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Item type:Publication, Control strategies for gait tele-rehabilitation system based on parallel robotics(MDPI, 2021-12-01)Among end-effector robots for lower limb rehabilitation, systems based on Stewart–Gough platforms enable independent movement of each foot in six degrees of freedom. Nevertheless, control strategies described in recent literature have not been able to fully explore the potential of such a mechatronic system. In this work, we propose two novel approaches for controlling a gait simulator based on Stewart–Gough platforms. The first strategy provides the therapist direct control of each platform using movement data measured by wearable sensors. The following scheme is designed to improve the level of engagement of the patient by enabling a limited degree of control based on trunk inclination. Both strategies are designed to facilitate future studies in tele-rehabilitation settings. Experimental results have illustrated the feasibility of both control interfaces, either in terms of system performance or user subjective evaluation. Technical capacity to deploy in tele-rehabilitation was also verified in this work. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A 3D-printed knee wearable goniometer with a mobile-app interface for measuring range of motion and monitoring activities(MDPI, 2022-02-01)Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs. The measurements of the wearable are compared with a commercial goniometer, and, with the Aktos-t system, a commercial motion capture system based on inertial sensors, at three speeds of gait (4.0 km/h, 4.5 km/h, and 5.0 km/h) in nine participants. Specifically, the four differences between maximum and minimum peaks in the gait cycle, starting with heel-strike, were compared by using the mean absolute error, which was between 2.46 and 12.49 on average. In addition, the algorithm was able to predict the three activities during online testing in one participant and detected on average 94.66% of the gait cycles performed by the participants during offline testing.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling and simulation of a 2SPU-RU parallel mechanism for a prosthetic ankle with three degrees of freedom(Multidisciplinary Digital Publishing Institute (MDPI), 2024-08-01)To assist an individual with an amputation in regaining daily quality of life, a 2SPU-RU type parallel mechanism was developed based on ankle biomechanics. The inverse kinematic analysis of this mechanism was performed using the vector method. Subsequently, the Jacobian matrices were analyzed. The dynamic model of the mechanism was then created based on the principle of virtual work, and its theoretical solution was compared with numerical results obtained in a simulation environment. Additionally, the validity of the dynamic model and the inverse kinematics was verified by comparing theoretical and simulation results for the movements of plantarflexion–dorsiflexion, eversion–inversion, and abduction–adduction during the gait cycle. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identification of the biomechanical response of the muscles that contract the most during disfluencies in stuttered speech(Multidisciplinary Digital Publishing Institute (MDPI), 2024-04-01)Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals’ quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30 individuals without stuttering, aged between 18 and 40, participated in the study. EMG signals from five facial and cervical muscles were recorded during speech tasks and analyzed for mean amplitude and frequency activity in the 5–15 Hz range to identify significant differences. Upon analysis of the 5–15 Hz frequency range, a higher average amplitude was observed in the zygomaticus major muscle for participants while stuttering (p < 0.05). Additionally, when assessing the overall EMG signal amplitude, a higher average amplitude was observed in samples obtained from disfluencies in participants who did not stutter, particularly in the depressor anguli oris muscle (p < 0.05). Significant differences in muscle activity were observed between the two groups, particularly in the depressor anguli oris and zygomaticus major muscles. These results suggest that the underlying neuromuscular mechanisms of stuttering might involve subtle aspects of timing and coordination in muscle activation. Therefore, these findings may contribute to the field of biosensors by providing valuable perspectives on neuromuscular mechanisms and the relevance of electromyography in stuttering research. Further research in this area has the potential to advance the development of biosensor technology for language-related applications and therapeutic interventions in stuttering. