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

Ramona Ritzmann^1*Kevin De Pauw²Bettina Wollesen³

• ¹University of Freiburg, Freiburg, Germany
• ²Vrije Universiteit Brussel, Brussel, Belgium
• ³German Sports University Cologne, Cologne, Germany

This Editorial provides an in-depth overview of neuro-cognition and its significance in human movement bridging fundamental research to bio-inspired innovations. Accurate motor execution involves neurocognitive aspects integrating somatosensory inputs that lead to situation-specific motor responses (Muellbacher et al., 2001;Todorov and Jordan, 2002). Neurocognitive learning happens consciously and unconsciously on the spinal and supraspinal levels and allows progressive improvement in motor precision and efficiency (Ioannucci et al., 2021). Thereby, the central nervous system governs the movements and enables coordinated synergistic muscle function, which is essential for adequate performance in voluntary motor tasks in physiological and pathological conditions (Kami et al., 1995). These motor tasks include, for example, reaching and grasping, fine motor commands with our hands or whole-body posture, and locomotor control. Paramount evidence exists that the central nervous system is plastic and adapts immediately to spontaneous stimuli referring to an extended cognitive learning algorithm over time. These neurocognitive adaptations are acknowledged to reflect changes in the structure and function with certain adaptation mechanisms still under debate (Muellbacher et al., 2001).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)(Lit). 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) (Lit). Examples can be found in smart sensors, feedback-based exoskeletons (Afschrift et al., 2023;Refai et al., 2023), sensors (Manakitsa et al., 2024b), 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). This Research Topic aims to provide high-quality knowledge in the field of neurocognitive functions under physiological and pathophysiological conditions by linking traditional experiments conducted with reference to the brain and spinal cord with new technologies. In addition to experiments on humans, IT solutions and hardware developments at the interface with industry are also included. The combination of interdisciplinary findings has the potential to illuminate results from complementary perspectives, enriching methodological advances and research results.Foundation of neuro-cognition controlling movement entails evidence from brain experimentation, psychological studes and neuro-cognition under consideration of sensory-motor interfaces in human movement.Neuro-mechanics in reactive movementPAGE \* Arabic \* MERGEFORMAT 3Neuroplasticity, or the brain's ability to reorganize itself by forming new neural connections, plays a pivotal role in motor learning and adaptation. The process is driven by repeated movements and taskspecific training, leading to structural and functional changes in the brain (Schlaug, 2001;Horton et al., 2017). It is well established that motor learning involves the activation and remodeling of brain regions such as the primary motor cortex, cerebellum, and basal ganglia (Doyon et al., 2009). For example, functional neuroimaging studies have demonstrated that skilled motor tasks, such as those involved in sports or rehabilitation, can lead to enhanced cortical mapping (Johansen-Berg et al., 2002). Importantly, neuroplasticity is not limited to the child's and adult brain (Zhang et al., 2024); even during aging, motor learning can induce significant structural changes (Boyke et al., 2008). In the context of motor adaptation, these changes facilitate the brain's ability to modify its motor output in response to changing sensory inputs or task demands. The role of neuroplasticity in adaptation has been well-documented in both human and animal models. Recent work highlights the involvement of the cerebellum in error correction during motor adaptation, particularly in visuomotor tasks (Criscimagna-Hemminger et al., 2010).In sports and rehabilitation, motor learning and neuroplasticity contribute to improvements in performance through practice and feedback (de Sousa Fernandes et al., 2020). Neural circuits adapt in response to the challenge of improving coordination and technique. Additionally, neuroplasticity is critical in rehabilitation and recovery, particularly after neurological injuries such as stroke, where plastic changes in the motor cortex are linked to motor recovery (Nudo, 2003). Rehabilitation strategies involving task-specific practice, electrical stimulation, and robotic devices can further enhance neuroplasticity and promote functional recovery.Understanding these basic mechanisms has broader applications in robotics and neuroprosthetics, where bio-inspired systems are being developed to replicate the neural processes that govern movement. Brain-machine interfaces (BMIs), which allow direct communication between the brain and external devices, rely on understanding neural motor signals from the motor cortex (Lebedev and Nicolelis, 2017). These technologies have been transformative for individuals with paralysis or neurodegenerative disorders, allowing them to regain control over robotic prostheses or computer cursors using brain signals alone (Lebedev and Nicolelis, 2006). Additionally, research into neural plasticity-how the brain reorganizes