Search Results (65)

Kinesthetic motor imagery (KMI), the mental rehearsal of a motor task without its actual performance, constitutes one of the most common techniques used for brain–computer interface (BCI) control for movement-related tasks. The effect of neural injury on motor cortical activity during execution and imagery remains under investigation in terms of activations, processing of motor onset, and BCI control. The current work aims to conduct a post hoc investigation of the event-related potential (ERP)-based processing of KMI during BCI control of anthropomorphic robotic arms by spinal cord injury (SCI) patients and healthy control participants in a completed clinical trial. For this purpose, we analyzed 14-channel electroencephalography (EEG) data from 10 patients with cervical SCI and 8 healthy individuals, recorded through Emotiv EPOC BCI, as the participants attempted to move anthropomorphic robotic arms using KMI. EEG data were pre-processed by band-pass filtering (8–30 Hz) and independent component analysis (ICA). ERPs were calculated at the sensor space, and analysis of variance (ANOVA) was used to determine potential differences between groups. Our results showed no statistically significant differences between SCI patients and healthy control groups regarding mean amplitude and latency (p < 0.05) across the recorded channels at various time points during stimulus presentation. Notably, no significant differences were observed in ERP components, except for the P200 component at the T8 channel. These findings suggest that brain circuits associated with motor planning and sensorimotor processes are not disrupted due to anatomical damage following SCI. The temporal dynamics of motor-related areas—particularly in channels like F3, FC5, and F7—indicate that essential motor imagery (MI) circuits remain functional. Limitations include the relatively small sample size that may hamper the generalization of our findings, the sensor-space analysis that restricts anatomical specificity and neurophysiological interpretations, and the use of a low-density EEG headset, lacking coverage over key motor regions. Non-invasive EEG-based BCI systems for motor rehabilitation in SCI patients could effectively leverage intact neural circuits to promote neuroplasticity and facilitate motor recovery. Future work should include validation against larger, longitudinal, high-density, source-space EEG datasets. Full article

Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants’ median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2. Full article

(1) Background: This study aimed to assess whether older adults exhibit greater discrepancies between intended and actual motor unit recruitment, which could affect the quality of muscle activation and potentially increase the risk of falls. (2) Methods: Forty-eight physically active older women were assessed (65 ± 6 years, 164 ± 6 cm, and 76 ± 7 kg). The bioelectrical activity (EMG) of the vastus lateralis oblique (VLO) and vastus medialis oblique (VMO) muscles were assessed during isometric testing with the knee joint bent to 75 degrees. The participants were instructed to press against a stable bar for 5 s at a specific percentage of their perceived force level (at 15%, 30%, and 60% of MVC) when the EMG activity was recorded. Balance was assessed using a stabilometric platform in a standing position. (3) Results: In all three thresholds, the bioelectrical activity of the VLO and VMO muscles significantly deviated from what was expected under the assumption of a nearly linear relationship between muscle force and bioelectrical activity. In each of the three thresholds, it did not exceed 10% MVC and significantly differed only between the 15% and 60% MVC thresholds. No significant differences were found between the dominant and non-dominant sides. A significant relationship was observed between the sway area (Area 95%) and the activity of the non-dominant limb VLO muscle. (4) Conclusions: Our results suggest that older adults experience deficits in muscle activation perception, leading to discrepancies between intended and actual muscle engagement, which may affect functional task performance and potentially increase fall risk. Full article

This study investigated the causes of incorrect judgments in a motor task and examined differences between students with varying levels of judgment accuracy. Twenty-two seventh graders participated. Based on their estimated and actual scores in two volleyball serve trials, students were categorized into two groups: “low accuracy” and “high accuracy”. Before each trial, they estimated their scores according to the American Alliance for Health, Physical Education, Recreation, and Dance test. Following the trials, students were interviewed about their justifications and their confidence in the accuracy of their judgments. Independent sample t-tests indicated that both “low accuracy” and “high accuracy” students appeared to use metacognitive skills (t(20) = 0.82, p > 0.05). However, the “low accuracy” group lacked the declarative and procedural knowledge (t(20) = 4.59, p < 0.001) necessary for accurately evaluating their performance. Findings suggest that students focused more on outcome-based rather than process-based assessments when evaluating their performance. Enhancing students’ access to both theoretical and experience-based cues in sports may improve their ability to accurately judge their performances and foster greater confidence in lifelong participation in physical activities. Full article