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The LIBRA NeuroLimb: Hybrid Real-Time Control and Mechatronic Design for Affordable Prosthetics in Developing Regions(Multidisciplinary Digital Publishing Institute (MDPI), 2023-12-22)Globally, 2.5% of upper limb amputations are transhumeral, and both mechanical and electronic prosthetics are being developed for individuals with this condition. Mechanics often require compensatory movements that can lead to awkward gestures. Electronic types are mainly controlled by superficial electromyography (sEMG). However, in proximal amputations, the residual limb is utilized less frequently in daily activities. Muscle shortening increases with time and results in weakened sEMG readings. Therefore, sEMG-controlled models exhibit a low success rate in executing gestures. The LIBRA NeuroLimb prosthesis is introduced to address this problem. It features three active and four passive degrees of freedom (DOF), offers up to 8 h of operation, and employs a hybrid control system that combines sEMG and electroencephalography (EEG) signal classification. The sEMG and EEG classification models achieve up to 99% and 76% accuracy, respectively, enabling precise real-time control. The prosthesis can perform a grip within as little as 0.3 s, exerting up to 21.26 N of pinch force. Training and validation sessions were conducted with two volunteers. Assessed with the "AM-ULA" test, scores of 222 and 144 demonstrated the prosthesis's potential to improve the user's ability to perform daily activities. Future work will prioritize enhancing the mechanical strength, increasing active DOF, and refining real-world usability. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, EMG and IMU Data Fusion for Locomotion Mode Classification in Transtibial Amputees(Multidisciplinary Digital Publishing Institute (MDPI), 2023-12-01)Despite recent advancements in prosthetic technology, lower-limb amputees often remain limited to passive prostheses, which leads to an asymmetric gait and increased energy expenditure. Developing active prostheses with effective control systems is important to improve mobility for these individuals. This study presents a machine-learning-based approach to classify five distinct locomotion tasks: ground-level walking (GWL), ramp ascent (RPA), ramp descent (RPD), stairs ascent (SSA), and stairs descent (SSD). The dataset comprises fused electromyographic (EMG) and inertial measurement unit (IMU) signals from twenty non-amputated and five transtibial amputated participants. EMG sensors were strategically positioned on the thigh muscles, while IMU sensors were placed on various leg segments. The performance of two classification algorithms, support vector machine (SVM) and long short-term memory (LSTM), were evaluated on segmented data. The results indicate that SVM models outperform LSTM models in accuracy, precision, and F1 score in the individual evaluation of amputee and non-amputee datasets for 80–20 and 50–50 data distributions. In the 80–20 distribution, an accuracy of 95.46% and 95.35% was obtained with SVM for non-amputees and amputees, respectively. An accuracy of 93.33% and 93.30% was obtained for non-amputees and amputees by using LSTM, respectively. LSTM models show more robustness and inter-population generalizability than SVM models when applying domain-adaptation techniques. Furthermore, the average classification latency for SVM and LSTM models was 19.84 ms and 37.07 ms, respectively, within acceptable limits for real-time applications. This study contributes to the field by comprehensively comparing SVM and LSTM classifiers for locomotion tasks, laying the foundation for the future development of real-time control systems for active transtibial prostheses. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Review of Parallel Robots: Rehabilitation, Assistance, and Humanoid Applications for Neck, Shoulder, Wrist, Hip, and Ankle Joints(Multidisciplinary Digital Publishing Institute (MDPI), 2023-10-01)This review article presents an in-depth examination of research and development in the fields of rehabilitation, assistive technologies, and humanoid robots. It focuses on parallel robots designed for human body joints with three degrees of freedom, specifically the neck, shoulder, wrist, hip, and ankle. A systematic search was conducted across multiple databases, including Scopus, Web of Science, PubMed, IEEE Xplore, ScienceDirect, the Directory of Open Access Journals, and the ASME Journal. This systematic review offers an updated overview of advancements in the field from 2012 to 2023. After applying exclusion criteria, 93 papers were selected for in-depth review. This cohort included 13 articles focusing on the neck joint, 19 on the shoulder joint, 22 on the wrist joint, 9 on the hip joint, and 30 on the ankle joint. The article discusses the timeline and advancements of parallel robots, covering technology readiness levels (TRLs), design, the number of degrees of freedom, kinematics structure, workspace assessment, functional capabilities, performance evaluation methods, and material selection for the development of parallel robotics. It also examines critical technological challenges and future prospects in rehabilitation, assistance, and humanoid robots. