Search Results (59)

Intention prediction is essential for enabling intuitive and adaptive control in upper-limb exoskeletons, especially in dynamic industrial environments. However, the suitability of different cues, sensors, and computational models for real-world industrial applications remains unclear. This systematic review, conducted according to PRISMA guidelines, analyzes 29 studies published between 2007 and 2024 that investigate intention prediction in active exoskeletons. Most studies rely on motion capture (14) and electromyography (14) to estimate joint torque or trajectories, predicting from 450 ms before to 660 ms after motion onset. Approaches include model-based and model-free regression, as well as classification methods, but vary significantly in complexity, sensor setups, and evaluation procedures. Only a subset evaluates usability or support effectiveness, often under laboratory conditions with small, non-representative participant groups. Based on these insights, we outline recommendations for robust and adaptable intention prediction tailored to industrial task requirements. We propose four generalized support modes to guide sensor selection and control strategies in practical applications. Future research should leverage wearable sensors, integrate cognitive and contextual cues, and adopt transfer learning, federated learning, or LLM-based feedback mechanisms. Additionally, studies should prioritize real-world validation, diverse participant samples, and comprehensive evaluation metrics to support scalable, acceptable deployment of exoskeletons in industrial settings. Full article

Increased forearm activity may reflect greater steering input or control effort, which, if optimised, could reduce cornering time and thereby improve lap performance. This proof-of-concept case study aimed to quantify forearm muscle activity during two test sessions, with data-driven driver feedback in between sessions to inform steering technique. One ex-professional European karting driver was recruited for this study. A 20 Hz GPS was mounted on the kart, and two electromyography sensors were attached to the left and right flexor digitorum superficialis. In session one, the driver completed 19 laps; EMG data from the fastest lap (51.99 s) were analysed and used to provide feedback. In session two, the driver completed 20 laps, achieving a best time of 51.60 s. EMG analysis revealed greater left forearm activity during left-hand corners in session one, shifting to greater right forearm activity during right-hand corners in session two. The 0.39 s improvement in lap time suggests that EMG-informed feedback may influence steering technique and enhance performance. These findings highlight the potential of integrating EMG analysis into driver coaching, particularly in training and qualifying contexts. Full article

Ensuring that older adults perform chair-based exercises (CBEs) correctly is essential for improving physical outcomes and reducing the risk of injury, particularly in home and community rehabilitation settings. However, evaluating the correctness of movements accurately and objectively outside clinical environments remains challenging. In this study, camera-based methods have been used to evaluate practical exercise quality. A single-camera system utilizing MediaPipe pose estimation was used to capture joint angle data as twenty older adults performed eight CBEs. Simultaneously, surface electromyography (sEMG) recorded muscle activity. Participants were guided to perform both proper and commonly observed incorrect forms of each movement. Statistical analyses compared joint angles and sEMG signals, and a support vector machine (SVM) was trained to classify movement correctness. The analysis showed that correct executions consistently produced distinct joint angle patterns and significantly higher sEMG activity than incorrect ones (p < 0.001). After modifying the selection of joint angle features for Movement 5 (M5), the classification accuracy improved to 96.26%. Including M5, the average classification accuracy across all eight exercises reached 97.77%, demonstrating the overall robustness and consistency of the proposed approach. In contrast, high variability across individuals made sEMG less reliable as a standalone indicator of correctness. The strong classification performance based on joint angles highlights the potential of this approach for real-world applications. While sEMG signals confirmed the physiological differences between correct and incorrect executions, their individual variability limits their generalizability as a sole criterion. Joint angle data derived from a simple single-camera setup can effectively distinguish movement quality in older adults, offering a low-cost, user-friendly solution for real-time feedback in home and community settings. This approach may help support independent exercise and reduce reliance on professional supervision. Full article

Hands are central to nearly every aspect of daily life, so losing an upper limb due to amputation can severely affect a person’s independence. Robotic prostheses offer a promising solution by mimicking many of the functions of a natural arm, leading to an increasing need for advanced prosthetic designs. However, developing an effective robotic hand prosthesis is far from straightforward. It involves several critical steps, including creating accurate models, choosing materials that balance biocompatibility with durability, integrating electronic and sensory components, and perfecting control systems before final production. A key factor in ensuring smooth, natural movements lies in the method of control. One popular approach is to use electromyography (EMG), which relies on electrical signals from the user’s remaining muscle activity to direct the prosthesis. By decoding these signals, we can predict the intended hand and arm motions and translate them into real-time actions. Recent strides in machine learning have made EMG-based control more adaptable, offering users a more intuitive experience. Alongside this, researchers are exploring tactile sensors for enhanced feedback, materials resilient in harsh conditions, and mechanical designs that better replicate the intricacies of a biological limb. This review brings together these advancements, focusing on emerging trends and future directions in robotic upper-limb prosthesis development. Full article

