Better position for the wearable sensor to monitor badminton sport training loads

Liu, Tsung‐Han; Chen, Wei-Han; Shih, Yo; Lin, Yi-Chih; Yu, Chia‐Jung; Shiang, Tzyy-Yuang

doi:10.1080/14763141.2021.1875033

Cited by 9 publications

(15 citation statements)

References 32 publications

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“…Although studies that quantify these loads are mostly limited to match performance or selected training periods (Bartlett et al, 2017;Simpson et al, 2020;Taylor et al, 2020), the loads required in various sportspecific practices are equally important. Liu found that the player's lower back is an ideal location for a wearable sensor capable of monitoring overall badminton external loads (Liu et al, 2021). Trivial to moderate relationships have been found between internal and external match loads in male, singles badminton players (Abdullahi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Energy Contributions and Workloads in Male and Female Badminton Players During Games Versus Repetitive Practices

Liu

Chen

et al. 2021

Front. Physiol.

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PurposeThe aim of this study was to compare the energy contributions and workloads in men and women during badminton matches versus frequently used multi-ball smash practices.MethodsFourteen badminton players performed one badminton singles game and one session of smashing practice on separate days. The energy contributions were examined in terms of each individual’s three energy systems and substrate oxidation, while workloads included heart rate (HR), Player Load (PL), accelerations, decelerations, changes of direction, and jumps.Results(1) During games, male players exhibited higher adenosine triphosphate–phosphocreatine system contribution (EPCr, kJ) (p = 0.008) and average rate of carbohydrate oxidation (RCHO, g/min) (p = 0.044) than female players, while female players showed greater absolute PL (p = 0.029) and more accelerations (p = 0.005) than male players. Furthermore, players who lost performed higher relative PL (p = 0.017) than those who won. (2) Higher energy system contributions, including EPCr (kJ) (p = 0.028), EHLa (kJ) (p = 0.024), EAer (kJ) (p = 0.012), ETot (kJ) (p = 0.007), and RCHO (g/min) (p = 0.0002), were seen in male players during repetitive spike practices. Male players also made greater number of jumps (p = 0.0002). (3) Players exhibited higher aerobic energy contribution (p < 0.001), mean HR (p = 0.002), and HRmax (p = 0.029) during games, while exhibiting greater anaerobic energy contribution (p < 0.001) and relative PL (p = 0.001) during repetitive practices.ConclusionThe similarities between male and female badminton players in proportional use of the three energy systems during games and repetitive spike training indicate similar relative energy demands for both genders. However, considering the need for higher aerobic capacity in competition, it might be advisable to design appropriate work:rest ratios for repetitive practices in daily training.

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Section: Introductionmentioning

confidence: 99%

Comparison of Energy Contributions and Workloads in Male and Female Badminton Players During Games Versus Repetitive Practices

Liu

Chen

et al. 2021

Front. Physiol.

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“…Specifically, PL is calculated as the square root of the sum of the squared rates of change in acceleration between each moment of a training session in each movement axis (x, y, and z), and it is represented in arbitrary units [ 12 , 14 , 15 ]. Previous research has confirmed the validity of PL as a training load metric [ 8 , 16 , 17 , 18 ]. However, most of the above studies put an accelerometer on the upper back to calculate PL.…”

Section: Introductionmentioning

confidence: 83%

“…Heart rate (HR) -based metrics are a common method to quantify internal intensity, in which the percentage of heart rate reserve (%HRR) is currently the most recommended for the determination of the aerobic exercise intensity [ 7 ]. Acceleration-based intensity metrics (AIMs) are a common method to quantify external intensity [ 5 ], provide objective information for coaches and athletes to quantify exercise intensity and volume to enhance training efficiency [ 8 ]. Two different AIM commonly used to estimate external intensity are Player Load per minute (PL/min) and mean amplitude deviation (MAD).…”

Section: Introductionmentioning

confidence: 99%

“…Player Load (PL) is a commonly used metric in the field and court sports to quantify external training load [ 8 , 9 , 10 , 11 , 12 , 13 ] and to derive an average external intensity measurement (PL/min) [ 9 ]. PL is a function of the sum of changes in acceleration (ΣΔ acceleration), as measured by triaxial accelerometers [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the above studies put an accelerometer on the upper back to calculate PL. Liu et al [ 8 ] found that PLs calculated from accelerometers at different body locations varied substantially in the strength of correlation to HR-based training impulse (TRIMP) (racket and non-racket hands: r = 0.570, 0.796; lower back: r = 0.843; racket and non-racket legs: r = 0.728, 0.836). Therefore, it is necessary to clarify the combined impact on sensor-position and movement type on PL calculations and determine the ideal sensor-positions for evaluating the exercise intensity in different sports.…”

Section: Introductionmentioning

confidence: 99%

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Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports

Chen

Chiang

Fiolo

et al. 2022

Sensors

Self Cite

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This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds). Fourteen participants (age: 24.9 ± 2.4 years) wore a chest strap HR monitor and placed three accelerometers at the left wrist (non-dominant), trunk, and right shank, respectively. The %HRR and two different AIMs (Player Load per minute [PL/min] and mean amplitude deviation [MAD]) during exercise were calculated. During running, both AIMs at the shank and PL at the wrist had strong correlations (r = 0.777–0.778) with %HRR; while other combinations were negligible to moderate (r = 0.065–0.451). For basketball, both AIMs at the shank had stronger correlations (r = 0.604–0.628) with %HRR than at wrist (r = 0.536–0.603) and trunk (r = 0.403–0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (r = 0.782–0.793) with %HRR than those at wrist (r = 0.587–0.621) and MAD at trunk (r = 0.608) and trunk (r = 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination.

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Self-powered intelligent badminton racket for machine learning-enhanced real-time training monitoring

Yuan,

Xue,

Liu

et al. 2024

Nano Energy

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Better position for the wearable sensor to monitor badminton sport training loads

Cited by 9 publications

References 32 publications

Comparison of Energy Contributions and Workloads in Male and Female Badminton Players During Games Versus Repetitive Practices

Comparison of Energy Contributions and Workloads in Male and Female Badminton Players During Games Versus Repetitive Practices

Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports

Self-powered intelligent badminton racket for machine learning-enhanced real-time training monitoring

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