Embodied Music Preference Modeling: Real-Time Prediction From Wearable Gait Telemetry, Google Fit Activity, and Gesture-Based Feedback
Bibliographic record
Abstract
Abstract Background Music is widely deployed to enhance exercise, yet far less is known about how an exerciser’s real-time physiological state feeds back to shape musical liking. Wearable sensors now permit second-by-second coupling of gait dynamics, activity load, and affective response. Objective We developed a mobile workflow that predicts immediate like versus dislike judgments for unfamiliar songs by fusing clinical-grade inertial kinematics (Ambulosono), passive smartphone activity logs (Google Fit), and a single high-knees/low-knees gesture. The study tested whether momentary movement intensity biases preference, identified the strongest biometric predictors, and evaluated a sensor-aware classifier suitable for adaptive playlists. Methods Seventy-three healthy undergraduates performed fifty 60-second stepping-in-place trials while listening to tempo-normalised tracks spanning five genres. Ambulosono units sampled lower-limb acceleration at 200 Hz; Google Fit recorded pre- and post-trial step counts. Four gait features—mean and peak cadence, mean and peak step length—were normalised and averaged into a Composite Motivation Score (CMS). Breathlessness and fatigue ratings were logged after each track. A 500-tree random forest trained on gait variables, activity counts, perceptual deltas, and CMS classified preference using 10-fold cross-validation. Statistical tests compared liked and disliked trials for physiological change and speed strata. Results The protocol yielded 3 864 complete song exposures. Participants judged 54 % of tracks liked and 60 % disliked (paired t = –2.11, p = 0.036). Disliked trials exhibited larger breathlessness and fatigue increases (Wilcoxon p < 0.05). High-CMS trials showed a 68 % like-rate versus 37 % in low-CMS trials. The classifier achieved 0.78 accuracy and 0.82 AUC; permutation analysis ranked post-trial Google Fit steps, bout duration, and pre-trial steps as top predictors. Track-level analysis revealed that the ten most-disliked songs coincided with the highest mean stepping speeds, despite non-significant effects at coarse speed tiers. Conclusions Immediate bodily engagement and short-term physiological strain strongly colour musical appraisal. Integrating wearable kinematics, smartphone step counts, and low-friction gestures enables accurate, interpretable prediction of liking, paving the way for context-adaptive playlists and emotionally intelligent rehabilitation cues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".