Workload Management System for Cricketers

Cricketers are dynamic players in the field and hence more vulnerable to injuries. The injury rate of Sri Lankan cricketers is very high, resulting in their careers being shortened. Therefore, we established a workload management system for cricketers to resolve this issue with wearable Inertial Mea...

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Bibliographic Details
Published in2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2021; pp. 7292 - 7295
Main Authors Ekanayaka, E. M. S. B., Gunawardhana, A. A. S., Mihirani, M. B., Silva, P., Prins, N. W.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.11.2021
Subjects
Activity recognition
Athletes
Athletic Injuries
Biology
Convolutional Neural Network (CNN)
Convolutional neural networks
Engineering profession
Human Activity Recognition (HAR)
Humans
Inertial Measurement Unit (IMU)
Injuries
Measurement units
Neural networks
Workload
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Summary:Cricketers are dynamic players in the field and hence more vulnerable to injuries. The injury rate of Sri Lankan cricketers is very high, resulting in their careers being shortened. Therefore, we established a workload management system for cricketers to resolve this issue with wearable Inertial Measurement Unit (IMU) sensors mounted on their bodies. In order to mitigate their accidents, we evaluated kinds of the activities performed by an athlete using Convolutional Neural Network (CNN) and computed the workload parameters after the session. The expected results of our project were to develop a system to collect and analyze the critical workload parameters of cricketers and showcase results in a user-friendly manner.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9631106