Investigation of Multiparameter Trends and Anthropometric Measurements for Cardiorespiratory Fitness Assessment Among UTM Staff

Cardiorespiratory fitness (CRF) is known to reduce metabolic-related diseases like cardiovascular diseases (CVD), obesity, hypertension, and type II diabetes. On the other hand, the gold standard to measure CRF is by measuring maximal oxygen consumption, VO2 max over the years. This study is perform...

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Published inIOP conference series. Materials Science and Engineering Vol. 884; no. 1; pp. 12002 - 12011
Main Authors Muralitharan, Latha Nair, Zahari, Wan Nor Syuhada Wan, Rosli, Nor Aziyatul Izni Mohd, Ismail, Norjihada Izzah, Malarvili, MB, Kadir, Mohammed Rafiq Abdul
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.07.2020
Subjects
Anthropometry
Body composition
Body size
Decision trees
Fitness
Heart rate
Hypertension
Maximum oxygen consumption
Muscles
Oxygen consumption
Parameter identification
Trends
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Summary:Cardiorespiratory fitness (CRF) is known to reduce metabolic-related diseases like cardiovascular diseases (CVD), obesity, hypertension, and type II diabetes. On the other hand, the gold standard to measure CRF is by measuring maximal oxygen consumption, VO2 max over the years. This study is performed to identify parameters that influence CRF without solely relying on invasive features such as VO2 max. A number of 31 UTM staff aged between 30 and 40 years old have participated in this study with 17 female subjects and 14 male subjects. Anthropometric measurements are obtained by direct measurement and body composition analysis using a body composition monitor. Multiparameter trend measurements were obtained from vital sign monitors at rest. Single feature analysis was performed in terms of accuracy, specificity and sensitivity to identify which feature influences CRF the most. The features collected are body mass index (BMI), body fat (BF), muscle mass (MM), bone density (BD), waist circumference (WC), resting heart rate (RHR), resting systolic blood pressure (RSBP), forced expiratory volume in one second (FEV1), and recovery trend heart rate (RecHR). Next, all these features were validated using Naïve Bayes (NB) and Decision Tree (DT) classifiers. Finally, six features which are BF, BM, BD, RHR, RSBP and FEV1, with accuracy more than 70% were selected and identified as the features which influence CRF of UTM staff.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/884/1/012002