A novel electromyography (EMG)-based wheelchair interface was developed that uses contractions from the temporalis muscle to control a wheelchair. To aid in the training process for users of this interface, a serious training game, Limbitless Journey, was developed to support patients. Amyotrophic Lateral Sclerosis (ALS) is a condition that causes progressive motor function loss, and while many people with ALS use wheelchairs as mobility devices, a traditional joystick-based wheelchair interface may become inaccessible as the condition progresses. Limbitless Journey simulates the wheelchair interface by utilizing the same temporalis muscle contractions for control of in-game movements, but in a low-stress learning environment. A usability study was conducted to evaluate the serious-game-based training platform. A major outcome of this study was qualitative data gathered through a concurrent think-aloud methodology. Three cohorts of five participants participated in the study. Audio recordings of participants using Limbitless Journey were transcribed, and a sentiment analysis was performed to evaluate user perspectives. The goal of the study was twofold: first, to perform a think-aloud usability study on the game; second, to determine whether accessible controls could be as effective as manual controls. The user comments were coded into the following categories: game environment, user interface interactions, and controller usability. The game environment category had the most positive comments, while the most negative comments were primarily related to usability challenges with the flexion-based controller. Interactions with the user interface were the main topic of feedback for improvement in future game versions. This game will be utilized in subsequent trials conducted at the facility to test its efficacy as a novel training system for the ALS population. The feedback collected will be implemented in future versions of the game to improve the training process. Full article

Background: In this study, we used intraoperative neurophysiological monitoring (IONM) during anterior cervical discectomy and fusion (ACDF). Rather than emphasizing its use for safety purposes, our goal was to evaluate how neurophysiological parameters change during surgery and their correlation with clinical findings. Methods: This study included 30 patients who underwent ACDF. Detailed neurological examination was performed together with manual muscle testing (MMT), the Numeric Pain Rating Scale (NPRS), and the Neck Disability Index (NDI) questionnaire. During surgery, somatosensory-evoked potentials (SSEPs), motor-evoked potentials (MEPs), and spontaneous electromyography were registered. Results: There were statistically significant difference in the latency and amplitude of SSEPs of the right median nerve. Regarding the left median nerve, there was a statistically significant difference in amplitude, but not in latency. Differences were also observed in the amplitudes of right and left tibial nerve SSEPs, though no significant differences were found in their latencies. No statistically significant difference was found in the threshold values required to elicit MEPs between the beginning and end of the surgery. Additionally, we found a statistically significant positive correlation between the latency of the left and right median nerve and the left tibial nerve with somatosensory impairment. There was also a significant negative correlation between the amplitude of both tibial nerves and somatosensory impairment, and their latency showed a significant negative correlation with pain level before surgery. We found statistically significant decreases in NDI and pain level values one month after surgery. Conclusions: The results show significant changes in SSEPs and a correlation between clinical and neurophysiological findings and emphasize the importance of using MEPs to assess the condition of the motor system. Additionally, there was a general improvement in the patients’ condition, as assessed by NDI and pain scores. This study identifies critical surgical phases to consider in the absence of real-time neuromonitoring feedback and emphasizes that clinical observations may not fully reflect the condition of neurological structures in patients with myelopathy, which is crucial when deciding on timely surgery. Full article

Musculoskeletal disorders are prevalent among medical professionals like dentists, who often maintain prolonged, ergonomically disadvantageous postures. This study aims to evaluate the feasibility and efficacy of a wearable sensor-based monitoring and feedback system designed to improve posture and evaluate muscle recruitment. Thirty-five healthy adults participated in a controlled experiment, performing a typing task under various postural conditions with and without haptic feedback. Surface electromyography sensors measured muscle activity in the upper trapezius and infraspinatus muscles, while inertial measurement units tracked spine orientation. The results indicated that haptic feedback significantly influenced muscle activity and posture. Feedback reduced deviations from the desired postures but increased muscle activity in certain conditions. Cognitive performance, measured by typing speed, decreased with feedback, suggesting a trade-off between maintaining posture and the performance of the task. These findings highlight the potential of haptic feedback in ergonomic interventions to mitigate MSDs. Future research should explore the long-term effects and optimize feedback mechanisms to balance posture correction and cognitive demands. Full article