itself after injury-has had profound implications for rehabilitation following stroke or spinal cord injury. Investigations into how the brain can adapt, particularly in the context of neurorehabilitation, have led to the development of therapies that aim to harness and enhance these adaptive processes to restore motor function (Kitago et al., 2015).Cognitive neuroscience and psychology provide crucial insights into the interplay between cognitive functions and motor control. Traditionally, movement was viewed primarily as a low-level process controlled by the motor cortex, spinal cord and brainstem. However, the work of cognitive neuroscientists has revealed that movement is tightly coupled with higher-order cognitive processes such as attention, memory, and decision-making (Koziol et al., 2012). The prefrontal cortex, known for its role in executive functions such as planning, inhibition, and working memory, is critically involved in goal-directed motor actions. This connection has been well-documented in studies that highlight the role of the prefrontal cortex in initiating and planning voluntary movements, as well as its involvement in motor inhibition (Miller and Cohen, 2001). Further, studies in cognitive psychology have emphasized that our understanding of motor actions goes beyond execution, with cognitive functions like attention and prediction playing key roles in how we move (Goldman-Rakic, 2007). One MERGEFORMAT area where cognitive neuroscience has made a major contribution is in the understanding of how the brain learns, re-learns and refines motor skills (Doyon and Benali, 2005).Current concepts of motor learning, central to cognitive neuroscience, are deeply intertwined with plasticity in the central nervous system (Raiola, 2017). Studies of motor imagery has demonstrated that only imagining an action without any muscle action recruits similar neural circuits as those used during actual execution, providing valuable insight into how the brain consolidates and refines motor skills (Grèzes et al., 1999;Jackson et al., 2003). Motor learning research has identified two key forms of learning: procedural and declarative. Procedural learning, crucial for the development of skilled movements, involves the basal ganglia and cerebellum, while declarative learning such as learning facts and events is associated with the hippocampus (Doyon et al., 2009). These findings have been pivotal in understanding how the brain encodes, stores, and retrieves motor skills (Doyon et al., 2009). This discovery has significant implications for understanding not only motor control but also how we learn new movements by observing others, a process that is foundational in rehabilitation therapies and inspiring bio-copied technologies (Rajkumar and Ganapathy, 2020). For example, in motor rehabilitation, techniques that involve observing and imitating motor actions can facilitate motor recovery by leveraging these mirror neuron systems to enhance neural plasticity and promote functional recovery (Lohse et al., 2014).In the realm of psychological studies, research on action planning and decision-making under uncertainty also underscores the cognitive aspect of movement (Kim et al., 2021). Complex movements require not only precise motor execution but also the ability to predict outcomes based on sensory input and environmental cues. Psychological theories on decision-making, such as the drift-diffusion model, have been applied to understand how the brain integrates information from multiple sources to select the most appropriate motor response in uncertain or dynamic environments (Ratcliff and McKoon, 2008). These insights have led to the development of advanced robotic systems that can adapt to unpredictable environments by simulating human-like decision-making processes during motor tasks (Tanzmeister et al., 2016).Sensory-motor integration is the foundational process that allows for the precise coordination of movement with permanent sensory feedback, ensuring adaptive and efficient motor control (Ackerley et al., 2016). This dynamic process is essential for activities ranging from basic motor actions like reaching and grasping to complex tasks such as walking or balancing (Nowak and Hermsdörfer, 2009). The integration of sensory information allows the brain to continuously monitor and adjust motor outputs in real-time to meet changing environmental demands (Nowak and Hermsdörfer, 2009). Sensory modalities such as proprioception, vision, and vestibular inputs provide essential information about body position, movement, and the surrounding environment, which is crucial for maintaining balance and coordination during movement (Mergner and Rosemeier, 1998).The brain regions responsible for sensory-motor integration beside the spinal cord itself include the parietal cortex, which integrates proprioceptive and visual information to guide goal-directed movements (Kaas, 1997). The posterior parietal cortex, in particular, is key in transforming sensory input into appropriate motor commands, especially in upper-extremity tasks like reaching or grasping, where sensory information from both the visual and proprioceptive systems must be integrated (Edwards et al., 2019). Research has shown that lesions in the parietal cortex led to deficits in spatial awareness and the ability to perform coordinated movements (Battaglia-Mayer et al., 