Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

Abstract

Inertial measurement units (IMUs) are becoming increasingly popular in activity classification and workload measurement in sport. This systematic literature review focuses on upper body activity classification in court or field-based sports. The aim of this paper is to provide sport scientists and coaches with an overview of the past research in this area, as well as the processes and challenges involved in activity classification. The SPORTDiscus, PubMed and Scopus databases were searched, resulting in 20 articles. Both manually defined algorithms and machine learning approaches have been used to classify IMU data with varying degrees of success. Manually defined algorithms may offer simplicity and reduced computational demand, whereas machine learning may be beneficial for complex classification problems. Inter-study results show that no one machine learning model is best for activity classification; differences in sensor placement, IMU specification and pre-processing decisions can all affect model performance. Accurate classification of sporting activities could benefit players, coaches and team medical personnel by providing an objective estimate of workload. This could help to prevent injuries, enhance performance and provide valuable data to coaching staff.

Validity and Reliability of the Inertial Measurement Unit for Barbell Velocity Assessments: A Systematic Review

“…While several systematic reviews have been published about the use of IMUs [ 50 , 51 , 52 , 53 ], no systematic review has summarized the validity and reliability levels of different IMU models for measuring barbell velocity. Considering the importance of the accuracy and precision level of determining barbell velocity in providing adequate prescriptions of resistance training, the aim of the present systematic review was twofold: (1) to identify and summarize studies that have examined the validity of wearable wireless IMU for measuring barbell velocity, and (2) to identify and summarize studies that have examined the reliability of IMUs for measuring barbell velocity.…”

Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?

“…IMUs are increasingly used as a reliable and accurate method of monitoring external workloads [ 15 , 16 ]. They are light, inexpensive, and not restricted to laboratory spaces or indoor environments.…”

“…However, only IMUs with a 16 g measurement range were utilized, and not IMUs with a higher range such as 200 g (which are more expensive compared to 16 g). A higher threshold may be more sensitive in detecting different throws and estimating ball velocity [ 16 ].…”

One Size Doesn't Fit All: Supervised Machine Learning Classification in Athlete-Monitoring

“…The advent of microelectromechanical systems (MEMS) has facilitated the use of wearable inertial measurement unit sensors (IMUs) for performance monitoring in elite sport [13], [14]. As of 2018, two extensive systematic literature reviews explored the technologies, data collection procedures, feature engineering, validation methods and overall performance of machine learning models implemented in sport-specific movement classification tasks [1], [2]. Cust et al [1] reviewed a total of 52 manuscripts and included both IMUs and vison-based classification algorithms.…”

“…Cust et al [1] reviewed a total of 52 manuscripts and included both IMUs and vison-based classification algorithms. McGrath et al [2] reviewed a total of 20 manuscript, however, only included records reporting IMUs classification algorithms. In [1] 15/52 (29%) papers used a leave-one (or more)-subject-out (LOSO) model validation, while 22/52 (42%) trained and tested on non-independent data (crossvalidation or train/test split) and 15/52 (29%) did not clarify a validation method.…”