Insufficient sleep causes muscle fatigue, impacting performance. The mechanism of brain–muscle signaling remains uncertain. In this study, we examined the impact of sleep deprivation on muscle endurance during isometric contractions and explored the changes in brain–muscle connectivity. Methods: The research involved 35 right-handed male participants who took part in an exercise test that included isometric contractions of the left and right biceps in both sleep-deprived and well-rested states. Muscle contraction duration and electroencephalogram (EEG) and electromyography (EMG) signals were recorded. Functional connectivity between brain regions was assessed using the phase locking value (PLV), while partial directed coherence (PDC) was used to analyze signal directionality between motor centers and muscles. Results: The connectivity strength between Brodmann areas (BAs) 1-5 and the right BA6, 8 regions was significantly decreased in the isometric contractions after sleep deprivation. Insufficient sleep enhanced the PDC signals from the motor center of the right brain to the left biceps, and it decreased the PDC signals from both biceps to their opposite motor centers. Conclusions: Sleep deprivation shortened muscle isometric contraction duration by affecting the interaction between the somatosensory motor cortex and the right premotor cortex, reducing biceps feedback signal connectivity to the contralateral motor center in the brain. Full article

Stroke is a leading cause of disability worldwide. The long-term effects of a stroke depend on the location and size of the affected brain area, resulting in diverse disabilities and experiences for survivors. More than 70% of people experiencing stroke suffer upper-limb dysfunction, which can significantly limit independence in daily life. The growing strain on national healthcare resources, coupled with the rising demand for personalised, home-based rehabilitation, along with increased familiarity with digital technologies, has set the stage for developing an advanced therapeutics system consisting of a wearable solution aimed at complementing current stroke rehabilitation to enhance recovery outcomes. Through a user-centred approach, supported by primary and secondary research, this study has developed an advanced prototype integrating electromyography smart sensors, functional electrical stimulation, and virtual reality technologies in a closed-loop system that is capable of supporting personalised recovery journeys. The outcome is a more engaging and accessible rehabilitation experience, designed and evaluated through the participation of stroke survivors. This paper presents the design of the therapeutic platform, feedback from stroke survivors, and considerations regarding the integration of the proposed technology across the stroke pathway, from early days in a hospital to later stage rehabilitation in the community. Full article

Background and Objectives: While shoulder injuries represent the musculoskeletal disorders (MSDs) most encountered in physical therapy, there is no consensus on their management. In attempts to provide standardized and personalized treatment, a robotic-assisted device combined with EMG biofeedback specifically dedicated to shoulder MSDs was developed. This study aimed to determine the efficacy of an 8-week rehabilitation program (3 sessions a week) using a robotic-assisted device combined with EMG biofeedback (RA-EMG group) in comparison with a conventional program (CONV group) in patients presenting with shoulder MSDs. Materials and Methods: This study is a retrospective cohort study including data from 2010 to 2013 on patients initially involved in a physical rehabilitation program in a private clinic in Chicoutimi (Canada) for shoulder MSDs. Shoulder flexion strength and range of motion were collected before and after the rehabilitation program. Forty-four patients participated in a conventional program using dumbbells (CONV group), while 73 completed a program on a robot-assisted device with EMG and visual biofeedback (RA-EMG group); both programs consisted of two sets of 20 repetitions at 60% of maximal capacity. Results: We showed that the RA-EMG had significantly greater benefits than the CONV group for shoulder flexion strength (4.45 [2.6;6.15] kg vs. 2.3 [0.90;4.775] kg, U = 761, p = 0.013) and for normalized strength (77.5 [51.3;119.1] % vs. 39.1 [16.6;89.2] %, U = 755, p = 0.016). In addition, the RA-EMG group showed a trend to greater absolute gain of ROM than the CONV group (10.0 [0;24.3] degrees vs. 5.5 [0;12.0] degrees, U = 1931, p = 0.067), and a greater benefit in normalized ROM was observed for the RA-EMG (7.4. [0;17.7] %) than the CONV group (4.6 [0;10.8], U = 1907, p = 0.046). Conclusions: The current retrospective cohort study showed that a specific and tailored 8-week rehabilitation program with constant effort by automatic adjustment of the level of resistance by EMG feedback induced greater benefits for shoulder flexion strength and a trend to improve range of motion compared to conventional rehabilitation in patients with shoulder MSDs. Future research should be pursued to determine the added potential of this approach for abduction and external rotation with a randomized controlled design. Full article