2006). This connection between sensory input and motor action is further emphasized in the concept of the body schema, a mental representation of the body that updates in real-time to reflect changes in body position and movement. The body schema allows for the rapid integration of sensory feedback into motor behavior, ensuring that movements are accurately executed.The role of the cerebellum in sensory-motor integration is particularly significant, as it not only coordinates motor output but also helps process sensory feedback during ongoing movement (Atasavun Uysal and Düger, 2020). For example, during walking or running, the cerebellum receives continuous sensory input regarding limb position and muscle tension, and it adjusts motor commands accordingly to ensure smooth and coordinated movements (Galea, 2011). The cerebellum also plays a crucial role in motor learning, particularly in tasks that require repeated practice and refinement of motor skills. The ability of the cerebellum to adjust motor output based on sensory feedback is critical for adapting to novel or changing conditions, such as when learning a new motor skill or recovering from injury.In addition to basic sensory-motor integration, the concept of feedback and feedforward control is integral to understanding how the brain coordinates movement (Wulf et al., 2002). Feedback control involves the use of real-time sensory information to correct movements during their execution, while feedforward control anticipates sensory consequences and adjusts motor output in advance. For example, when reaching for an object, the brain uses both feedback from proprioceptors in the arm and visual input to adjust the trajectory of the hand (Luft, 2014). In contrast, feedforward control allows the brain to plan and execute movements based on previous experiences and anticipated sensory outcomes.These principles of sensory-motor integration are also applied in the development of bio-inspired robotic systems. By mimicking the way humans integrate sensory feedback with motor output, researchers have been able to create robots that can navigate complex environments, balance dynamically, and interact with humans in a natural and adaptive way (Tanzmeister et al., 2016). These advancements are transforming fields like assistive robotics, where robotic exoskeletons and prostheses rely on real-time sensory feedback to adjust their movements and respond to the user's intentions (Crea et al., 2021;De Bock et al., 2021;De Bock et al., 2022b;Coser et al., 2024;Costanzo et al., 2024).Control and learning of motor tasks entail various brain regions and the interplay of complex structures. Neuroplasticity of specific circuitries are involved in skilled motor behaviors and serve as models for computational and neurophysiological studies.Neuroplasticity, or brain plasticity, is the ability of the brain to reorganize its structure, functions, or connections in response to learning, progressive experience, or environmental changes (Joshua, 2022) . In the context of motor learning and adaptation, neuroplasticity refers to the dynamic alterations in neural circuits that occur when an individual learns or refines a motor skill, and how the brain adapts to new motor demands or recovering from injuries. The principle of neuroplasticity is central to understanding the brain's capacity to optimize motor behaviour (Maier et al., 2019). Motor learning, which is the process of acquiring or improving motor skills, involves modifications to the synaptic strength and connectivity within motor-related brain regions, particularly the primary motor cortex (M1), the cerebellum, and the basal ganglia (Kleim, 2011). This can lead to changes in representations, as seen in studies of cortical map plasticity, where M1 maps associated with body parts, such as fingers or arms, reorganize with training (Nudo, 2003).Neuroplasticity is not confined to the motor cortex but extends to other regions involved in motor control, such as the cerebellum, which is essential for motor coordination and error correction. During motor learning, synaptic changes in the cerebellum and its connections with the motor cortex contribute to the fine-tuning of movements, especially in tasks requiring precision and coordination, such as sports or rehabilitation activities. Moreover, the basal ganglia, through its feedback loops with the motor cortex, are involved in the initiation and modulation of movements, with neuroplastic changes contributing to skill acquisition, motor learning, and habit formation (Dorris et al., 2000).In sports, neuroplasticity enables athletes or novices to refine their movements through practice, leading to progressively more efficient and fluid motor skills. Expert athletes show a high degree of motor cortical reorganization, which improves their movement efficiency and reduces the cognitive load during performance (Ludyga et al., 2016). Similarly, neuroplasticity plays a critical role in rehabilitation, particularly after neurological injuries such as stroke or spinal cord injury. For instance, neuroplastic changes induced by physical therapy or training have been linked to functional recovery in patients, as rehabilitation programs enhance synaptic strength and promote new neural connections in areas such as the motor cortex and spinal cord circuits (Lang et al., 2009) . Brain-computer interfaces (BCIs) and