Editorial: Neuro-cognition in human movement: from fundamental experiments to bio-inspired innovation

Introduction This Editorial provides an in-depth overview of motor execution involving neurocognitive aspects integrating somatosensory inputs and their significance in human movement bridging fundamental research to bio-inspired innovations. Technology transfer of basic scientific evidence into mainstream practice of innovation-inspired application and technology-supported therapy has been catalyzed in the past decade by broad horizontally and vertically structured methodological progression (Luu et al., 2022). Recent developments of new neurophysiological and imaging techniques or their synthesis currently allow progress in the experimental evaluation and tracking of cognitive or motor short-term and long-term learning which is implemented in transfer arms into practice (Tanzmeister et al., 2016;Luu et al., 2022). The advance in technology innovation of the past decade allows not only a broad practical connectivity in technical innovations and digital solutions, but even the purposeful integration of technologies, intelligent solutions, and algorithm-based modeling into active research (Hutchison and Gallivan, 2018;Kroemer et al., 2021). Examples can be found in smart sensors, feedback-based exoskeletons (Afschrift et al., 2023; Refai et al., 2023), virtual reality (Lang et al., 2009), and e-health diagnostics. Still challenging is the validation of the new scientific pathways using experimentally obtained neurocognitive data sets and the global consideration and the dedicated inclusion of these new branches of science in research practice (Latella et al., 2024). 2. Cognition and Motor Coordination in Complex Environments Cognitive control, also known as executive function, refers to the mental processes that allow individuals to regulate their thoughts, emotions, and actions in pursuit of goal-directed behavior (Friedman and Robbins, 2022). In real-world dynamic environments, cognitive control becomes crucial for adapting to changing conditions and achieving desired outcomes despite constant environmental fluctuations (Cañas et al., 2003). These environments—ranging from sports fields to bustling urban settings—demand flexibility, attentional focus, and the ability to switch between tasks or strategies rapidly. Cognitive control in real-world dynamic environments In dynamic environments, cognitive control mechanisms are engaged to manage competing demands, resolve conflicts, and plan adaptive responses (Ritz et al., 2022). The prefrontal cortex is particularly central in these processes, serving as the hub for executive functions like inhibition, working memory, and task-switching (Johnston et al., 2007;Kim et al., 2017). Furthermore, cognitive control allows for the maintenance of goal-directed behavior despite distractions or unexpected changes in the environment. In the context of sports or robotics, these mechanisms must not only handle internal cognitive load but also coordinate with perceptual processes to align action with external feedback in real-time. Decision-making and action selection in everyday tasks, and robotics Decision-making is the process through which individuals choose a course of action from a set of alternatives (Taylor, 2013). In robotics, and everyday tasks, decision-making is dynamic and often occurs under conditions of uncertainty and time pressure. Robotics systems, especially in autonomous robots or vehicles, must process a wide range of sensory data to make decisions regarding movement, task execution, and error correction in real-time (Baxi et al., 2022). The underlying cognitive and neural mechanisms of decision-making in these contexts often involve a balance between automatic, intuitive responses and deliberate, effortful reasoning. Cognitive control plays a role in managing this balance, especially when conditions become uncertain or high-stakes. In everyday tasks, such as crossing a street or driving a car, decision-making integrates sensory inputs with prior experiences, expected outcomes, and risk assessments (Soares et al., 2021). The efficiency of decision-making can be influenced by factors such as working memory, attention, and the ability to inhibit prepotent or habitual responses (Friedman and Robbins, 2022). The neurobiological mechanisms of decision-making are often studied in the context of reinforcement learning, where the brain learns to optimize actions based on feedback from the environment. Therefore, the sensory input niéeds to be coupled with the motor outputs also in Human Machine Interaction (HMI) (Yuan et al., 2023). Role of perception-action coupling in movement efficiency Perception-action coupling refers to the dynamic, reciprocal relationship between sensory inputs and motor outputs that underlie coordinated action (Dumas and Fairhurst, 2021). The efficiency of movement depends not only on physical factors like strength and agility but also on cognitive factors, such as attention, anticipation, and reaction time. This concept is central to understanding how individuals and robotic systems achieve efficient and adaptive movement in real-time. In real-world tasks, efficient movement requires the integration of perception (e.g., visual, auditory, or tactile input) with motor planning and execution. The ability to couple perception and action allows for anticipatory control, where individuals or robots can predict future states and modify their behavior accordingly. In robotics, perception-action coupling is crucial for tasks such as navigation, manipulation, and interaction with the environment (Warren, 2024). Autonomous robots use sensors (e.g., cameras, lidar, force sensors) to gather data about the environment, which is then used to inform their motor actions (Manakitsa et al., 2024a). The coupling of perception and action in robotics systems has advanced significantly with the development of techniques such as computer vision and machine learning, allowing robots to adapt to dynamic environments and optimize movement efficiency in real-time. The interaction