OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM).
METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status.
RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent.
CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.
METHOD: Two lifting loads were considered in this study: 1 kg and 5 kg. Each subject adjusted his frequency of lifting using a psychophysical approach. The subjects were instructed to perform combined MMH task as fast as they could over a period of 45 minutes without exhausting themselves or becoming overheated. The physiological response energy expenditure was recorded during the experimental sessions. The ratings of perceived exertion (RPE) for four body parts (forearms, upper arm, lower back and entire body) were recorded after the subjects had completed the instructed task.
RESULTS: The mean frequencies of the MMH task had been 6.8 and 5.5 cycles/minute for lifting load of 1 and 5 kg, respectively, while the mean energy expenditure values were 4.16 and 5.62 kcal/min for 1 and 5 kg load, respectively. These displayed a significant difference in the Maximum Acceptable Frequency of Lift (MAFL) between the two loads, energy expenditure and RPE (p < 0.05) whereby the subjects appeared to work harder physiologically for heavier load.
CONCLUSION: It can be concluded that it is significant to assess physiological response and RPE in determining the maximum acceptable lifting frequency at varied levels of load weight. The findings retrieved in this study can aid in designing tasks that do not exceed the capacity of workers in order to minimise the risk of WRMSDs.
MATERIALS AND METHODS: A quasi-experimental study employing a one-group pre- and post-intervention was carried out involving 142 male firefighter recruits from a Fire and Rescue Academy in Malaysia. Various aspects of physical fitness changes, including speed, agility, and coordination (SAC), muscle strength, endurance, and power, were evaluated at baseline (Week 1) and upon completion of the first phase (Week 5). Changes in health parameters, such as blood pressure, resting heart rate, body weight, muscle mass, body fat percentage, and body mass index, were also assessed. A paired sample t-test was conducted with the significance level set at 0.05. The magnitude of changes was assessed using the following criteria: values of 0.3 were considered a small effect size, 0.5 indicated a moderate effect size, and 0.8 signified a large effect size.
RESULTS: Upon completion of the first phase of the physical training regimen, there was a statistically significant improvement in cardiorespiratory fitness, with a mean increment of VO2max was 9 mL/kg/min (95% CI: 8.33, 9.58, p<0.001, large effect size of 2.40). Both pre-and postintervention assessments of abdominal and upper body muscle strength and endurance showed statistically significant improvement with the mean difference of 11 situps (95%CI: 10.08, 12.01; p<0.001, large effect size of 1.89) and 1.5 pull-ups (95%CI: 1.07, 1.86; p<0.001, moderate effect size of 0.63), respectively. Health parameters showed similar, except for systolic BP (SBP). There was a small increment in recruits' SBP following the 4-week training period with a mean difference of 4.3 mmHg (95%CI: 2.37, 6.24; effect size = 0.37, p<0.001).
CONCLUSION: The first phase of the newly introduced fourweek physical training regimen has proven effective in enhancing cardiorespiratory fitness, as well as abdominal and upper body muscle strength and endurance. Additionally, the regimen has positively influenced several health parameters, except for systolic blood pressure. The observed increase in average systolic blood pressure indicates a necessity for continuous monitoring at the academy to address this issue effectively. confirm our findings.
RECENT FINDINGS: Methods of acquisition and analysis of BPV and cognitive measurements and their relationship were extracted from selected articles. Of 656 studies identified, 53 articles were selected. Twenty-five evaluated long-term (LTBPV), nine mid-term (MTBPV), 12 short-term (STBPV) and nine very short-term BPV (VSTBPV) with conflicting findings on the relationship between BPV and cognition. Variations existed in devices, period and procedure for acquisition. The studies also utilized a wide range of methods of BPV calculation. Thirteen cognitive assessment tools were used to measure global cognition or domain functions which were influenced by the population of interest. The interpretation of available studies was hence limited by heterogeneity. There is an urgent need for standardization of BPV assessments to streamline research on BPV and cognition. Future studies should also establish whether BPV could be a potential modifiable risk factor for cognitive decline, as well as a marker for treatment response.