robotic prosthetics also exploit neuroplasticity by providing feedback and promoting adaptive responses from the brain, helping patients regain lost functions through real-time interaction with external devices (Lebedev and Nicolelis, 2006). These technologies leverage the brain's inherent plasticity, allowing for new forms of sensory-motor integration.The process of neuroplasticity is influenced by various factors, including the frequency, intensity, and type of practice, as well as the individual's age and overall health. For example, neuroplastic changes are often more pronounced during the early stages of learning but may plateau with extensive practice (Kami et al., 1995). In neurorehabilitation, interventions that provide high levels of task-specific training or sensory feedback, such as virtual reality-based rehabilitation, have shown to produce more robust plasticity, promoting more significant improvements in motor function (Lang et al., 2009). The role of neuroplasticity in motor learning extends beyond injury recovery, forming the foundation for the development of advanced motor skills and the integration of new motor functions in both human performance and machine learning.Skilled motor behaviour relies on the interaction of various neural circuits that coordinate movement execution. The central nervous system (CNS) integrates sensory input with motor output through a network of interconnected brain regions that process and regulate movement. The primary motor cortex (M1) plays a central role in initiating voluntary movements, with the motor map in M1 corresponding to different body parts, such as the hands, arms, or legs. Skilled behaviours are often associated with enhanced motor representation in M1, particularly in areas corresponding to body parts requiring precise control, such as the fingers (Yokoi et al., 2018).The basal ganglia, a group of subcortical structures, are essential for movement initiation, modulation, and learning. The striatum, the major input nucleus of the basal ganglia, receives projections from cortical regions involved in motor control and sends output to other areas of the basal ganglia, including the globus pallidus and substantia nigra. This network regulates motor output through two pathways: the direct pathway, which facilitates movement initiation, and the indirect pathway, which inhibits unwanted movements. Disruptions in these pathways are implicated in movement disorders such as Parkinson's disease, where basal ganglia dysfunction leads to bradykinesia (slowness of movement) and rigidity (Albin et al., 1989). Additionally, the basal ganglia interact with the cerebellum, another crucial structure for motor control, particularly in the fine-tuning of voluntary movements and in the coordination of multi-joint activities. The cerebellum also plays a key role in motor learning, particularly in tasks requiring precise timing and error correction. Its involvement is vital for tasks such as reaching or hitting a ball in sports, where real-time sensory feedback is crucial for adjusting movements to match dynamic environmental conditions.Neuroplasticity within these neural circuits is particularly important during skill acquisition and refinement. Repeated practice and motor learning lead to modifications in the synaptic connections of these regions, with cortical representations becoming more efficient. For example, after learning to play a musical instrument, musicians show enhanced cortical maps of hand representation in M1, reflecting increased precision in finger movements (Kami et al., 1995). Similarly, in skilled athletes, the basal ganglia and cerebellum undergo functional changes that enable the individual to perform complex movements with minimal conscious effort (Ludyga et al., 2016). These neural adaptations are necessary for the automation of movements, allowing for fluid and coordinated actions during athletic performance.The role of neural circuits in skilled motor behaviour also extends to injury recovery and neuroprosthetics. In patients with motor deficits, rehabilitation protocols that focus on task-specific exercises have been shown to stimulate plastic changes in these neural circuits, particularly in the motor cortex and cerebellum (Lang et al., 2009). Additionally, research into brain-computer interfaces (BCIs) and robotic prosthetics has highlighted the importance of understanding these circuits to facilitate the integration of robotic devices with human movements. BCIs leverage brain activity to control external devices, while robotic prosthetics use feedback to improve motor control through the principle of neuroplasticity (Lebedev and Nicolelis, 2006). These systems promote the adaptation of neural circuits, allowing individuals to regain lost motor functions by integrating artificial limbs into natural motor behavior.Computational models and neurophysiological studies are invaluable tools in understanding the complex mechanisms underlying motor learning, adaptation, and control. Computational models offer a way to simulate and predict how the brain processes sensory and motor information, learns new skills, and adapts to changes in the environment A prominent computational model used in motor control is the optimal feedback control model, which posits that the brain uses sensory feedback to minimize the costs associated with movement execution, balancing