Background: Minimally invasive surgical techniques, including laparoscopic and robotic surgery, have profoundly impacted surgical practice by improving precision, reducing recovery times, and minimizing complications. However, these modalities differ in their cognitive demands and skill acquisition requirements, which can influence the learning curve and operative performance. To assess and evaluate this variability across these modalities, a functional near-infrared spectroscopy (fNIRS) system is used as an objective method for monitoring cognitive activity in surgical trainees. fNIRS can provide insights and further our understanding of the mental demands of different surgical techniques and their association with varying task complexity. Objective: This study seeks to assess the influence of surgical modality (laparoscopy vs. robotic surgery) and task complexity (pick and place (PP) vs. knot tying (KT)) on cognitive workload through fNIRS. We compare real-world and simulation-based training environments to determine changes in brain activation patterns and task performance. Methods: A total of twenty-six surgical trainees (general and gynecologic surgery residents and specialists) participated in this study. Participants completed standardized laparoscopic and robotic surgical tasks at varying levels of complexity while their cognitive workload was measured using fNIRS. This study included both simulation-based training and real-world surgical environments. Hemodynamic responses in the prefrontal cortex (PFC), task completion times, and performance metrics were analyzed. Results: Laparoscopic surgery elicited higher activity changes in the prefrontal cortex, indicating increased cognitive demand compared with robotic surgery, particularly for complex tasks like knot tying. Task complexity significantly influenced mental load, with more intricate procedures eliciting greater neural activation. Real-world training resulted in higher cognitive engagement than simulation, emphasizing the gap between simulated and actual surgical performance. Conclusions: Cognitive workload was lower and significantly different during robotic surgery than during laparoscopy, potentially due to its ergonomic advantages and enhanced motor control. Simulation-based training effectively prepares surgeons, but the cognitive workload results indicate that it may not fully replicate real-world surgical environments. These findings reveal the importance of cognitive workload assessment in surgical education and suggest that incorporating neuroimaging techniques such as fNIRS into training programs could enhance skill acquisition and performance. Full article

Background/Objectives: The aim of this study is to shed light on activity-based prospective memory upon the awakening and its association with motor sleep inertia in different phenotypes of insomnia disorder. Methods: To this end, 67 patients with insomnia and 51 healthy controls took part in the study. After enrollment, previously proposed actigraphic quantitative criteria were adopted, and the following phenotypes of insomnia disorder were observed in the patient sample: sleep onset (n = 12), maintenance (n = 19), mixed (n = 17), and negative misperception (n = 19). Each participant had used the Micro Motionlogger Watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA) actigraph for one week. Actigraphic recording allowed for a description of both the activity-based prospective memory performance upon the awakening—by computing the time interval between sleep end and the time participants actually remembered to push the event-marker button of the actigraph—and the motor sleep inertia, i.e., the mean motor activity, minute-by-minute, in the first 60 min after sleep end in the morning. Results: Compared to healthy controls, a longer time interval was observed between sleep end and activity-based prospective memory performance in patients with mixed and maintenance insomnia. Moreover, a significant association was highlighted between motor sleep inertia and the activity-based prospective memory performance: higher levels of motor activity in those who remembered to perform the memory task early after sleep end, that spread over a longer time interval in maintenance and mixed insomnia. Conclusions: Overall, the present results seem to highlight a more marked cognitive inertia in patients with mixed and maintenance insomnia as well as a significant association between motor and cognitive inertia that spreads over a different time interval according to the phenotype of insomnia. Full article

