*3.2. Performance Features*

An athlete's overall performance in rowing is dependent on many factors. Rowing is an interesting sport to analyse as it has to be considered a system controlled by athlete–equipment interaction. The performance features that were examined in the captured articles were extracted (Table 2). Ultimately, the most important factor for performance in rowing is boat velocity; however, this metric is influenced by various inputs. The features were divided into four main groups: stroke quality, instrumented material metrics, athlete physicality/physiology metrics, and general biofeedback. The stroke quality group consists of metrics such as stroke length, stroke rate, recovery/drive phase ratio, stroke variance, stroke force, and cadence. By instrumenting the equipment used by the athlete, many measures of performance can be observed. Instrumented material metrics included boat position, boat velocity, foot-rest force, boat orientation, oar orientation, and stability. Rowing is an extremely physically demanding sport and requires high skill and therefore the physical and physiological makeup of the athlete is also vital. In this group, measures associated with fatigue, power output, muscle activity, energy output, and also crew synchronicity for team rowing. Finally, general feedback consisted of split times and activity classification.

### *3.3. Data Processing Algorithms*

Various techniques can be used for signal processing of time-series data, for example, frequency filtering of data to remove the effect of noise or drift. Time-series data can also be transferred to the frequency domain using Fourier transforms enabling frequency analysis. Machine learning and deep learning techniques can also be applied for automatic classification of significant events in rowing or human actions, summarized in Table 3. It should be noted that some of the included records used commercialized measurement technology and so limited information about the data processing methods is available.

### *3.4. Study Design and Hardware*

The included records were also reviewed with respect to their study design and the properties of the inertial sensor hardware used. The methodology was evaluated on the number of inertial sensor devices used, the sampling frequency and operating range of the sensors, the location of the device(s), data transmission, the testing environment, and participant selection (Tables 4 and 5). For studies testing the performance of novel hardware or extracting innovative metrics to assess an athlete's performance, then it is important to verify the measurements obtained with a measurements from a 'golden' standard technology. This ensures validity and reproducibility of the measurements obtained (Table 6). In sport and biomechanics research, the golden standard is commonly a multi-camera retro-reflective motion capture system that can track human positions in three-dimensional space. From this data, the acceleration and rotation of the body part can be calculated and compared to inertial sensor data [3]. The competitive setting for rowing is on-water and thus retro-reflective motion capture is not a viable option. It is recommended that measurements are made on a rowing ergometer in a laboratory environment initially. 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.


**Table 2.** Rowing performance features.


**Table 3.** Signal processing algorithms used on data, KNN (K-nearest Neighbours); DTW (Dynamic Time Warping); SVM (Support Vector Machine).

**Table 4.** Properties of inertial sensor instrumentation, Number (#) of devices; OR (Operating Range); RF (Radio Frequency); BT (Bluetooth); ANT (Adaptive network technology); NS (Not Stated); NA (Not Applicable).


**Table 5.** Sensor placement, Erg (Ergometer); B (Boat).



**Table 6.** Table of inertial sensor validation methods used in the included records.
