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McGrath JW, Neville J, Stewart T, Lamb M, Alway P, King M, Cronin J. Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling? Sports Biomech 2023:1-13. [PMID: 37941397 DOI: 10.1080/14763141.2023.2275251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 01/17/2023] [Indexed: 11/10/2023]
Abstract
This study examined whether an inertial measurement unit (IMU) could measure ground reaction force (GRF) during a cricket fast bowling delivery. Eighteen male fast bowlers had IMUs attached to their upper back and bowling wrist. Each participant bowled 36 deliveries, split into three different intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. A force plate was embedded into the bowling crease to measure the ground truth GRF. Three machine learning models were used to estimate GRF from the IMU data. The best results from all models showed a mean absolute percentage error of 22.1% body weights (BW) for vertical and horizontal peak force, 24.1% for vertical impulse, 32.6% and 33.6% for vertical and horizontal loading rates, respectively. The linear support vector machine model had the most consistent results. Although results were similar to other papers that have estimated GRF, the error would likely prevent its use in individual monitoring. However, due to the large differences in raw GRFs between participants, researchers may be able to help identify links among GRF, injury, and performance by categorising values into levels (i.e., low and high).
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Affiliation(s)
- Joseph W McGrath
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
- Manukau Institute of Technology School of Sport, Auckland, New Zealand
- Paramedicine and Emergency Management, School of Health Care Practice, AUT University, Auckland, New Zealand
| | - Jonathon Neville
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
| | - Tom Stewart
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
- Human Potential Centre, AUT University, Auckland, New Zealand
| | - Matt Lamb
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Peter Alway
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
- England & Wales Cricket Board
| | - Mark King
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - John Cronin
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
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McGrath JW, Neville J, Stewart T, Lamb M, Alway P, King M, Cronin J. The relationship between bowling intensity and ground reaction force in cricket pace bowlers. J Sports Sci 2022; 40:1602-1608. [PMID: 35786386 DOI: 10.1080/02640414.2022.2094561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
This study examined the relationship between perceived bowling intensity, ball release speed and ground reaction force (measured by peak force, impulse and loading rate) in male pace bowlers. Twenty participants each bowled 36 deliveries, split evenly across three perceived intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. Peak force and loading rate were significantly different across the three perceived intensity zones in the horizontal and vertical directions (Cohen's d range = 0.14-0.45, p < 0.01). When ball release speed increased, peak force and loading rate also increased in the horizontal and vertical directions (ηp2 = 0.04-0.18, p < 0.01). Lastly, bowling at submaximal intensities (i.e., low - medium) was associated with larger decreases in peak horizontal force (7.9-12.3% decrease), impulse (15.8-21.4%) and loading rate (7.4-12.7%) compared to decreases in ball release speed (5.4-8.3%). This may have implications for bowling strategies implemented during training and matches, particularly for preserving energy and reducing injury risk.
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Affiliation(s)
- Joseph W McGrath
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand.,School of Sport, Manukau Institute of Technology, Auckland, New Zealand.,Paramedicine and Emergency Management, School of Health Care Practice, AUT University, Auckland, New Zealand
| | - Jonathon Neville
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
| | - Tom Stewart
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand.,Human Potential Centre, AUT University, Auckland, New Zealand
| | - Matt Lamb
- School of Sport, Exercise and Health Sciences, Loughborough University, Leicestershire, UK
| | - Peter Alway
- School of Health and Sports Sciences, University of Suffolk, Ipswich, England
| | - Mark King
- School of Sport, Exercise and Health Sciences, Loughborough University, Leicestershire, UK
| | - John Cronin
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
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Human Health Activity Recognition Algorithm in Wireless Sensor Networks Based on Metric Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4204644. [PMID: 35479601 PMCID: PMC9038378 DOI: 10.1155/2022/4204644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 11/17/2022]
Abstract
Wireless sensor network is an ad hoc network with sensing capability. Usually, a large number of sensor nodes are randomly deployed in an unreachable environment or complex area for data collection and transmission, which can realize the perception and monitoring of the target area or specific objects and transmit the obtained data to the remote end of the system. Human health activity recognition algorithm is a hot topic in the field of computer. Based on the small sample problem and the linear indivisibility of real samples encountered in metric learning, this paper proposes a human activity recognition algorithm for wireless sensor networks. Human activity recognition algorithm for wireless sensor networks uses human activity recognition algorithm to solve the singularity of intraclass divergence matrix, so as to reduce the impact of small sample problem. The algorithm maps two different feature spaces to the high-dimensional linearly separable kernel space through the corresponding kernel function, calculates the distance between samples in the two projected feature subspaces to obtain two distance measurement functions, and finally linearly combines them with weights to obtain the final distance measurement function.
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