the accuracy of motor performance with the costs of energy and time (Todorov and Jordan, 2002). According to this model, the brain continuously adjusts motor commands based on sensory feedback, ensuring that movements are smooth and accurate. It also helps explain how the brain adapts when faced with new challenges, such as an unfamiliar motor task or altered environmental conditions.Neurophysiological studies, on the other hand, provide direct insights into how the brain encodes and processes motor behaviour through measurements of neural activity. Electrophysiological recordings from the motor cortex, for example, reveal that motor learning is associated with changes in the firing patterns of neurons. During the learning process, motor cortical neurons increase their firing rate, and synchronized firing between neurons becomes more prominent, reflecting the strengthening of neural connections. This is consistent with the concept of experience-dependent plasticity, where repeated motor practice leads to enhanced synaptic efficiency and cortical reorganization. Additionally, techniques like transcranial magnetic stimulation (TMS) have demonstrated that motor cortical excitability increases with training, indicating that motor learning is accompanied by an overall increase in neural responsiveness (Muellbacher et al., 2001).Advanced neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), have further advanced our understanding of motor learning. fMRI studies show increased activation in motor-related areas of the brain during motor tasks, particularly in the sensorimotor cortex, while DTI studies reveal structural changes in white matter pathways that connect motor regions, reflecting the brain's adaptation to new motor behaviors (Boyke et al., 2008). These neurophysiological insights have been crucial in the development of motor rehabilitation techniques and the design of brain-computer interfaces. BCIs, for example, rely on understanding how motor cortical activity corresponds to movement intention, using real-time neural signals to control external devices (Lebedev and Nicolelis, 2006). Similarly, robotic prosthetics are being designed to incorporate adaptive motor control, guided by computational models of motor behavior and feedback mechanisms that mirror the brain's neuroplastic processes (Tanzmeister et al., 2016).Together, computational models and neurophysiological studies provide a comprehensive framework for understanding motor control, learning, and adaptation, with significant applications in fields ranging from sports performance to rehabilitation and neuroprosthetics.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.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 is the process through which individuals choose a course of action from a set of alternatives (Taylor, 2013). In sports, robotics, and everyday tasks, decision-making is dynamic and often occurs under conditions of uncertainty and time pressure. In sports, athletes must make splitsecond decisions that balance offensive and defensive strategies, adapt to unpredictable opponents, and optimize their physical performance (Wylie et al., 2018). Similarly, 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).Perception-action coupling refers to the dynamic, reciprocal relationship between sensory inputs and motor outputs that underlie coordinated action (Dumas and Fairhurst, 2021). In sports, this coupling is critical for tasks like ball tracking in tennis or predicting the movement of a defender in soccer. Athletes continually adjust their movements based on real-time sensory feedback, ensuring that actions are performed with maximum efficiency and accuracy (Vignais et al., 2009). 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 (1999). 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 sports, cognitive control mechanisms regulate the decisionmaking process by managing attention, inhibiting inappropriate responses, and adapting to environmental cues. The coupling of perception and action ensures that the athlete 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).Big potential exist to leverage socioeconomic effects of technological innovation on ageing, pathological conditions and shifts in social care.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).In the context of neurological disorders, e.g., stroke and Parkinson's disease, and degenerative conditions, such as Alzheimer's disease, bio-inspired systems are enabling real-time, personalized therapeutic interventions. Neuro-inspired robotic prosthetics, bionic limbs with adaptive control, and brain-computer interfaces (BCIs) are not only restoring lost functions but also enhancing neural plasticity and rehabilitation outcomes (Bates et al., 2020;Dillen et al., 2022). These innovations shift the paradigm from reactive care to proactive, precision-based medicine, thereby reducing long-term treatment costs and improving patient agency (Parvin et al., 2025).Bio-inspired robotics also have the potential to recalibrate the economics of social care. Socially interactive robots are being used in elderly care settings to provide cognitive stimulation, emotional support, and routine monitoring (Rincon Arango et al., 2025). This results in decreased pressure on human caregivers and also provides a scalable model for care provision, especially in regions facing caregiver