between cognitive control, decision-making, and perception-action coupling is essential for achieving optimal performance in dynamic environments (Manakitsa et al., 2024a;Warren, 2024). For example, in daily environments, cognitive control mechanisms regulate the decision-making process by managing attention, inhibiting inappropriate responses, and adapting to environmental cues. The coupling of perception and action ensures that the the individual can adjust movements in real-time based on sensory feedback, while decision-making processes guide the selection of appropriate actions in response to changing conditions. Similarly, in robotics, efficient action selection is facilitated by decision-making algorithms that process sensory input in conjunction with movement commands (Hutchison and Gallivan, 2018). Cognitive control in this context helps manage resource allocation and action prioritization, while perception-action coupling enables the robot to adjust its movements based on feedback from its sensors. This integration is vital for maintaining efficiency, reducing errors, and improving task performance in environments that are both unpredictable and dynamic. In industrial settings, collaborative robots (cobots) also depend on this coupling. As a worker and robot share the same workspace, their actions must be synchronized in real time. For instance, if the worker is placing a component on an assembly line, the robot must sense the worker's position and actions, adjusting its own movements to ensure safety, efficiency, and the correct handling of tasks. Cognitive control plays a crucial role in these interactions, as the human operator must be aware of both their own movements and the robot's actions, while also interpreting the robot's feedback (Demir et al., 2019). 3. Bio-inspired Innovations in Technology and Robotics Big potential exist to leverage socioeconomic effects of technological innovation on ageing, pathological conditions and shifts in social care. Socioeconimic impact of new technologies Assistive technologies, such as AI-powered prosthetics, wearable neurostimulators and exoskeletons, offer new possibilities or individuals with neurodegenerative disorders, reducing healthcare burdens and enabling greater independence (Malcangi, 2021;Luu et al., 2022). In aging societies, smart rehabilitation tools could alleviate pressure on caregivers and improve quality of life (Latella et al., 2024). However, challenges remain, including accessibility, ethical considerations, and disparities in technology adoption across different socio-economic groups. Bio-inspired technologies have a major impact on healthcare, assistive robotics, and social care. These innovations hold potential in addressing some of the most pressing socioeconomic challenges arising from demographic ageing, chronic health conditions, and the evolving role of caregivers in society (Horton et al., 2017;Latella et al., 2024). It is known that the global population age, which puts pressure on healthcare systems and social care infrastructures. Besides developing adequate fall prevention programs through identifying biological, behavioral, environmental, and socio-economic fall risk factors (Lathouwers et al., 2022), the rise of bio-inspired robotic systems, such as exoskeletons, soft robotics, and sensor-embedded wearables, are aiding older adults to maintain autonomy, mobility, and social participation (Afschrift et al., 2023;Paternò and Lorenzon, 2023). By integrating musculoskeletal structures and neuromuscular control, these systems offer more naturalistic support than traditional assistive technologies, thereby reducing dependency (Díaz et al., 2023). Translating neuro-cognitive principles into bio-inspired robotic systems Translating neuro-cognitive principles into bio-inspired robotic systems draws from the functioning of the human brain and nervous system to design robots that replicate similar processes (Aitsam et al., 2022). For example, neuromorphic systems emulate the brain's neural structures, enabling robots to process sensory data and make decisions like the human brain (Aitsam et al., 2022;Bartolozzi et al., 2022). Cognitive models such as ACT-R and SOAR simulate human cognition, including reasoning, memory, and problem-solving. By integrating these architectures, robots can perform complex tasks that require reasoning, planning, decision-making, and social interaction (Schilling et al., 2019). Robots can use artificial neural networks (ANNs) and deep learning algorithms for tasks like object recognition, speech processing, and environmental mapping, enabling autonomous navigation in dynamic environments (Hua et al., 2021). As the brain allocates cognitive resources based on task demands, robots can adjust their cognitive workload depending on task complexity. By prioritizing tasks and adjusting focus, robots can manage multi-step processes more effectively, much like humans shift attention according to environmental demands. This gives potentials to support interactions in rehabilitation or elderly care (Zhao and Zeng, 2022). Moreover, by adding neuro-cognitive motor learning principles involving refining of motor skills through feedback and adaptation, robots can employ reinforcement learning algorith§ms to improve their actions over time, adapting motor commands based on environmental feedback, which is crucial for dynamic tasks like walking and object manipulation (Kroemer et al., 2021). This might also be combined sensory feedback (e.g., cameras, accelerometers) to adjust movements in real-time, such as modifying grip strength or avoiding obstacles (Mikolajczyk et al., 2022). Translating neuro-cognitive principles into bio-inspired robotic systems allows robots to not only interact with their environment physically but also process information, learn, and adapt. By mimicking biological processes like learning, perception, and decision-making, robots can function more intuitively, enhancing their utility in fields such as healthcare, manufacturing, and human-robot collaboration. Current research also showed that intent-driven, intuitive