OBJECTIVE: To develop and evaluate a time-aware data mining pipeline that integrates accelerometry-based Physical Activity (PA), static dietary intake, and sociodemographic factors to predict Healthy Weight Status (HWS), and identify features with predictive importance for HWS. METHODS: We propose TimePAD, Time-Based Physical Activity and Dietary Intake data mining, a three-stage prediction pipeline that integrates time-based PA representations, static dietary intake, and sociodemographic variables to predict HWS categories. Stage 1 derives intensity-specific hourly PA sequences and learns time-of-day PA embeddings using a Transformer encoder trained with masked Self-Supervised Learning (SSL) reconstruction. Stage 2 combines the learnt PA embeddings with static dietary variables and participant characteristics derived from the Food Frequency Questionnaire (FFQ). Stage 3 trains prediction models and ranks predictors by their predictive importance for HWS. TimePAD was evaluated on a real-world dataset of 206 adolescents (10-16 years) with 7-day continuous wrist-worn accelerometer data, FFQ records, and sociodemographic attributes, using 10-fold cross-validation and comparison to an ARIMA-based feature engineering baseline. RESULTS: TimePAD achieves an accuracy of 82.90% and an F1-score of 67.92% for HWS prediction, outperforming the best baseline (ARIMA-based PA features + diet, 77.51% accuracy). Across experiments, time-of-day light PA (LPA) features were consistently ranked as important predictors. Other key features include time-of-day Moderate-to-Vigorous Activity (MVPA) and Sedentary (SED) levels, Tanner stage, total weekly sleep time, age (in months), weekly fruit intake, weekly vegetable and legume intake, and intake of sugar-sweetened beverages. DISCUSSION: TimePAD contributes a pipeline for learning from the time-of-day structure in wearable PA time series and integrating it with static dietary and contextual data for prediction and feature analysis. The findings suggest that LPA is likely to have a significant association with HWS, calling for further attention and investigation to better understand the role of LPA in overall health outcomes. This illustrates the potential benefits of TimePAD in modelling PA with dietary intake context in shaping healthy behaviours.

Multimodal annotations provide important cues for understanding how a conversation proceeded, particularly in relation to affective factors. In this paper, we extend the automated conversation annotation system MONAH withpitchandvolumeannotations to encode new affective cues, making MONAHv3 the state-of-the-art automatic annotation system in terms of the number of automatically annotated aspects. MONAHv3 provides an automated solution that, while not a direct equivalent, offers performance that is competitive with the manually produced Jefferson transcription system. In automatic evaluations, the additions significantly improve supervised learning in ten out of eighteen experiments. In human evaluations of emotion recognition, the additions significantly outperformed the Jefferson transcription system. Usability studies further showed that MONAHv3 is much more user-friendly than Jefferson-style transcripts. Lastly, in human evaluations of paralinguistic cues (e.g., tone and volume), MONAHv3 achieved performance comparable to the Jefferson system, while remaining more competitive in kinesics (e.g., describing actions). This distinction between automated and manual systems is critical for appreciating the scalability and accessibility that MONAHv3 offers.

Joshua Y. Kim, Kalina Yacef, Greyson Kim, Chunfeng Liu, Rafael Calvo, Silas Taylor. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.