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Finger and wrist rehabilitation system based on soft robotics(Springer Science+Business Media, 2025-10-01)Soft robotics have been shown to offer benefits in physical hand rehabilitation therapies. However, most existing proposals focus solely on adults, neglecting the distinct biomechanical characteristics of children. Moreover, these proposals focus on systems designed for either finger or wrist rehabilitation, overlooking that effective hand rehabilitation therapies requires coordinated movements of fingers and the wrist. Accordingly, this study presents the design, simulation, and validation of a pediatric rehabilitation system that enables finger pulp pinch and wrist flexion–extension movements. The actuator design process considers the biomechanical characteristics of children. The actuators are constructed using a material rarely mentioned in the literature: RTV type 6 — Silika Moldes e Insumos. The mechanical tests are performed in accordance with the ASTM D412 standard to simulate it using finite element analysis in ANSYS software. The ANSYS fitting source is used to extract material coefficients from the fitted Yeoh model. Subsequently, the manufacturing of both actuators is described in detail, followed by testing using a proposed pneumatically controlled system. Our comprehensive experimental validation tests evaluate the actuators’ force output, kinematic hysteresis, fatigue, maximum breaking point, and safety factors, ensuring confidence in the system’s performance. Finally, a prototype of the finger and wrist rehabilitation system is presented.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of wrist and finger function in healthy children through music-based video game therapy(BioMed Central Ltd, 2025-12-01)BACKGROUND: The loss of hand and wrist function significantly impairs an individual's ability to perform everyday tasks, resulting in reduced independence and a lower quality of life. Neurological disorders, such as cerebral palsy, are among the leading causes of such impairments. These conditions often lead to difficulties with muscle strength, coordination, and motor control, impacting an individual's ability to manipulate objects. Cerebral palsy is a prevalent neurological disorder in children that often causes severe impairments in hand and wrist function. Traditional rehabilitation methods, such as physiotherapy, are effective but often suffer from poor adherence, especially in pediatric populations. Therefore, the use of engaging interventions, such as music-based and game-based therapies, holds significant promise for improving therapy adherence and effectiveness in children with cerebral palsy. METHODS: The proposed rehabilitation system integrates a wearable data glove with a music-based serious game to promote hand and wrist function in children with neurological impairments. The data glove, equipped with two inertial measurement unit sensors, detects hand and wrist movements, serving as the primary input device for the game. The game design incorporates music therapy elements, including metronome-based rhythms and volume feedback to motivate movement and enhance neuroplasticity. Three distinct games are designed to target wrist flexion and extension, ulnar and radial deviation, and gross motor grip. RESULTS: Ten healthy pediatric participants completed all sessions under both music and no-music conditions. As the data were non-normal, the Wilcoxon signed-rank test was used. Statistically significant differences were found in all games, although effect sizes were small. These results suggest that music may subtly modulate motor performance. For example, in the Rocket game, music reduced variability and range of motion, suggesting more controlled wrist flexion/extension. In the Squirrel and Bubble games, music contributed to smoother movements and greater consistency in pinch grip, respectively. Usability survey data revealed high levels of user satisfaction and enjoyment, with items related to clarity, comfort, engagement, and particularly relaxation showing significant differences above neutral ([Formula: see text]). CONCLUSIONS: This study provides exploratory evidence supporting the feasibility of music-based, game-driven rehabilitation tools in pediatric populations. Although the observed effects were modest, the system demonstrated high usability and acceptability. Future studies should include clinical populations, assess longer-term retention effects, and further investigate how music-induced relaxation may support engagement and treatment adherence in rehabilitation contexts.7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessment of artificial intelligence-based control algorithms to be implemented in an affordable transradial myoelectric prosthesis(Nature Portfolio, 2026-12-01)This study assesses artificial intelligence-based control algorithms for a transradial myoelectric prosthesis. The analysis is supported by a dataset collected from 20 Peruvian participants with transradial amputation, including congenital and traumatic cases. Each participant performed 240 gesture repetitions under varying postures with surface electromyographic (sEMG) signals recorded on the user's forearm. The dataset was processed to extract time and frequency domain features, enabling the implementation of classifiers such as Neural Networks (NN), Random Forest (RF), Extreme Gradient Boosting (XGB), and Decision Trees (DT). The results demonstrate that RF and XGB outperformed other classifiers when employed in a stack model architecture (97.4% accuracy). Distal amputations exhibited superior outcomes, as results from users of congenital amputations were also superior. Favorable results were also observed among individuals with a medium time since limb loss (26-51 years). Initial tests on a Raspberry Pi Zero 2 W system validated the feasibility of real-time implementation with reduced sliding window sizes. These findings highlight the potential of bespoke machine learning approaches to enhance gesture recognition accuracy, contributing to the development of affordable, personalized prosthetic solutions for individuals with transradial amputations.1