William P Rey
ABSTRACT
This study presents the development and testing of a precise cockfighting training aid leveraging a modified impact sensor microphone. The research aimed to enhance cockfighting practices by providing trainers with a reliable tool to optimize rooster capabilities. The device, integrated with the SenSabong App, accurately recorded roosters' hits, distinguishing between fight-related and incidental impacts. Rigorous testing confirmed the device's reliability and effectiveness in improving rooster performance with recommended training procedures. The findings contribute to ethical and effective cockfighting training, representing a crucial step toward advancing the sport. Recommendations include integrating wireless connectivity for enhanced user experiences and incorporating gyroscope and accelerometer technologies for precise and real-time training feedback.
- Michael Woldu and Konstantinos Sosopoulos , “IoT Smart Athletics Boxing glove sensors implementing Machine Learning for an integrated training solution”, Kristianstad University, Sweden.Google Scholar
- Athleteintelligence, “”Improve your fight with wearable tech for boxing. Athlete Intelligence”, Retrieved April 18, 2022, from https://www.athleteintelligence.com/improve-your-fight-with-wearable-tech-for-boxing/, 2020.Google Scholar
- Adva, & Rachel, “Unleash your inner fighter with one of these boxing trackers. Gadgets & Wearables”, Retrieved April 18, 2022, from https://gadgetsandwearables.com/2021/02/02/best-boxing-trackers/, February 9, 2021.Google Scholar
- Ryan Chambers, Tim J. Gabbett, Michael H. Cole, and Adam Beard, “The Use of Wearable Microsensors to Quantify Sport-Specific Movements”, Sports Med. 2015 Jul;45(7):1065-81. doi: 10.1007/s40279-015-0332-9. https://pubmed.ncbi.nlm.nih.gov/25834998/, 2015.Google ScholarCross Ref
- Manabu Morita; Kajiro Watanabe; Kazuyuki Kobayashi; Yousuke Kurihara, “Boxing punch analysis using 3D gyro sensor”, SICE Annual Conference 2011. https://ieeexplore.ieee.org/abstract/document/6060500, 2011.Google Scholar
- S. Chadli, N. Ababou, & A. Ababou, “A new instrument for punch analysis in boxing”. Procedia Engineering. Retrieved April 18, 2022, from https://www.sciencedirect.com/science/article/pii/S187770581400589X, June 19, 2014.Google Scholar
- K. Sosopoulos, & M. Woldu, “IoT smart athletics: Boxing glove sensors implementing machine learning for an integrated training solution”, Digitala Vetenskapliga Arkivet. Retrieved June 8, 2021, from https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1561033&dswid=-867, June 5, 2021.Google Scholar
- S. Kumari, S.K. Yadav, “Development of IOT-based Smart Animal Health Monitoring System using Raspberry Pi”. SSRN. Retrieved April 18, 2022, from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3315327, January 28, 2019.Google Scholar
- J. Tiete, F. Domínguez, B.D. Silva, L. Segers, K. Steenhaut, & A. Touhafi, “SoundCompass: A distributed MEMS microphone array-based sensor for sound source localization”. MDPI. https://www.mdpi.com/1424-8220/14/2/1918. January 23, 2017.Google Scholar
- M. W. D. Nielsen, R. Butler, A. Rhead, “Smart Impact Sensors: Microphone Arrays and Machine Learning Techniques for Impact Detection”, ResearchGate. https://www.researchgate.net/publication/327947076_Smart_Impact_Sensors_Microphone_Arrays_and_Machine_Learning_Techniques_for_Impact_Detection. 2018.Google Scholar
- Wang, “Using Microphone as an Impact Sensor for Detection of Stone Cutting Tools Wear””, Research Gate. https://www.researchgate.net/publication/305687140_Using_microphone_as_an_impact_sensor_for_detection_of_stone_cutting_tools_wear. 2017Google Scholar
- L. Zhang, Y. Cao, Z. Liu, and L. Cheng, "Impact event detection and classification based on microphone sensor array," Mechanical Systems and Signal Processing, Vol. 100, pp. 179-194, April 2018.Google Scholar
- V. M. Jadhav, & R. Dahiya, “Microphone-Based Impact Sensor for Dynamic Analysis of Composite Plates”Google Scholar
- A. Dundes, “Cockfight a casebook”. University of Wisconsin Press. 2014.Google Scholar
- G. Foster, “Cockfighting: The social structure of a deviant subculture”. TopSCHOLAR®. Retrieved April 18, 2022, from https://digitalcommons.wku.edu/theses/2349/. 2014.Google Scholar
- Lee Perry-Gal, Adi Erlich, Ayelet Gilboa, Guy Bar-Oz, “Earliest economic exploitation of chicken outside East Asia: Evidence from the hellenistic southern levant”. Proceedings of the National Academy of Sciences of the United States of America. Retrieved April 18, 2022, from https://pubmed.ncbi.nlm.nih.gov/26195775/. 2015.Google Scholar
- C. Geertz, “Deep play: Notes on the Balinese Cockfight”. SpringerLink. Retrieved April 18, 2022, from https://link.springer.com/chapter/10.1007/978-1-349-62965-7_10. C. January 1, 1970.Google Scholar
- A. Jovanovski, B. Stappenbelt, “Measuring the boxing punch: Development and calibration of a non-embedded in-glove piezo-resistive sensor”. MDPI. Retrieved April 18, 2022, from https://doi.org/10.3390/proceedings2020049013. June 15, 2020.Google ScholarCross Ref
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