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In this paper we consider the detection of a decrease of engagement by users spontaneously interacting with a socially assistive robot in a public space. We first describe the UE-HRI dataset that collects spontaneous human–robot interactions following the guidelines provided by the affective computing research community to collect data “in-the-wild”. We then analyze the users’ behaviors, focusing on proxemics, gaze, head motion, facial expressions and speech during interactions with the robot. Finally, we investigate the use of deep leaning techniques (recurrent and deep neural networks) to detect user engagement decrease in real-time. The results of this work highlight, in particular, the relevance of taking into account the temporal dynamics of a user’s behavior. Allowing 1–2 s as buffer delay improves the performance of taking a decision on user engagement.

V̇O2max is recognized as a key measure in exercise physiology and sports medicine. However, only 20–50% of maximal incremental exercise tests (IET) result in a plateau of V̇O2 (V̇O2pl). To our knowledge, no study has yet examined the possible difference in brain activity during an IET, in V̇O2pl and non-plateau athletes with the same V̇O2max and age. This study aimed to shed light on the central governor hypothesis, namely that the inability to reach a V̇O2pl may be dictated by the brain rather than by a peripheral physical limit. This hypothesis can now be explored using electroencephalography (EEG) during IET, measuring concomitant power in specific frequency bands. Forty-two athletes were divided into two groups: those who practiced endurance sports and those who did not, and were asked to perform an IET. EEG signals and gas exchange were recorded. A V̇O2pl was observed in twenty-two subjects (52%). EEG power increased in all subjects during IET, except in the alpha band, which showed variability, but not significantly (64% increase, 34% decrease, p = 0.07). No differences were found between endurance athletes and non-endurance athletes, except for V̇O2max (60.10 ± 6.16 vs. 51.77 ± 6.41, p < 0.001). However, the baseline-corrected ratio of EEG power to V̇O2 was found to decrease in all subjects during IET, in the alpha, beta and theta bands. In conclusion, the presence or absence of a V̇O2pl is not related to the type of EEG response during an IET. Nevertheless, the decline in brain and V̇O2 powers/ratios in all frequency bands suggests that aerobic power may be constrained by brain mobilization.

Background: Exercise duration at maximum oxygen uptake (V˙O2max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at V˙O2max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach V˙O2max correlated with Alpha/V˙O2 (p < 0.001), Alpha/V˙CO2 (p < 0.001), and Beta/V˙CO2 (p = 0.002). The time spent at V˙O2max correlated with Theta/V˙O2 (p = 0.002) and Theta/V˙CO2 (p < 0.001). The time-to-exhaustion was correlated with Theta/V˙CO2 (p < 0.001) and Alpha/V˙CO2 (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

Background: Exercise duration at maximum oxygen uptake (V˙O[sub.2]max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at V˙O[sub.2]max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach V˙O[sub.2]max correlated with Alpha/V˙O[sub.2] (p < 0.001), Alpha/V˙CO[sub.2] (p < 0.001), and Beta/V˙CO[sub.2] (p = 0.002). The time spent at V˙O[sub.2]max correlated with Theta/V˙O[sub.2] (p = 0.002) and Theta/V˙CO[sub.2] (p < 0.001). The time-to-exhaustion was correlated with Theta/V˙CO[sub.2] (p < 0.001) and Alpha/V˙CO[sub.2] (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

Background: Exercise duration at maximum oxygen uptake (V˙O2max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at V˙O2max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach V˙O2max correlated with Alpha/V˙O2 (p < 0.001), Alpha/V˙CO2 (p < 0.001), and Beta/V˙CO2 (p = 0.002). The time spent at V˙O2max correlated with Theta/V˙O2 (p = 0.002) and Theta/V˙CO2 (p < 0.001). The time-to-exhaustion was correlated with Theta/V˙CO2 (p < 0.001) and Alpha/V˙CO2 (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

