## 2 Materials and Methods
### Data Analysis  
The data from the three different systems (i.e., Powerline, Delsys, iSen) were synchronized as follows. The data from the starboard oar angle were sent to the Delsys system (Trigno Control Utility version 3.6.0) via a trigger adapter. This allowed to synchronize the short segments recorded with the Delsys system with the much longer sessions recorded with the Powerline system (version 4.12.0.0). Then, the iSen (version 2022.0) data were synchronized with the Powerline data based on the signal of the boat acceleration along the direction of motion measured by both the IMU placed on the boat and the Powerline box sensor. A custom-made code in Matlab (version R2022b) identified the minima and maxima of the starboard oar angle (i.e., the catch and finish, respectively), which were then used to segment the rowing cycles. The information about body kinematics (joint angles and angles with respect to the vertical) were provided by the built-in models of the iSen package. We analyzed wrist, elbow, and shoulder flexion as well as the angle with respect to the vertical axis of the T6,  
Figure 1: Placement of IMU and EMG sensors on the body. EMG: 1—biceps brachii, 2—brachioradialis, 3— flexor carpi ulnaris, 4—triceps brachii longus, 5—deltoideus anterior, 6—deltoideus posterior, 7—thoracic enctor spinae, 8—lumbar erector spinae, 9—obliquus externus, 10—obliquus internus, 11—rectus abdominis, 12—tensor fasciae latae, 13—gluteus maximus. IMU: 1—hand, 2—forearm, 3—arm, 4—C7 verteba, 5—T6 verteba, 6—L2 verteba, 7—pelvis. L2, and pelvis IMUs. Shoulder flexion is given by the position of the arm in the sagittal plane and has 0\({}^{\circ}\) corresponding to a neutral hanging arm. EMG signals were high-pass filtered at 30 Hz to remove the ECG contributions [32]. They were then demeaned, rectified, and low-pass filtered at 5 Hz, similar to [PERSON] et al. [33]. EMG signals for each subject were normalized to the maximum value observed during the three bouts performed. The decision to normalize to the maximum value observed during the three bouts was due to constraints in the scheduled training, which prevented from performing a rowing bout at maximum intensity and to the desire to avoid effects of perspiration and degradation of the adhesive layer during the session [34]. After processing and segmenting the EMG data into cycles, signals were visually inspected and cycles with motion artefacts or missing data (e.g., due to the sensors being out of reach of the receiver) were removed from subsequent analyses. When both right and left GMs were present, their curves were averaged together and considered as one muscle for the rest of the analysis. The root mean square (RMS) of each signal was calculated over eight equal intervals for the drive (catch to finish) and recovery (finish to catch) phases. Thus, each muscle had 16 RMS values describing its activation pattern throughout the rowing cycle. These values were then used as predictor variables in the statistical analysis. The variables used as predictors are detailed in Table 11. The variables selected describe the technique of the rowers in terms of body kinematics (WriCatch, Wrifinish, WrigRoM, ElbCatch, ElbFinish, ElbRoM, ShoCatch, ShoFinish, ShoRQM, \(\mathds{16}_{\text{Catch}}\), \(\mathds{16}_{\text{Finish}}\), \(\mathds{16}_{\text{RoM}}\), \(\mathds{12}_{\text{Catch}}\), \(\mathds{12}_{\text{Finish}}\), \(\mathds{12}_{\text{RoM}}\), PelVatch, PelVrinish, PelVRoM), boat telemetry (Length, LengthAs, CSA, CSAAs, FSAAs, EFA, EFAAs, mAcc, ForceTEP, ForceTEP, ForceTEP, ForceAP), and neuromuscular activations (RMS). These variables were selected based on literature [2; 3; 6; 14; 21] and feedback from trainers. In particular, variables such as slip angles and effective angles are often used as performance indicators by trainers as they are among the parameters provided by the Powerline system. Some of these technical determinants were already analyzed by [PERSON] et al.