Virtuosity in music performance is often associated with fast, precise, and efficient sound-producing movements. The generation of such highly skilled movements involves complex joint and muscle control by the central nervous system, and depends on the ability to anticipate, segment, and coarticulate motor elements, all within the biomechanical constraints of the human body. When successful, such motor skill should lead to what we characterize as fluency in musical performance. Detecting typical features of fluency could be very useful for technology-enhanced learning systems, assisting and supporting students during their individual practice sessions by giving feedback and helping them to adopt sustainable movement patterns. In this study, we propose to assess fluency in musical performance as the ability to smoothly and efficiently coordinate while accurately performing slow, transitionary, and rapid movements. To this end, the movements of three cello players and three drummers at different levels of skill were recorded with an optical motion capture system, while a wireless electromyogrphy (EMG) system recorded the corresponding muscle activity from relevant landmarks. We analyze the kinematic and coarticulation characteristics of these recordings separately and then propose a combined model of fluency in musical performance predicting music sophistication. Results suggest movements from expert performers' are characterized by consistently smooth strokes and scaling of muscle phasic coactivation. The explored model of fluency as function of movement smoothness and coarticulation patterns was shown to be limited by the sample size but serves as a proof of concept. Results from this study show the potential of a technology-enhanced objective measure of fluency in musical performance, which could lead to improved practices for aspiring musicians, instructors, and researchers.

The relationships between human body motion and music have been the focus of several studies characterizing the correspondence between voluntary motion and various sound features. The study of involuntary movement to music, however, is still scarce. Insight into crucial aspects of music cognition, as well as characterization of the vestibular and sensorimotor systems could be largely improved through a description of the underlying links between music and involuntary movement. This study presents an analysis aimed at quantifying involuntary body motion of a small magnitude (micromotion) during standstill, as well as assessing the correspondences between such micromotion and different sound features of the musical stimuli: pulse clarity, amplitude, and spectral centroid. A total of 71 participants were asked to stand as still as possible for 6 min while being presented with alternating silence and music stimuli: Electronic Dance Music (EDM), Classical Indian music, and Norwegian fiddle music (Telespringar). The motion of each participant's head was captured with a marker-based, infrared optical system. Differences in instantaneous position data were computed for each participant and the resulting time series were analyzed through cross-correlation to evaluate the delay between motion and musical features. The mean quantity of motion (QoM) was found to be highest across participants during the EDM condition. This musical genre is based on a clear pulse and rhythmic pattern, and it was also shown that pulse clarity was the metric that had the most significant effect in induced vertical motion across conditions. Correspondences were also found between motion and both brightness and loudness, providing some evidence of anticipation and reaction to the music. Overall, the proposed analysis techniques provide quantitative data and metrics on the correspondences between micromotion and music, with the EDM stimulus producing the clearest music-induced motion patterns. The analysis and results from this study are compatible with embodied music cognition and sensorimotor synchronization theories, and provide further evidence of the movement inducing effects of groove-related music features and human response to sound stimuli. Further work with larger data sets, and a wider range of stimuli, is necessary to produce conclusive findings on the subject.

Workers, especially those involved in jobs requiring extended standing or repetitive movements, often face significant health challenges due to Musculoskeletal Disorders (MSDs). To mitigate MSD risks, enhancing workplace ergonomics is vital, which includes forecasting long-term employee postures, educating workers about related occupational health risks, and offering relevant recommendations. However, research gaps remain, such as the lack of a sustainable AI/ML pipeline that combines sensor-based, uncertainty-aware posture prediction with large language models for natural language communication of occupational health risks and recommendations. We introduce ERG-AI, a machine learning pipeline designed to predict extended worker postures using data from multiple wearable sensors. Alongside providing posture prediction and uncertainty estimates, ERG-AI also provides personalized health risk assessments and recommendations by generating prompts based on its performance and prompting Large Language Model (LLM) APIs, like GPT-4, to obtain user-friendly output. We used the Digital Worker Goldicare dataset to assess ERG-AI, which includes data from 114 home care workers who wore five tri-axial accelerometers in various bodily positions for a cumulative 2913 hours. The evaluation focused on the quality of posture prediction under uncertainty, energy consumption and carbon footprint of ERG-AI and the effectiveness of personalized recommendations rendered in easy-to-understand language.

Ensemble musicians engage with each other visually through glances and body motion. We conducted a case study to test how string quartet musicians would respond to playing conditions that were meant to discourage or promote visually communicative behaviour. A quartet performed in different seating configurations under rehearsal and concert conditions. Quantity of head motion was reduced when musicians' gaze was constrained. Differences in gaze and body motion between musicians reflected their musical roles in the ensemble. Overall, our findings suggest that gaze and motion dynamics vary within and between performances in response to changing musical, situational and social factors.

Previous studies have shown that music may lead to spontaneous body movement, even when people try to stand still. But are spontaneous movement responses to music similar if the stimuli are presented using headphones or speakers? This article presents results from an exploratory study in which 35 participants listened to rhythmic stimuli while standing in a neutral position. The six different stimuli were 45 s each and ranged from a simple pulse to excerpts from electronic dance music (EDM). Each participant listened to all the stimuli using both headphones and speakers. An optical motion capture system was used to calculate their quantity of motion, and a set of questionnaires collected data about music preferences, listening habits, and the experimental sessions. The results show that the participants on average moved more when listening through headphones. The headphones condition was also reported as being more tiresome by the participants. Correlations between participants' demographics, listening habits, and self-reported body motion were observed in both listening conditions. We conclude that the playback method impacts the level of body motion observed when people are listening to music. This should be taken into account when designing embodied music cognition studies.

Bench and cross-sectional study. Information obtained from dexterity tests is an important component of a comprehensive examination of the hand. To analyze and compare finger interdependencies during the performance of the Purdue Pegboard Test (PBT) and comparative daily tasks. A method based on the optoelectronic kinematic analysis of the precision grip style and on the calculation of cross-correlation coefficients between relevant joint angles, which provided measures of the degree of finger coordination, was conducted on 10 healthy participants performing the PBT and 2 comparative daily living tasks. Daily tasks showed identifiable interdependencies patterns between the metacarpophalangeal joints of the fingers involved in the grip. Tasks related to activities of daily living resulted in significantly higher cross-correlation coefficients across subjects and movements during the formation and manipulation phases of the tasks (0.7-0.9), whereas the release stage produced significantly lower movement correlation values (0.3-0.7). Contrarily, the formation and manipulation stages of the PBT showed low finger correlation across most subjects (0.2-0.6), whereas the release stage resulted in the highest values for all relevant movements (0.65-0.9). Interdependencies patterns were consistent for the activities of daily living but differ from the patterns observed from the PBT. The PBT does not compare well with the whole range of finger movements that account for hand performance during daily tasks. Not applicable.