Smart technologies for motor function assessment in innovative and collaborative environments

Smart technologies are rapidly reshaping healthcare and industrial domains, creating environments where humans interact with virtual systems and collaborative robots. These innovations promise efficiency, rehabilitation opportunities, and enhanced safety, yet they also raise critical questions about ergonomics, cognitive load, and human adaptability. Despite the growing presence of virtual reality and robotics in several contexts, systematic and quantitative investigations into their impact on motor function and cognitive states remain limited. Addressing this gap, the present thesis explores how technologically enhanced environments influence human motor behavior, with the goal of informing the design of systems that are safe, effective, and human-centered. Human movement analysis has long been a cornerstone of clinical practice, rehabilitation, ergonomics, and performance research, but traditional methods often lack sensitivity and objectivity. Recent advances in wearable sensors, motion capture, electromyography, and immersive technologies now allow continuous, multidimensional, and high-resolution monitoring of motor behavior. This thesis leverages these innovations to investigate motor strategies, workload, and postural control in contexts where humans collaborate with technologically mediated environments. The experimental core comprises four complementary studies: (1) the detection of sensorimotor deficits in individuals with dementia during reaching and catching tasks in virtual reality environments; (2) the analysis of postural control in collaborative manipulation with a robotic arm; (3) the multimodal investigation of a hand-over task involving robotic interaction and stop signals; and (4) the development of electromyography-based machine learning models to classify reaching targets and infer upper-limb motor intentions. Together, these studies integrate kinematic, postural, muscular and cognitive-related data to characterize key aspects of human adaptation in innovative and collaborative environments. The findings demonstrate that: virtual reality assessments can sensitively detect motor alterations associated with dementia; robot-assisted tasks modulate motor and cognitive strategies; electromyography-driven models can reliably predict reaching targets, with accuracy depending on muscle selection, feature extraction, and temporal windows. Beyond methodological contributions, the results have practical implications: they support early diagnosis and rehabilitation in neurodegenerative conditions, inform ergonomic risk assessment and adaptations in industrial robotic collaboration, and lay the groundwork for adaptive robotic systems capable of responding to human intention in real time. Collectively, these results advance the understanding of human adaptation in innovative and collaborative environments. By bridging neuroscience, ergonomics, and robotics, this thesis contributes to the broader effort of integrating intelligent technologies into healthcare and industry in ways that aim to support human performance and collaboration.