Virtual Body Ownership Illusion (Virtual BOI) refers to the perceptual, cognitive, and behavioral changes that occur due to the illusion that a virtual body is one’s own actual body. Recent research has focused on inducing Virtual Body Ownership Illusion (Virtual BOI) using various physical conditions of VR environments such as haptic feedback and 360-degree immersion, among others. The level of Virtual BOI has been recognized as an important factor in VR-based clinical therapy programs where patient immersion is crucial. However, a common issue is the lack of standardized evaluation tools for Virtual BOI, with most experiments relying on ad hoc tools based on experimental conditions or lacking consideration for the physical design elements of VR. This measurement tool was designed to consider the characteristics of recent VR devices, such as haptics and hand tracking, in the design of experiments and questionnaires. The tool is composed of sub-attributes related to VR technology, including Embodiment, Presence, Visuo-tactile, Visuo-proprioceptive, and Visuo-Motor. Based on a review of the existing literature, we hypothesized that the Virtual BOI scores would vary depending on manipulation methods, viewpoints, and haptic conditions. An experiment was conducted with 39 participants, who performed the same task under four different conditions using a virtual hand. Virtual BOI scores were assessed using the evaluation tool developed for this study. The questionnaire underwent CFA, and three items with factor loadings below 0.5 were removed, resulting in a total of 14 items. Each subscale demonstrated high reliability, with Cronbach’s alpha values greater than 0.60. When developing experiments, clinical programs, or VR content related to Virtual BOI, the evaluation tool presented in this study can be used to assess the level of Virtual BOI. Additionally, by considering technological elements such as haptics and hand tracking, VR environments can be designed to enhance the level of Virtual BOI. Full article

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards. Full article

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper’s contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises. Full article

The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore damaged central nervous system function in patients. Patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb’s movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient’s expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient’s ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human–robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient’s movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient’s healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient’s pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements. Full article

Previous studies on the mechanisms underlying willed actions reported that the premotor cortex may be involved in the construction of motor awareness. However, its exact role is still under investigation. Here, we investigated the role of the dorsal premotor cortex (PMd) in motor awareness by modulating its activity applying inhibitory rTMS to PMd, before a specific motor awareness task (under three conditions: without stimulation, after rTMS and after Sham stimulation). During the task, subjects had to trace straight lines to a given target, receiving visual feedback of the line trajectories on a computer screen. Crucially, in most trials, the trajectories on the screen were deviated, and to produce straight lines, subjects had to correct their movements towards the opposite direction. After each trial, participants were asked to judge whether the line seen on the computer screen corresponded to the line actually drawn. Results show that participants in the No Stimulation condition did not recognize the perturbation until 14 degrees of deviation. Importantly, active, but not Sham, rTMS significantly modulated motor awareness, decreasing the amplitude of the angle at which participants became aware of the trajectory correction. These results suggest that PMd plays a crucial role in action self-monitoring. Full article

Virtual reality is used in rehabilitation and training simulators. However, whether movements in real and virtual spaces are similar is yet to be elucidated. The study aimed to examine the smoothness, trajectory, and velocity of participants’ movements during task performance in real and virtual space. Ten participants performed the same motor task in these two spaces, reaching for targets placed at six distinct positions. A head-mounted display (HMD) presented the virtual space, which simulated the real space environment. The smoothness of movements during the task was quantified and analysed using normalised jerk cost. Trajectories were analysed using the actual trajectory length normalised by the shortest distance to the target, and velocity was analysed using the time of peak velocity. The analysis results showed no significant differences in smoothness and peak velocity time between the two spaces. No significant differences were found in the placement of the six targets between the two spaces. Conversely, significant differences were observed in trajectory length ratio and peak velocity time, albeit with small effect sizes. This outcome can potentially be attributed to the fact that the virtual space was presented from a first-person perspective using an HMD capable of presenting stereoscopic images through binocular parallax. Participants were able to obtain physiological depth information and directly perceive the distance between the target and the effector, such as a hand or a controller, in virtual space, similar to real space. The results suggest that training in virtual space using HMDs with binocular disparity may be a useful tool, as it allows the simulation of a variety of different environments. Full article

