Search Results (9)

The analysis of the masticatory cycle plays a fundamental role in studying the functions of the stomatognathic system and evaluating temporomandibular dysfunctions (TMD). The primary objective of this study is to investigate the complex interplay between mandibular kinematics and surface electromyography (sEMG) activity during the masticatory cycle using advanced 4D dentistry technology in 22 healthy subjects (without TMD). By employing electromyography, it becomes feasible to capture the electrical activity of the masticatory muscles throughout the chewing process. The BTS TMJOINT (© 2023 BTS Bioengineering, Garbagnate Milanese, MI, Italy) electromyograph was utilized in this study. Mandibular tracking, on the other hand, allows for recording the movements of the mandible during chewing and condylar slopes. This latest technology (ModJaw^®, Tech in motion™, Villeurbanne, France) utilizes motion sensors placed on the jaw to accurately track three-dimensional movements, including jaw opening, closing, and lateral movements. Nowadays, in clinical gnathology, it is common practice to examine masticatory function by analyzing mandibular kinematics and muscle contraction as distinct entities. Similarly, the results obtained from these analyses are typically assessed independently. The investigation of a correlation between electromyography data and mandibular kinematics during the masticatory cycle could provide several advantages for clinicians in diagnosis and lead to a combined analysis of muscle activities and intraarticular dynamics. In conclusion, it can be inferred from the results obtained in the present study that the chewing cycle with a greater vertical movement results in increased masseter muscular activity, and condylar slopes are positively correlated to an increase in temporalis muscle activation. This comprehensive approach can provide valuable insights into the relationship between muscle activity and mandibular movement, enabling clinicians to gain a deeper understanding of the functional dynamics of the stomatognathic system. Full article

This single-blind randomized controlled trial investigated the effectiveness of surface electromyography (sEMG)-triggered constraint-induced movement cycling therapy (CIMCT) in improving balance, lower extremity strength, and activities of daily living in patients with chronic stroke. The participants included patients with chronic stroke-induced hemiplegia who had been diagnosed for more than 6 months, with a minimum score of 24 points on the Mini-Mental State Examination and above level 3 on the Brunnstrom stages. The trial lasted 4 weeks and participants were divided into a CIMCT group and a general cycling training (GCT) group. The CIMCT group (n = 20) used an sEMG-triggered constrained-induced movement therapy device, whereas the GCT group (n = 19) used a standard stationary bicycle. The primary outcome measures showed a significant increase in muscle strength on the affected side in the CIMCT group, as assessed by a manual muscle tester (p < 0.05), with a large effect size (d = 1.86), while no meaningful improvement was observed in the GCT group. Both groups demonstrated significant improvements in dynamic balance, as measured by the Timed Up and Go (TUG) test (p < 0.05), with the CIMCT group showing superior results compared to the GCT group, reflected by a large effect size (d = 0.96). Additionally, both groups showed significant improvements in balance as assessed by the Berg Balance Scale (BBS) and the Functional Reach Test (FRT). The CIMCT group exhibited more pronounced improvements than the GCT group, with large effect sizes of 0.83 for the BBS and 1.25 for the FRT. The secondary outcome measures revealed significant improvements in activities of daily living in both groups, as assessed by the modified Barthel index (MBI), with the CIMCT group achieving a substantial improvement (p < 0.05), accompanied by a large effect size (d = 0.87). This study concludes that sEMG-triggered CIMCT effectively improved muscle strength, postural balance, and activities of daily living in patients with chronic stroke. Full article

The continuous advancement of electronic technology has led to the gradual integration of automated intelligent devices into various aspects of human life. Motion gesture-based human–computer interaction systems offer abundant information, user-friendly functionalities, and visual cues. Surface electromyography (sEMG) signals enable the decoding of muscle movements, facilitating the realization of corresponding control functions. Considering the inherent instability and minuscule nature of sEMG signals, this thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling for the calculation of similarity between the sample and the model. This process facilitates gesture recognition and ensures effective communication between individuals and the 3D printed prosthesis. Utilizing this algorithm, the best feature model was generated by amalgamating six types of gesture classification model. A total of 600 training and evaluation experiments were performed, with each movement recognized 100 times. The experimental tests demonstrate that the accuracy of gesture recognition and prosthetic limb control using the temporal dynamic regularization algorithm achieves an impressive 93.75%, surpassing the performance of the traditional threshold control switch. Full article

Neuromusculoskeletal models often require three-dimensional (3D) body motions, ground reaction forces (GRF), and electromyography (EMG) as input data. Acquiring these data in real-world settings is challenging, with barriers such as the cost of instruments, setup time, and operator skills to correctly acquire and interpret data. This study investigated the consequences of limiting EMG and GRF data on modelled anterior cruciate ligament (ACL) forces during a drop–land–jump task in late-/post-pubertal females. We compared ACL forces generated by a reference model (i.e., EMG-informed neural mode combined with 3D GRF) to those generated by an EMG-informed with only vertical GRF, static optimisation with 3D GRF, and static optimisation with only vertical GRF. Results indicated ACL force magnitude during landing (when ACL injury typically occurs) was significantly overestimated if only vertical GRF were used for either EMG-informed or static optimisation neural modes. If 3D GRF were used in combination with static optimisation, ACL force was marginally overestimated compared to the reference model. None of the alternative models maintained rank order of ACL loading magnitudes generated by the reference model. Finally, we observed substantial variability across the study sample in response to limiting EMG and GRF data, indicating need for methods incorporating subject-specific measures of muscle activation patterns and external loading when modelling ACL loading during dynamic motor tasks. Full article