The paper presents an innovative virtual reality (VR)-based environment for personalized telerehabilitation programs. This environment integrates a parallel robotic structure designed for the lower limb rehabilitation of patients with neuromotor disabilities and a virtual patient. The robotic structure is controlled via a user interface (UI) that communicates with the VR environment via the TCP/IP protocol. The robotic structure can also be operated using two controllers that communicate with a VR headset via the Bluetooth protocol. Through these two controllers, the therapist demonstrates to the patient various exercises that the robotic system can perform. With the right-hand controller, the therapist guides exercises for the hip and knee, while the left-hand controller manages ankle exercises. The therapist remotely designs a rehabilitation plan for patients at home, defining exercises, interacting with the rehabilitation robot in real-time via the VR headset and the two controllers, and initiating therapy sessions. The user interface allows monitoring of patient progress through video feedback, electromyography (EMG) sensors, and session recording. Full article

In an intelligent driving environment, monitoring the physiological state of drivers is crucial for ensuring driving safety. This paper proposes a method for monitoring and analyzing driver physiological characteristics by combining electronic vehicle identification (EVI) with multimodal biometric recognition. The method aims to efficiently monitor the driver’s heart rate, breathing frequency, emotional state, and fatigue level, providing real-time feedback to intelligent driving systems to enhance driving safety. First, considering the precision, adaptability, and real-time capabilities of current physiological signal monitoring devices, an intelligent cushion integrating MEMSs (Micro-Electro-Mechanical Systems) and optical sensors is designed. This cushion collects heart rate and breathing frequency data in real time without disrupting the driver, while an electrodermal activity monitoring system captures electromyography data. The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle’s operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver’s physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring. Full article

Bionic prosthetic hands hold the potential to replicate the functionality of human hands. The use of bionic limbs can assist amputees in performing everyday activities. This article systematically reviews the research progress on bionic prostheses, with a focus on control mechanisms, sensory feedback integration, and mechanical design innovations. It emphasizes the use of bioelectrical signals, such as electromyography (EMG), for prosthetic control and discusses the application of machine learning algorithms to enhance the accuracy of gesture recognition. Additionally, the paper explores advancements in sensory feedback technologies, including tactile, visual, and auditory modalities, which enhance user interaction by providing essential environmental feedback. The mechanical design of prosthetic hands is also examined, with particular attention to achieving a balance between dexterity, weight, and durability. Our contribution consists of compiling current research trends and identifying key areas for future development, including the enhancement of control system integration and improving the aesthetic and functional resemblance of prostheses to natural limbs. This work aims to inform and inspire ongoing research that seeks to refine the utility and accessibility of prosthetic hands for amputees, emphasizing user-centric innovations. Full article

Background: Movement feedback is used to promote anatomically correct movement patterns. Two primary forms of movement feedback exist: verbal cues and visual cues. There is ongoing debate regarding which type of feedback yields superior effects for learning desired movements. This study investigated how a combination of visual and verbal cues improved shoulder stability in four arm movements, Biceps Curls, Reverse Flys, Rowing, and Shoulder Extensions. Methods: Twelve participants were allocated to three different conditions and instructed to perform four different arm movements: Condition 1 (no specific instructions), Condition 2 (image only), and Condition 3 (verbal cues and image). Measurements of acromioclavicular (AC) joint displacement, and electromyography (EMG) peak and burst duration were taken for each arm movement within each condition. Results: Condition 3 exhibited a significant reduction in AC displacement and prolonged EMG burst duration. Variations in EMG peak and burst duration across different arm movements were attributed to anticipated muscle activation specific to each movement. Conclusions: The combination of visual and verbal cues through the “reConnect Your Dots” movement language was found to improve scapular stabilization and associated muscle activation. This approach to movement patterns practice holds promise for injury rehabilitation and risk mitigation for future occurrences. Full article

Upper-limb paralysis requires extensive rehabilitation to recover functionality for everyday living, and such assistance can be supported with robot technology. Against such a background, we have proposed an electromyography (EMG)-driven hybrid rehabilitation system based on motion estimation using a probabilistic neural network. The system controls a robot and functional electrical stimulation (FES) from movement estimation using EMG signals based on the user’s intention, enabling intuitive learning of joint motion and muscle contraction capacity even for multiple motions. In this study, hybrid and visual-feedback training were conducted with pointing movements involving the non-dominant wrist, and the motor learning effect was examined via quantitative evaluation of accuracy, stability, and smoothness. The results show that hybrid instruction was as effective as visual feedback training in all aspects. Accordingly, passive hybrid instruction using the proposed system can be considered effective in promoting motor learning and rehabilitation for paralysis with inability to perform voluntary movements. Full article