*4.6. Future Recommendations*

This review makes it known that stroke quality and instrumented material metrics are the most frequently assessed performance feature in rowing using inertial sensors. This is reflected by the sensor placements extracted from the included records; the majority of records instrumented a piece of rowing equipment. Future research should focus on instrumenting the rowing athlete as well as the equipment and finding an interrelationship between the two. This will help coaches identify faults within the rowing system as a whole.

Machine learning, neural network and AI algorithms are gaining momentum in the sport data field. This systematic review revealed that only four of the 36 records (4/36—11.1%) implemented machine learning algorithms in rowing. Moreover, only two (2/36—5.6%) transformed their data into the frequency domain. Further exploration of these two data processing techniques within the rowing technology field is warranted. Numerous included manuscripts used filtering/windowing processing techniques on their signal data. The majority of filters were used for noise removal and a minority used advanced sensor fusion techniques to calculate values of orientation. Using advanced sensor algorithms such as orientation filters can obtain more insightful metrics about the athlete and/or equipment they are using in terms of Euler Angles and Quaternions, allowing improved information quality and a more detailed assessment of performance.

The highest accelerometer operating range and sample rate reported were ± 16 g and 250 Hz, respectively. Rowing is a low impact sport and subsequently an operating range of ±16 g is acceptable with a low risk as of sensor clipping. MEMS technology is improving rapidly and inertial sensors with sampling frequencies >1 kHz are now readily available with improved sensor resolution. Therefore, it is recommended that future investigations could explore using higher sampling frequencies to improve the quality of the captured information.