Exercise duration at maximum oxygen uptake (V˙O max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Beta power was negatively correlated with time spent at V˙O max (r = -0.542, = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG-metabolic ratios revealed significant correlations. Specifically, the time to reach V˙O max correlated with Alpha/V˙O ( < 0.001), Alpha/V˙CO ( < 0.001), and Beta/V˙CO ( = 0.002). The time spent at V˙O max correlated with Theta/V˙O ( = 0.002) and Theta/V˙CO ( < 0.001). The time-to-exhaustion was correlated with Theta/V˙CO ( < 0.001) and Alpha/V˙CO ( < 0.001). : These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG-metabolic ratios may provide exploratory indicators of brain-metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

Background: Exercise duration at maximum oxygen uptake ( V ˙ O2max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at V ˙ O2max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach V ˙ O2max correlated with Alpha/ V ˙ O2 (p < 0.001), Alpha/ V ˙ CO2 (p < 0.001), and Beta/ V ˙ CO2 (p = 0.002). The time spent at V ˙ O2max correlated with Theta/ V ˙ O2 (p = 0.002) and Theta/ V ˙ CO2 (p < 0.001). The time-to-exhaustion was correlated with Theta/ V ˙ CO2 (p < 0.001) and Alpha/ V ˙ CO2 (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.

Music understanding and reasoning are central challenges in the Music Information Research field, with applications ranging from retrieval and recommendation to music agents and virtual assistants. Recent Large Audio-Language Models (LALMs) have shown remarkable progress in answering music-related questions by following user instructions. However, their massive scale, often billions of parameters, results in expensive training, slow inference, and limited deployability on edge devices. In this work, we present TinyMU, a lightweight (229M) Music-Language Model (MLM) that achieves performance comparable to much larger LALMs while remaining efficient and compact. To train TinyMU, we introduce MusicSkills-3.5M, a carefully curated, music-grounded question-answering dataset with 3.5M samples. Spanning multiple-choice, binary, and open-ended formats, this dataset provides fine-grained supervision across diverse musical concepts. For its architecture, TinyMU leverages MATPAC++, the SOTA self-supervised audio encoder for fine-grained feature extraction. Paired with a lightweight linear projector, it efficiently aligns audio embeddings with the language model. Through extensive evaluation, we show that TinyMU performs strongly in both basic music understanding and complex reasoning. Notably, on the MuChoMusic benchmark, it achieves 82\\% of SOTA LALM's performance despite being 35x smaller, highlighting the potential of small MLMs under constrained computational budgets.

We present a new, freely available, multimodal corpus for research into, amongst other areas, real-time realistic interaction between humans in online virtual environments. The specific corpus scenario focuses on an online dance class application scenario where students, with avatars driven by whatever 3D capture technology is locally available to them, can learn choreographies with teacher guidance in an online virtual dance studio. As the dance corpus is focused on this scenario, it consists of student/teacher dance choreographies concurrently captured at two different sites using a variety of media modalities, including synchronised audio rigs, multiple cameras, wearable inertial measurement devices and depth sensors. In the corpus, each of the several dancers performs a number of fixed choreographies, which are graded according to a number of specific evaluation criteria. In addition, ground-truth dance choreography annotations are provided. Furthermore, for unsynchronised sensor modalities, the corpus also includes distinctive events for data stream synchronisation. The total duration of the recorded content is 1 h and 40 min for each single sensor, amounting to 55 h of recordings across all sensors. Although the dance corpus is tailored specifically for an online dance class application scenario, the data is free to download and use for any research and development purposes.

Ambiguities in data and problem constraints can lead to diverse, equally plausible outcomes for a machine learning task. In beat and downbeat tracking, for instance, different listeners may adopt various rhythmic interpretations, none of which would necessarily be incorrect. To address this, we propose a contrastive self-supervised pre-training approach that leverages multiple hypotheses about possible positive samples in the data. Our model is trained to learn representations compatible with different such hypotheses, which are selected with a knowledge-based scoring function to retain the most plausible ones. When fine-tuned on labeled data, our model outperforms existing methods on standard benchmarks, showcasing the advantages of integrating domain knowledge with multi-hypothesis selection in music representation learning in particular.