## 4 Experiments  
### Settings  
#### 4.1.1 Datasets  
The dataset used in this study consists of both a classic music dataset and additional real-world validation data. The classic dataset originates from the appendix of [PERSON]'s 2012 study [9], which includes 312 music clips spanning various genres. These tracks were previously rated, with scores ranging from 40 to 107, and were subsequently normalized to a 0-100 scale. The tracks were then divided into three groove levels (low, moderate, and high) by splitting the scores into tertiles.  
For real-world validation, 20 participants were recruited to perform a cycling task while listening to randomly selected music tracks from the dataset. Each participant was assigned 40 clips in total, presented in random order, during the cycling task. Their cadence (pedal stroke rate) was recorded and analyzed. Tracks were excluded from the dataset if the correlation between the participant's pedal stroke rate and the music's BPM was lower than 0.5; see Figure 3.  
The use of pedal stroke rate to validate the groove levels in [PERSON]'s dataset was chosen because [PERSON] also employed similar behavioral experiments to determine groove levels. Groove's most essential characteristic is its ability to induce synchronization between movement and music, rather than evoking emotional responses. Therefore, conducting such behavioral experiments provides the most direct way to assess groove.  
Additionally, since [PERSON]'s playlist has already been validated [10; 11; 12], this study further refined the dataset by removing outdated tracks or those that failed to evoke a sense of movement synchronization in modern participants. No new dataset was created, as the purpose was to optimize the model training by filtering existing data effectively. Cycling was chosen as the experimental activity because groove has significant potential to enhance physical performance in cycling and improve motor rhythm in [PERSON] patients [47], where cycling has been shown to offer a safer and more effective intervention than other forms of movement.  
Cycling Task Setup  
The cycling task was conducted using a stationary bicycle. The seat height was adjusted to approximately 88% of each participant's inner leg length, ensuring a comfortable and natural pedaling posture. A standardized protocol was followed throughout each session. Before starting the formal task, participants completed a 3-minute warm-up phase at a torque of 2.5 N\(\cdot\)m, maintaining a cadence of approximately 55-60 revolutions per  
Figure 3: Dataset and experimental validation. (**a**) [PERSON] and colleagues utilized behavioral and computational techniques to measure the sensorimotor coupling in response to grooves, thereby establishing a groove rating; (**b**) real-world validation using cycling task.  
minute (rpm). Following the warm-up, the formal cycling task began, with the torque increased to 3 N\(\cdot\)m. Participants began cycling at the start of each music stimulus, and after one minute of cycling (with the music clip looping), data from the last 20 seconds were recorded. The last 20 seconds were collected to ensure that participants had sufficient time to adapt and reach a stable rhythm before their cadence was measured.  
Motion capture was performed using the FAB inertial measurement unit (IMU) system (BIOSYN, Surrey, BC, Canada) to continuously track joint trajectories and analyze participants' pedal cycles. Seven IMUs were placed on the pelvis, thighs, shanks, and feet, with a sampling frequency of 100 Hz, as shown in Figure 3b.  
After the experiment, 54 music clips were excluded for not meeting the correlation threshold, leaving a final dataset consisting of 258 music clips with corresponding groove classification labels. These labels reflect the groove levels determined by participant ratings in [PERSON]'s study, where listeners evaluated how much each track encouraged bodily movement. The training dataset comprises 80% of the data and the testing dataset comprises 20%.