Background and Objectives: Ankle foot orthoses (AFOs) are commonly used by stroke patients to walk safely and efficiently. Both posterior AFOs (PAFOs) and anterior AFOs (AAFOs) are available. The objective of this study was to compare the efficacy of AAFOs and PAFOs in the treatment of ankle spasticity. Materials and Methods: A crossover design with randomization for the interventions and blinded assessors was used. Twenty patients with chronic stroke, a Modified Ashworth Scale (MAS) score of the ankle joint of 2, and a Tardieu angle ≥20 degrees were recruited. The patients were assigned to wear either an AAFO or PAFO at random and subsequently crossover to the other AFO. Results: Twenty stroke patients with ankle spasticity were recruited. The mean age was 46.60 (38–60) years. The mean time since stroke onset was 9.35 (6–15) months. It was discovered that the AAFO improved walking speed as well as the stretch reflex dynamic electromyography (dEMG) and walking dEMG amplitudes of the medial gastrocnemius muscles more significantly than the PAFO (p < 0.05). Conclusions: The AAFO had greater efficacy in reducing both static and dynamic ankle spasticity, and allowed for faster walking than the PAFO. The stretch reflex and walking dEMG amplitudes could be used for quantitative spasticity assessment. Full article

In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities. Full article

In this study, restorative environment theory and virtual reality (VR) technology were combined to build different 3D dynamic VR interactive scenes. We discuss the effects of a VR restorative environment on the emotional and cognitive recovery of individuals with mild-to-moderate anxiety and depression. First, we built a VR restorative garden scene, divided into four areas: forest, lawn, horticultural planting, and water features. The scene was verified to have a good recovery effect in 26 participants. Then, 195 participants with mild-to-moderate anxiety and depression were selected as experimental subjects. Through psychological testing and EMG (Electromyography) and EEG (Electroencephalography) data feedback, we further explored the differences in the sense of presence in VR restorative scenes and their effect on individual emotional and cognitive recovery. The results showed that (1) both the restorative environment images and the VR scenes had a healing effect (the reduction in negative emotions and the recovery of positive emotions and cognition), with no difference in the subjective feeling of recovery among the different scenes, but the recovery score of the VR urban environment was higher than that of the natural environment (differing from the results in real environments); (2) a high sense of presence can be experienced in different VR scenes, and interactive activities in VR scenes can provide a great presence experience; (3) the recovery effects of VR restorative environment on emotion and self-efficacy are realized through the presence of VR scenes; (4) a VR restorative environment is helpful for the emotional improvement and cognitive recovery of individuals with mild-to-moderate anxiety and depression. VR urban scenes also have good recovery effects. In terms of cognitive recovery, self-efficacy improved significantly. In addition, from the perspective of EEG indicators, the VR restorative scene experience activated the prefrontal lobe, which is conducive to cognitive recovery in individuals with mild-to-moderate anxiety and depression. In terms of emotional improvement, negative emotions were significantly reduced in the different VR scene groups. In conclusion, we further explored ways to help individuals with mild-to-moderate anxiety and depression, in order to promote the development and application of mental health. Full article

There is a lack of evidence about the ways in which balance ability influences the kinematic and kinetic parameters and muscle activities during gait among healthy individuals. The hypothesis is that balance ability would be associated with the lower limb kinematics, kinetics and muscle activities during gait. Twenty-nine healthy volunteers (Age 32.8 ± 9.1; 18 males and 11 females) performed a Star Excursion Balance test to measure their dynamic balance and walked for at least three trials in order to obtain a good quality of data. A Vicon^® 3D motion capture system and AMTI^® force plates were used for the collection of the movement data. The selected muscle activities were recorded using Delsys^® Electromyography (EMG). The EMG activities were compared using the maximum values and root mean squared (RMS) values within the participants. The joint angle, moment, force and power were calculated using a Vicon Plug-in-Gait model. Descriptive analysis, correlation analysis and multivariate linear regression analysis were performed using SPSS version 23. In the muscle activities, positive linear correlations were found between the walking and balance test in all muscles, e.g., in the multifidus (RMS) (r = 0.800 p < 0.0001), vastus lateralis (RMS) (r = 0.639, p < 0.0001) and tibialis anterior (RMS) (r = 0.539, p < 0.0001). The regression analysis models showed that there was a strong association between balance ability (i.e., reaching distance) and the lower limb muscle activities (i.e., vastus medialis–RMS) (R = 0.885, p < 0.0001), and also between balance ability (i.e., reaching distance) and the lower limb kinematics and kinetics during gait (R = 0.906, p < 0.0001). In conclusion, the results showed that vastus medialis (RMS) muscle activity mainly contributes to balance ability, and that balance ability influences the lower limb kinetics and kinematics during gait. Full article

High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning how the instantaneous image evolves with time. This paper presents a 3D CNN with 3D kernels to capture both spatial and temporal structures from sequential sEMG images and investigates its performance on HD-sEMG-based gesture recognition in comparison to the 2D CNN. Extensive experiments were carried out on two benchmark datasets (i.e., CapgMyo DB-a and CSL-HDEMG). The results show that, where the same network architecture is used, 3D CNN can achieve a better performance than 2D CNN, especially for CSL-HDEMG, which contains the dynamic part of finger movement. For CapgMyo DB-a, the accuracy of 3D CNN was 1% higher than 2D CNN when the recognition window length was equal to 40 ms, and was 1.5% higher when equal to 150 ms. For CSL-HDEMG, the accuracies of 3D CNN were 15.3% and 18.6% higher than 2D CNN when the window length was equal to 40 ms and 150 ms, respectively. Furthermore, 3D CNN achieves a competitive performance in comparison to the baseline methods. Full article