shortages. Notably, by reducing hospital admissions and delaying the onset of frailty, these technologies might reduce costs for public health 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.Neuroprosthetics and brain-computer interfaces (BCIs) translate neural intent into physical action through human-machine integration, thus customizing robotics control (Diaz et al., 2023;Elashmawi et al., 2024). These systems provide pathways for restoring movement in individuals affected by paralysis, movement disorders, amputation, stroke, or neurodegenerative diseases. At the core of neuroprosthetic systems is the ability to decode brain activity related to movement intention, which mainly rely on brain activity (EEG) or muscle activity (EMG), and convert this data into commands for prosthetic limbs, exoskeletons, or assistive devices (Díaz et al., 2023;Elashmawi et al., 2024). This process draws directly from models of voluntary motor control, incorporating error correction, feedback adaptation, and muscle synergies (Gragera et al., 2025), which are fundamental to natural movement execution. Emerging approaches increasingly integrate high-density EMG to isolate single motor unit activity, enabling finer movement control. In parallel, BCIs employing non-invasive (EEG, fNIRS) and invasive (intracortical implants) modalities offer bidirectional communication-allowing motor intention decoding and somatosensory feedback delivery via cortical stimulation.Neuroprosthetics and BCI's act as neural co-processors, engaging and enhancing the user's own neuroplastic potential. The use of neuroprosthetics is associated with cortical reorganization and functional recovery (Bonizzato and Martinez, 2021). BCIs further enable motor learning in the absence of physical movement, by engaging motor networks through motor imagery (Dillen et al., 2023;Dillen et al., 2024). In rehabilitation settings, closed-loop BCI-neuroprosthetic systems support adaptive, taskspecific training, where feedback from the device guides progressive motor re-learning (Broccard et al., 2014). This neuro-cognitive engagement positively influences motor recovery and reduces dependence on long-term care.Current research 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.Wearable technologies grounded in bio-inspired design are redefining the landscape of physical rehabilitation and human augmentation. By combining sensory feedback integration, and musculoskeletal coordination, multisensory fusion systems become more intuitive, efficient, and personalized for restoring or enhancing motor function. This is achieved through information originating from the periphery and nervous system. For example, soft robotics, such as exosuits, mimic the compliance and flexibility of muscles and tendons, allowing for assistance during specific movements without impeding user mobility (Refai et al., 2023)). Similarly, neuromuscular models, human-in-the-loop optimization strategies and AI (machine and deep learning) inform the midlevel control algorithms that govern actuation, enabling real-time adaptation to user intent, fatigue, or environmental variability (Zhang et al., 2017;Coser et al., 2024). Many systems are designed around the concept of closed-loop control integrating multimodal sensing, such as inertial measurement units, EMG and pressure sensors, with adaptive algorithms that refine support based on joint angles or muscle intensities/synergies, replicating the real-time responsiveness of human motor control.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.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 upperextremity 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 tRBT evaluates the role of cognitive processes, like reaction time and accuracy, in maintaining postural stability. Their findings demonstrate that balance performance is closely linked to cognitive processing, making it a useful tool for assessing motor control in rehabilitation in patients with CAI. Jia et al. (Jia et al., 2024) explored how the absence of an intimate partner influences cognitive and emotional responses during competitive situations. They found that the lack of a partner enhances interbrain synchronization, particularly in terms of focus and empathy. This study sheds light on how social dynamics affect cognitive processes, such as emotional regulation and cognitive control, during social interactions. Cui et al. (Cui et al., 2025) published a study protocol of a single center, evaluator blinded, prospective, two arm parallel group randomized controlled trial with 1:1 allocation ratio in stroke patients. The experiment group receives a combined treatment of 360° VR video action observation and neuromuscular electrical stimulation. The control group receives a cbined treatment of virtual reality landscape observation combined with neuromuscular electrical stimulation. The Fugl-Meyer Assessment for Upper Extremity is the primary outcome of this study, Brunstrom Recovery Stages for Upper Extremity, Manual Muscle Test, Range of Motion, Modified Barthel Index, and Functional Independence Measure are the secondary outcomes. In addition, functional near-infrared spectroscopy (fNIRS) and surface electromyography (sEMG) are used to evaluate the activation of MNS brain regions and related muscles, respectively. Cognitive and Physical Integration is one topic that aligns with all manuscripts in