interfaces minimize cognitive load, respond predictively to user intent, and adapt to individual variability in signal patterns (Dritsas and Trigka, 2025). Reinforcement learning, and user-in-the-loop design paradigms are enabling prosthetic and robotic devices to adjust in real time to environmental challenges, fatigue, or changes in neural signal quality (Díaz et al., 2023). However, it should be noted that challenges remain. Long-term reliability of neural signal acquisition, especially in non-invasive systems, and inter-individual variability must be addressed. Furthermore, accessibility is important in terms of affordability and scalability. Future perspectives focusing on the convergence of neuro-cognitive science, biomechatronics, AI, and material innovation (e.g., soft robotics, smart textiles) will push neuroprosthetics and BCIs from niche rehabilitation tools toward augmentative technologies, enhancing both impaired and able-bodied performance. Applications of bio-inspired design in wearable technology and rehabilitation In clinical rehabilitation, wearable devices such as assistive exosuits, sensor-embedded garments, and smart orthoses are used to support recovery after stroke, spinal cord injury, or orthopedic trauma (Bardi et al., 2022). Their bio-inspired design allows for task-specific training and neuroplasticity-driven motor re-learning (Bardi et al., 2022). These systems enable high-intensity, repetitive practice without requiring constant therapist supervision. By decentralizing care from the clinic to the home, wearable technologies offer new pathways for long-term, self-managed recovery. Additionally, cognitive-motor interaction is increasingly recognized as vital in rehabilitation. Wearables equipped with neurocognitive monitoring tools, such as EEG, can assess and modulate attentional load, dual-task performance, and mental fatigue (Marchand et al., 2021;Swerdloff and Hargrove, 2023;Riedel et al., 2024;Song et al., 2024), allowing for personalized interventions. In occupational settings, exosuits have been shown to reduce musculoskeletal strain during lifting or overhead tasks, reducing injury risk while maintaining productivity (Refai et al., 2023). Bio-inspired wearables can also benefit physical activity and sports performance. In sports science, wearable systems monitor biomechanics and neuromuscular dynamics to optimize performance and prevent overuse injuries (Yang et al., 2024). Despite significant advances, it should be mentioned that issues remain such as weight, cost, comfort, and user acceptance, limiting widespread adoption. Moreover, the integration of wearable systems with neurocognitive data raises concerns about privacy, autonomy, and long-term behavioral impacts. Therefore, future research is needed in terms of modular and scalable architectures for individualized use, gender-and age-inclusive design, and multidisciplinary validation frameworks. Furthermore, future developments are moving toward wearables that can adapt to the user and co-evolve with them over time, using reinforcement learning and cloud-based neural networks (Kristensen and Ruckenstein, 2018). These will enable personalized baseline tracking, predictive analytics, and context-aware support across diverse environments. 4. New insights from the article collection Within this special issue all the articles focus on how cognitive and motor functions are interconnected, particularly in the context of rehabilitation, external devices, or social interactions. Some studies focus on external devices (i.e., exoskeletons, virtual reality or exergames), while others focus on social dynamics (i.e., partner absence) and how this influence cognitive functioning. Within wearable technology use, Wollesen et al. (Wollesen et al., 2024) aim to evaluate how upper-extremity exoskeletons impact cognitive resources, posture, and muscle synergies during physically and cognitively demanding tasks. This study protocol investigates the interplay between physical assistance and cognitive function, crucial for understanding how technology can optimise human performance while minimising strain. The researchers plan to use motion analysis (3D), functional near-infrared spectroscopy (fNIRS) and surface electromyography (sEMG) to capture motor performance, muscle activation, and brain activation. By exploring gender-specific effects, the study addresses whether current exoskeleton designs accommodate diverse anthropometric needs, thereby influencing muscle activation and cognitive resource allocation. Gräf et al. (Gräf et al., 2024) examined how a passive exoskeleton affects both physical and cognitive functions during overhead tasks. They found that using the exoskeleton reduced shoulder muscle activity and improved cognitive performance, especially in dual-task situations following fatigue. Outcomes highlight the relationship between physical support via exoskeletons and neurocognitive resources in demanding tasks. Büttiker et al. (Büttiker et al., 2024) designed a pilot study to explore cognitive-motor exergame training for stroke patients. The study aims to improve cognitive and physical functions through exercises that combine motor tasks and cognitive challenges. The research emphasizes neuroplasticity and the potential of combining cognitive and physical rehabilitation for stroke recovery, targeting the brain-body connection. Maricot et al. (Maricot et al., 2024b) studied the reliability of the Reactive Balance Test (RBT) in individuals with chronic ankle instability (CAI). The the role of cognitive processes, like reaction time and in maintaining Their that balance performance is to cognitive processing, it a for motor control in rehabilitation in with et al. et al., 2024) how the of an partner cognitive and responses during They found that the of a partner particularly in terms of focus and This study on how social dynamics cognitive processes, such as and cognitive control, during social interactions. et al. et al., a study protocol of a with allocation in stroke patients. 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