People with fibromyalgia have been shown to experience more somatosensory disturbances than pain-free controls during sensorimotor conflicts (i.e., incongruence between visual and somatosensory feedback). Sensorimotor conflicts are known to disturb the integration of sensory information. This study aimed to assess the cerebral response and motor performance during a sensorimotor conflict in people with fibromyalgia. Twenty participants with fibromyalgia and twenty-three pain-free controls performed a drawing task including visual feedback that was either congruent with actual movement (and thus with somatosensory information) or incongruent with actual movement (i.e., conflict). Motor performance was measured according to tracing error, and electrocortical activity was recorded using electroencephalography. Motor performance was degraded during conflict for all participants but did not differ between groups. Time–frequency analysis showed that the conflict was associated with an increase in theta power (4–8 Hz) at conflict onset over the left posterior parietal cortex in participants with fibromyalgia but not in controls. This increase in theta suggests a stronger detection of conflict in participants with fibromyalgia, which was not accompanied by differences in motor performance in comparison to controls. This points to dissociation in individuals with fibromyalgia between an altered perception of action and a seemingly unaltered control of action. Full article

Currently, the estimated range of an electric vehicle is a variable value. The assessment of this power reserve is possible by various methods, and the results of the assessment by these methods will be quite different. Thus, building a model based on these cycles is an extremely important task for manufacturers of electric vehicles. In this paper, a simulation model was developed to determine the range of an electric vehicle by cycles of movement. A mathematical model was created to study the power reserve of an electric vehicle, taking into account four driving cycles, in which the lengths of cycles and the forces acting on the electric vehicle are determined; the calculation of the forces of resistance to movement was carried out taking into account the efficiency of the electric motor; thus, the energy consumption of an electric vehicle is determined. The modeling of the study of motion cycles on the presented model was carried out. The mathematical evaluation of battery life was based on simulation results. Simulation modeling of an electric vehicle in the MATLAB Simulink software environment was performed. An assessment of the power reserve of the developed electric vehicle was completed. The power reserve was estimated using the four most common driving cycles—NEDC, WLTC, JC08, US06. Studies have shown that the highest speed of the presented US06 cycle provides the shortest range of an electric vehicle. The JC08 and NEDC cycles have similar developed speeds in urban conditions, while in NEDC there is a phase of out-of-town traffic; therefore, due to the higher speed, the electric vehicle covers a greater distance in equal time compared to JC08. At the same time, the NEDC cycle is the least dynamic and the acceleration values do not exceed 1 m/s². Low dynamics allow for a longer range of an electric vehicle; however, the actual urban operation of an electric vehicle requires more dynamics. The cycles of movement presented in the article provide a sufficient variety and variability of the load of an electric vehicle and its battery over a wide range, which made it possible to conduct effective studies of the energy consumed, taking into account the recovery of electricity to the battery in a wide range of loads. It was determined that frequent braking, taking into account operation including in urban traffic, provides a significant return of electricity to the battery. Full article

Motor imagery (MI) is a technique of imagining the performance of a motor task without actually using the muscles. When employed in a brain–computer interface (BCI) supported by electroencephalographic (EEG) sensors, it can be used as a successful method of human–computer interaction. In this paper, the performance of six different classifiers, namely linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and three classifiers from the family of convolutional neural networks (CNN), is evaluated using EEG MI datasets. The study investigates the effectiveness of these classifiers on MI, guided by a static visual cue, dynamic visual guidance, and a combination of dynamic visual and vibrotactile (somatosensory) guidance. The effect of filtering passband during data preprocessing was also investigated. The results show that the ResNet-based CNN significantly outperforms the competing classifiers on both vibrotactile and visually guided data when detecting different directions of MI. Preprocessing the data using low-frequency signal features proves to be a better solution to achieve higher classification accuracy. It has also been shown that vibrotactile guidance has a significant impact on classification accuracy, with the associated improvement particularly evident for architecturally simpler classifiers. These findings have important implications for the development of EEG-based BCIs, as they provide valuable insight into the suitability of different classifiers for different contexts of use. Full article