this special issue. All studies examine the interplay between cognitive and physical functions, emphasizing how cognitive processes (i.e., such as attention, learning, memory, and decision-making) are influenced by physical tasks or external devices (i.e., exoskeletons, exergames, perturbation of posture control). Whether it's stroke recovery, balance control, or cognitive enhancement, each study involves some form of rehabilitation or performance improvement that requires both cognitive and motor functioning. Through measurements like fNIRS, tests for balance, and EEG hyper scanning, these studies highlight the importance of measuring and understanding the relationship between cognitive performance and motor control.Current research is still limited due to the lack of translation of fundamental science into scalable, human-centred applications. This gap reflects the need for interdisciplinary methodologies that bridge cognitive science, human movement science, biomedical and mechanical engineering.The core limitations in current research is the unidisciplinary nature of science. While neuroscience has made significant strides in understanding brain plasticity, motor learning, and sensory-motor integration, these insights mainly remain disconnected from robotics, materials science, and/or rehabilitation engineering. This approach hampers the design of technologies that align with the complexity of human behaviour, leading to solutions that are functionally effective but cognitively or socially misaligned, e.g., exoskeletons reduce muscle activity at the level of support (De Bock et al., 2022a), but still fail to account for mental and cognitive workload, user comfort, and user acceptance (De Bock et al., 2022b). These are factors critical for mass adoption of the technology (Crea et al., 2021).Much of the research is also conducted in laboratory conditions, offering limited insight into how technologies perform in dynamic, real-world settings. Although there is research showing that lab results cannot be simply transferred to in-field situations (De Bock et al., 2021), and an increasing amount of research is conducted in-field there is still a gap of task and user specific implementations (Crea et al., 2021). Additionally, current research frequently excludes age, gender, and anthropometric diversity in design and testing, despite the importance for adoption and usability. More recently, there is a push towards inclusion of for example female participants in robotics technology (Wollesen et al., 2024), advancing testing and designing technology for a wider range of individuals.While the physical aspects of movement assistance have advanced, the cognitive aspects remain underexplored. Technologies that support movement must also support decision-making, attention, emotion regulation, and social interaction. In human-in-the-loop optimization research, there is a push towards the inclusion of user preferences (Lee et al., 2023). Thus, bio-inspired systems must evolve to reflect the biomechanics of movement and neurocognition, incorporating feedback on mental fatigue, motivation, and executive functioning into the adaptive control strategies of devices and interfaces. To achieve this, research must integrate interdisciplinary co-creation, including interdisciplinary research teams combining neuroscience, robotics, AI, human movement science, ethics, but also living labs and real-world trials to evaluate devices in real-life environments. Furthermore, the integration of standardized protocols and open datasets to facilitate cross-study comparison and reproducibility is needed.In healthcare, neuro-cognitive technologies offer the opportunity for personalized and adaptive interventions. BCIs and neuroprosthetics enable individuals with paralysis, stroke, or neurodegenerative diseases to regain control over movement, communication, and daily activities, enhancing autonomy and reducing caregiver dependency. At the same time, wearable systems that integrate neurofeedback, gait analysis, and physiological monitoring allow for continuous, at-home rehabilitation, which is crucial for long-term recovery.Importantly, these systems allows for neuroplasticity to take place, reinforcing neural reorganization through task-specific training, which accelerates functional restoration and enhances cognitive-motor integration (Grosse-Wentrup et al., 2011). The incorporation of cognitive load monitoring and mental fatigue detection, through for example the use of EEG, into wearable neurotechnologies further supports the development of holistic rehabilitation paradigms. These tools have the potential to reduce healthcare burdens. By decentralizing rehabilitation and enabling early detection of functional decline, neuro-cognitive technologies can reduce the pressure on healthcare services and promote sustainable, community-based care models.In sports, neuro-cognitive innovations are rapidly becoming essential to optimizing motor precision, reaction times, and decision-making. Cognitive-motor training platforms (Maricot et al., 2024a) and neurofeedback-guided performance tools (Rydzik et al., 2023), and smart textiles, such as sensorized garments integrating EMG and IMU, enable athletes to refine their skills, improve sport performance, and optimize the rehabilitation process and return-to-sports decision. Such technologies might also support