobic exercise sessions (low intensity = 40% of maximal oxygen uptake [VO2 max], high intensity = 70% of VO2 max). The physical characteristics, exercise intensity, and the heart rate data monitored from the beginning of the exercise sessions to where the participants’ metabolic rate returned to an idle state were used in the EE estimation models. Our proposed estimation shows up to 0.976 correlation between estimated energy expenditure and ground truth (root mean square error: 0.624 kcal/min). In conclusion, our study introduces a highly accurate method for estimating human energy expenditure during exercise using wearable sensors and machine learning. The achieved correlation up to 0.976 with ground truth values underscores its potential for widespread use in fitness, healthcare, and sports performance monitoring. Full article
(This article belongs to the Special Issue Sensors for Exercise and Sport Activities: From Health Promotion to Sports Performance Ⅱ)
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14 pages, 5353 KiB  
Article
Using a Contemporary Portable Metabolic Gas Exchange System for Assessing Energy Expenditure: A Validity and Reliability Study
by Holly L. McClung, William J. Tharion, Leila A. Walker, Maxwell N. Rome, Reed W. Hoyt and David P. Looney
Sensors 2023, 23(5), 2472; https://doi.org/10.3390/s23052472 - 23 Feb 2023
Cited by 3 | Viewed by 2735
Abstract
There are several methods available to assess energy expenditure, all associated with inherent pros and cons that must be adequately considered for use in specific environments and populations. A requirement of all methods is that they must be valid and reliable in their [...] Read more.
There are several methods available to assess energy expenditure, all associated with inherent pros and cons that must be adequately considered for use in specific environments and populations. A requirement of all methods is that they must be valid and reliable in their capability to accurately measure oxygen consumption (VO2) and carbon dioxide production (VCO2). The purpose of this study was to evaluate the reliability and validity of the mobile CO2/O2 Breath and Respiration Analyzer (COBRA) relative to a criterion system (Parvomedics TrueOne 2400®, PARVO) with additional measurements to compare the COBRA to a portable system (Vyaire Medical, Oxycon Mobile®, OXY). Fourteen volunteers with a mean of 24 years old, body weight of 76 kg, and a VO2peak of 3.8 L∙min−1 performed four repeated trials of progressive exercises. Simultaneous steady-state measurements of VO2, VCO2, and minute ventilation (VE) by the COBRA/PARVO and OXY systems were conducted at rest, while walking (23–36% VO2peak), jogging (49–67% VO2peak), and running (60–76% VO2peak). Data collection was randomized by the order of system tested (COBRA/PARVO and OXY) and was standardized to maintain work intensity (rest to run) progression across study trials and days (two trials/day over two days). Systematic bias was examined to assess the accuracy of the COBRA to PARVO and OXY to PARVO across work intensities. Intra- and inter-unit variability were assessed with interclass correlation coefficients (ICC) and a 95% limit of agreement intervals. The COBRA and PARVO produced similar measures for VO2 (Bias ± SD, 0.01 ± 0.13 L·min−1; 95% LoA, (−0.24, 0.27 L·min−1); R2 = 0.982), VCO2 (0.06 ± 0.13 L·min−1; (−0.19, 0.31 L·min−1); R2 = 0.982), VE (2.07 ± 2.76 L·min−1; (−3.35, 7.49 L·min−1); R2 = 0.991) across work intensities. There was a linear bias across both the COBRA and OXY with increased work intensity. The coefficient of variation for the COBRA ranged from 7 to 9% across measures for VO2, VCO2, and VE. COBRA was reliable across measurements for VO2 (ICC = 0.825; 0.951), VCO2 (ICC = 0.785; 0.876), and VE (ICC = 0.857; 0.945) for intra-unit reliability, respectively. The COBRA is an accurate and reliable mobile system for measuring gas exchange at rest and across a range of work intensities. Full article
(This article belongs to the Special Issue Sensors for Exercise and Sport Activities: From Health Promotion to Sports Performance Ⅱ)
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