Design of a Wireless Sensor System with the Algorithms of Heart Rate and Agility Index for Athlete Evaluation

Li, Meina; Kim, Youn Tae

doi:10.3390/s17102373

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…It is the result of a multidisciplinary development that combines sensor technology, wireless transmission technology, distributed information technology, and embedded technology. Processing technology can collect, process, and transmit object information to objects that need to be sensed [1][2][3]. The WSN has a wide range of applications, from environmental monitoring, industrial processing control to military applications; you can see the WSN [4].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Zhang³

2021

Journal of Sensors

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According to the development needs of wireless sensor networks, this paper uses the combination of embedded system and wireless sensor network technology to design a network node platform. This platform is equipped with a sports training sensor module to measure the physiological indicators of the ward in real time. The network node sends the collected physiological parameters to a remote monitoring center in real time. First, according to the generation mechanism of the physiological index signal and the characteristics of the physiological index signal, the wireless sensor network analysis and processing method are used to denoise the physiological index signal, and the wireless sensor network package is used to extract the characteristics of the physiological index, indicating different types of respiration. The energy characteristics of the sound physiological index signals are different, which verifies the feasibility of the independent component analysis method for separating the physiological index and the physiological index signal of the heart sound. Secondly, the hardware system of physiological index signal acquisition is designed, and the selection principle of the hardware unit is introduced. At the same time, the system structure of the monitor is designed, and then, the wireless sensor network sensor node is researched, the hardware of the wearable monitor system is designed, and the hardware architecture and working mode based on the single-chip MSP430F149 are given. Finally, the wireless hardware platform includes the following main modules: sensor part, preprocessing circuit module, microprocessing module based on MSP430 low power consumption, wireless transceiver module based on RF chip CC2420, and power supply unit used to provide energy.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Zhang³

2021

Journal of Sensors

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“…Heart rate for athletes is an instrument to determine and monitor their daily effort for every training and how hard their body is being trained. A slower increase in heart rate while training sessions act as proof that athletes are physically fit (Aubert et al, 2003; Li and Kim, 2017). Although a slower heart rate is preferable, the small differences between the comparators have indicated that WPS is capable and comparable to the comparators.…”

Section: Discussionmentioning

confidence: 99%

Efficacy and Safety of Whey Protein Supplements on Vital Sign and Physical Performance Among Athletes: A Network Meta-Analysis

et al. 2019

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Introduction: Athletes train physically to reach beyond their potential maximum aerobic threshold. Whey protein supplements (WPS) are often used in conjunction with physiotherapy and psychotherapy to regain better vital sign and physical performances. This review aimed to explore the clinical evidence on the efficacy and safety of WPS in sports performance and recovery among athletes. Methodology: A comprehensive literature search was performed to identify relevant randomized control trials (RCTs) that investigated the efficacy and safety of WPS on the vital sign and physical performance among athletes. The Cochrane Risk of Bias (ROB) Assessment tools were used to assess the quality of the studies. Meta-analysis was conducted using the frequentist model with STATA version 14.2®. Results: A total of 333,257 research articles were identified out of which 20 RCTs were included for qualitative synthesis and network meta-analysis with 351 participants. Among the studies, 7 had low ROB and 3 RCTs had high ROB. Of these 20 trials, 16 trials were randomized clinical trials which compared whey protein supplements (WPS) with various comparators i.e., L-alanine, bovine colostrum, carbohydrate, casein, leucine, maltodextrin, rice, protein + caffeine were compared with placebo. Analysis from the pairwise meta-analysis revealed that for respiratory exchange ratio (RER) WPS was found to be significantly improving compared to maltodextrin (WMD = 0.012; 95%CI = 0.001, 0.023). Similarity to RPE (Rate Perceived Exertion), slight difference between WPS and the comparators, however, when the estimation was favorable to the comparators, there was moderate-high heterogeneity. For VO 2max , high heterogeneity appeared when WPS compared to maltodextrin with the I 2 = 97.8% (WMD = 4.064; 95% CI = −4.230, 12.359), meanwhile bovine colostrum (WMD = −2.658; 95%CI = −6.180, 0.865) only comparator that was better than WPS. According to the estimated effect of the supplements on physical performance outcome results, maximum power (8 studies, 185 athletes), highest ranked was bovine colostrum (SUCRA = 70.7%) and the lowest ranked was placebo (SUCRA = 17.9%), yet all insignificant. Then again, on average power (nine studies, 187 athletes), WPS was the highest ranked (SUCRA = 75.4 %) about −112.00 watt (−187.91, −36.08) and most of the estimations were significant. Body mass was reported in 10 studies (171 athletes), carbohydrate may be at the highest ranked (SUCRA = 66.9%) but it is insignificant. Thought the second highest ranked was WPS (SUCRA = 64.7%) and it is significant (WMD = −6.89 kg; CI = −8.24, −5.54). Conclusion: The findings of this review support the efficacy and safety of WPS as an ergogenic aid on athletes' sports performance and recovery. The overall quality of clinical evidence was found to be valid and reliable from the comprehensive search strategy and ROB assessment....