healthy ageing through the preservation of motor skills, coordination, and mobility, particularly in populations at risk of sedentary decline.In industrial settings, work-related musculoskeletal disorders are most prevalent at the back, shoulder, neck and lower back with prevalence values of over 50% (Govaerts et al., 2021). Neuro-cognitive systems are revolutionizing how humans interact with machines. Occupational exoskeletons integrating neural and biomechanics data, provide adaptive support for repetitive and/or strenuous tasks, hypothetically reducing musculoskeletal disorders and enhancing in general the quality of life of industrial employees. Furthermore, advanced human-robot interaction frameworks that involve multisensory systems, BCIs, attention-tracking, and emotion-sensitive interfaces enable machines to respond to human intent, cognitive load, and fatigue in real time-fostering safer and more efficient workplaces. This evolution supports the rise of collaborative robotics (cobots) that can dynamically adjust behavior based on user context and feedback.One of the trends in neurocognitive research and bio-inspired technology is the development of neuroadaptive systems. These are devices that continuously adjust to the user's cognitive and motor state through real-time interpretation of multimodal neural, physiological and biomechanical signals (Díaz et al., 2023). Brain-computer interfaces (BCIs), neuroprosthetics, and wearable robotics will increasingly integrate data from EEG, EMG, eye tracking, and IMU's to decode human movement intention, and modulate assistance based on fatigue, focus, stress, and learning progression, creating closed-loop interactions that reflect the dynamic nature of human sensorimotor control. The integration of bidirectional neural interfaces will enable more embodied and intuitive device use, which will advance applications from rehabilitation to augmented cognition.The next generation of bio-inspired neuro-technologies will be highly personalized. Digital twins will leverage AI and in-silico musculoskeletal modeling to simulate rehabilitation outcomes, optimize interventions, and guide training protocols tailored to each individual end-user. These models will allow clinicians and users to predict the effects of therapy, robotics configuration and customized control, or adapt workload on both motor performance and brain adaptation. Deep and reinforcement learning algorithms embedded within robotics devices will allow technologies to allow for customizing robotics control, continuously co-adapting depending on user's preferences and environment. This personalization will benefit user acceptance and long-term engagement. Future research will advance movement assistance in combination with cognitive functioning, such as attention, decision-making, emotional regulation, and social interaction. Devices will be expected to sense and adapt to these higher-order processes, fostering cognitive-physical integration critical for tasks in complex, realworld environments. This shift will probably allow advanced technologies to be used as both assistive and augmentative technologies.An aspect to be considered in future research is the importance of ethical and legal frameworks. These frameworks should include neural data governance (ownership, consent, data storage) and risk assessment for cognitive influence and behavioral modification.This Editorial discusses a pivotal advancement in the understanding of neuro-cognition and human movement, establishing the link between fundamental research and bio-inspired technological innovation. Through interdisciplinary work, this paper highlights the critical role of neuroplasticity, sensory-motor integration, and cognitive control in shaping human movement across contexts, from everyday tasks to elite sports, rehabilitation, and human-robot interaction. Research substantially advanced in terms of the mechanisms of motor learning and adaptation within the central nervous system, the importance of cognitive-motor coordination in dynamic environments, the translation of neurocognitive principles into wearable technology, exoskeletons, brain-computer interfaces, and assistive robotics, as well as the socioeconomic implications of neuro-cognitive innovations. Advancing science should include neuroscience, biomechanics, AI, engineering, and human movement science to create responsive, user-centered technologies that restore, assist and augment physiological functions.Future research should foster interdisciplinary collaboration, bridging cognitive neuroscience, human movement science, engineering, and clinical practice. Next steps should also prioritize real-world validation, ensuring innovations move from lab to life, with inclusive design that accounts for gender, age, and functional diversity. Additionally, ethical frameworks should be developed, addressing data privacy, neural autonomy, and long-term impacts of neurotechnologies. Furthermore, research should create open, shared datasets and protocols to support reproducibility, scalability, and faster cross-sector innovation.This Editorial gives future directions in which science will shape smart, adaptive technologies. By conducting interdisciplinary research, we are capable of developing technologies that can rehabilitate and assist, and even empower human potential in increasingly complex environments.