Design and implementation of real-time rowing simulator (ReTRoS) for high-performance and low-injury — A preliminary work

Safiee, Muhammad Syafoan; Ahmad, Afandi; Tukiran, Zarina; Roslan, Muhammad Faris; Nadzri, Muhammad Muzakkir Mohd

doi:10.1109/iecbes.2016.7843484

Cited by 2 publications

(2 citation statements)

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“…Despite the obvious differences in the biomechanical processes of an athlete when transferring from an ergometer to a scull, it will provide a baseline measurement with golden standard data to validate against. [12] Rowing × × [13] Rowing…”

Section: Study Design and Hardwarementioning

confidence: 99%

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A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

et al. 2019

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Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into many coaches’ training regimes. There is an emerging trend to use inertial sensors for performance monitoring in rowing; however, the use and selection of the sensor used has not been appropriately reviewed. Previous literature assessed the sampling frequency, position, and fixing of the sensor; however, properties such as the sensor operating ranges, data processing algorithms, and validation technology are left unevaluated. To address this gap, a systematic literature review on rowing performance monitoring using inertial-magnetic sensors was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominantly used for measuring stroke quality and the sensors were used to instrument equipment rather than the athlete. The methodology for both selecting and implementing technology appeared ad hoc, with no guidelines for appropriate analysis of the results. This review summarizes a framework of best practice for selecting and implementing inertial sensor technology for monitoring rowing performance. It is envisaged that this review will act as a guide for future research into applying technology to rowing.

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Section: Study Design and Hardwarementioning

confidence: 99%

“…Signal processing algorithms used on data, KNN (K-nearest Neighbours); DTW (Dynamic Time Warping); SVM (Support Vector Machine). [12] Rowing × × × [13] Rowing × × × [14] Rowing × × [15] Rowing × × × [16] Rowing…”

Section: Study Design and Hardwarementioning

confidence: 99%