The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Simon L Kappel - One of the best experts on this subject based on the ideXlab platform.
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real time hand gesture recognition using Temporal Muscle activation maps of multi channel semg signals
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Ashwin De Silva, Malsha V Perera, Kithmin Wickramasinghe, Asma M Naim, Thilina Dulantha Lalitharatne, Simon L KappelAbstract:Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of Muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.
Ashwin De Silva - One of the best experts on this subject based on the ideXlab platform.
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real time hand gesture recognition using Temporal Muscle activation maps of multi channel semg signals
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Ashwin De Silva, Malsha V Perera, Kithmin Wickramasinghe, Asma M Naim, Thilina Dulantha Lalitharatne, Simon L KappelAbstract:Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of Muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.
Bastien Berret - One of the best experts on this subject based on the ideXlab platform.
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Deciphering the functional role of spatial and Temporal Muscle synergies in whole-body movements
Scientific Reports, 2018Co-Authors: Ioannis Delis, Pauline M. Hilt, Thierry Pozzo, Stefano Panzeri, Bastien BerretAbstract:Voluntary movement is hypothesized to rely on a limited number of Muscle synergies, the recruitment of which translates task goals into effective Muscle activity. In this study, we investigated how to analytically characterize the functional role of different types of Muscle synergies in task performance. To this end, we recorded a comprehensive dataset of Muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task features. We found that the Temporal and spatial aspects of the movements were encoded by different Temporal and spatial Muscle synergies, respectively, consistent with the intuition that there should a correspondence between major attributes of movement and major features of synergies. This approach led to the development of a novel computational method for comparing Muscle synergies from different participants according to their functional role. This functional similarity analysis yielded a small set of Temporal and spatial synergies that describes the main features of whole-body reaching movements.
Hui Chen - One of the best experts on this subject based on the ideXlab platform.
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comparison of manually shaped and computer shaped titanium mesh for repairing large frontotemporoparietal skull defects after traumatic brain injury
Neurosurgical Focus, 2012Co-Authors: Jianming Luo, Bin Liu, Zeyu Xie, Shan Ding, Zerui Zhuang, Lan Lin, Yanchun Guo, Hui ChenAbstract:Object The object of this study was to compare the effects and complications of manual and computer-aided shaping of titanium meshes for repairing large frontotemporoparietal skull defects following traumatic brain injury. Methods From March 2005 to June 2011, 161 patients with frontotemporoparietal skull defects were observed. Patients were divided into 2 groups according to the repair materials used for cranioplasty: 83 cases used computer-aided shaping for the titanium mesh, whereas the remaining 78 cases used a manually shaped titanium mesh. The advantages and disadvantages of the 2 methods were compared. Results No case of titanium mesh loosening occurred in either group. Subcutaneous fluid collection, titanium mesh tilt, and Temporal Muscle pain were the most common complications. In the manually shaped group, there were 14 cases of effusion, 10 cases of titanium mesh tilt, and 15 cases of Temporal Muscle pain. In the computer-aided group, there were 6 cases of effusion, 3 cases of titanium mesh til...
Niels Hammer - One of the best experts on this subject based on the ideXlab platform.
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biomechanical characterization of human Temporal Muscle fascia in uniaxial tensile tests for graft purposes in duraplasty
Scientific Reports, 2021Co-Authors: Johann Zwirner, Benjamin Ondruschka, Mario Scholze, Gundula Schulzetanzil, Niels HammerAbstract:The human Temporal Muscle fascia (TMF) is used frequently as a graft material for duraplasty. Encompassing biomechanical analyses of TMF are lacking, impeding a well-grounded biomechanical comparison of the TMF to other graft materials used for duraplasty, including the dura mater itself. In this study, we investigated the biomechanical properties of 74 human TMF samples in comparison to an age-matched group of dura mater samples. The TMF showed an elastic modulus of 36 ± 19 MPa, an ultimate tensile strength of 3.6 ± 1.7 MPa, a maximum force of 16 ± 8 N, a maximum strain of 13 ± 4% and a strain at failure of 17 ± 6%. Post-mortem interval correlated weakly with elastic modulus (r = 0.255, p = 0.048) and the strain at failure (r = - 0.306, p = 0.022) for TMF. The age of the donors did not reveal significant correlations to the TMF mechanical parameters. Compared to the dura mater, the here investigated TMF showed a significantly lower elastic modulus and ultimate tensile strength, but a larger strain at failure. The human TMF with a post-mortem interval of up to 146 h may be considered a mechanically suitable graft material for duraplasty when stored at a temperature of 4 °C.
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mechanical properties of native and acellular Temporal Muscle fascia for surgical reconstruction and computational modelling purposes
Journal of The Mechanical Behavior of Biomedical Materials, 2020Co-Authors: Johann Zwirner, Benjamin Ondruschka, Mario Scholze, Gundula Schulzetanzil, Niels HammerAbstract:Abstract The Temporal Muscle fascia (TMF) is a widely used graft material and of interest for computational simulations of the temporomandibular joint as well as computational and physical human head models in general. However, reliable biomechanical properties of the TMF are lacking to date. This study provides tensile data of 52 TMFs at an age range of 18 to 94 years. It further investigates, if acellular fascia scaffolds differ from native counterparts in their biomechanical behaviour. Native TMF has a median elastic modulus of 26.2 MPa (acellular: 24.5 MPa), an ultimate tensile strength of 2.9 MPa (acellular: 2.1 MPa), a maximum force of 12.6 N (acellular: 9.9 N) and a strain at failure of 14.1% (acellular: 14.8%). No significant difference was found regarding the properties of native and acellular samples. Elastic modulus and the ultimate tensile strength increased with age but only in the acellular group (p