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“…(1) HR Extraction . HR was defined by calculating the R - R duration/interval time among QRS complexes of consecutive ECG waveforms within 1 minute intervals, where R was the first upward deflection wave after the P wave, the QRS complex was a series of waveforms following the P wave in the ECG waveforms, and the R-R interval was the elapsed time between two consecutive R waves [25, 26].…”

Section: Methodsmentioning

confidence: 99%

“…The autoalarm feature of the proposed system was based on the threshold value determination of monitored physiological parameters, such as HR, which reflects certain cases of ECG abnormalities and is considered as an indicator of a motion function [28]. In this study, HR was extracted and calculated from ECG waveforms on the basis of an algorithm proposed in [25], and the HR threshold values were determined based on works [13, 26].…”

Section: Methodsmentioning

confidence: 99%

Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone

Al-Naggar

Al-Hammadi

Al-Fusail

et al. 2019

Journal of Healthcare Engineering

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Background Utilization of the widely used wearable sensor and smartphone technology for remote monitoring represents a healthcare breakthrough. This study aims to design a remote real-time monitoring system for multiple physiological parameters (electrocardiogram, heart rate, respiratory rate, blood oxygen saturation, and temperature) based on smartphones, considering high performance, autoalarm generation, warning transmission, and security through more than one method. Methods Data on monitoring parameters were acquired by the integrated circuits of wearable sensors and collected by an Arduino Mega 250 R3. The collected data were transmitted via a Wi-Fi interface to a smartphone. A patient application was developed to analyze, process, and display the data in numerical and graphical forms. The abnormality threshold values of parameters were identified and analyzed to generate an autoalarm in the system and transmitted with data to a doctor application via a third-generation (3G) mobile network and Wi-Fi. The performance of the proposed system was verified and evaluated. The proposed system was designed to meet main (sensing, processing, displaying, real-time transmission, autoalarm generation, and threshold value identification) and auxiliary requirements (compatibility, comfort, low power consumption and cost, small size, and suitability for ambulatory applications). Results System performance is reliable, with a sufficient average accuracy measurement (99.26%). The system demonstrates an average time delay of 14 s in transmitting data to a doctor application via Wi-Fi compared with an average time of 68 s via a 3G mobile network. The proposed system achieves low power consumption against time (4 h 21 m 30 s) and the main and auxiliary requirements for remotely monitoring multiple parameters simultaneously with secure data. Conclusions The proposed system can offer economic benefits for remotely monitoring patients living alone or in rural areas, thereby improving medical services, if manufactured in large quantities.

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Design of a Wireless Sensor System with the Algorithms of Heart Rate and Agility Index for Athlete Evaluation

Cited by 15 publications

References 30 publications

[Retracted] Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

[Retracted] Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Efficacy and Safety of Whey Protein Supplements on Vital Sign and Physical Performance Among Athletes: A Network Meta-Analysis

Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone

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