417 research outputs found
sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network
Analysis of sEMG on biceps brachii and brachioradialis in static conditions: Effect of joint angle and contraction level
Despite several previous investigations, the direct correlation between the elbow joint angle and the activities of related muscles is still an unresolved topic. The sEMG signals were recorded from biceps brachii (6x8 electrodes, 10mm IED, d=3mm) and brachioradialis (1x8 electrodes, 5mm IED, d=3mm) of ten subjects. The subjects were asked to perform isometric elbow flexion at five joint angles with four contraction levels with respect to the maximum contraction (MVC) at that joint angle. The RMS values of biceps brachii (BB) and brachioradialis (BR) are computed within 500ms epoch and averaged over the muscle’s active region. These values increase along as force increases regardless the joint angle. Concerning the different joint angle, we found that as the arm extended, the RMS values of seven subjects decreased, while the RMS values of three subjects increased. This behavior suggests different strategies of muscle contribution to the task in different subjects but may also be attributed to the technical issues discussed in Chapter 2 - 7.
Prior to this investigation, several issues related to the sEMG signals recording and processing were evaluated. Analysis on the effect of different elbow joint angle on the position of the innervations zone (IZ) of biceps brachii muscle indicates that the IZ shifts distally 24±9mm as the subjects extend their arms. Thus to assure sEMG signal recording, a grid of electrodes is selected instead of bipolar electrodes.
The issue of spatial aliasing, which has not been addressed before, was studied. Greater electrode’s diameter implies higher spatial low pass filtering effect which gives an advantage as anti-aliasing filter in space. On the other hand, this low pass filtering effect increase the error on the power for the single sEMG image (d=10mm, 10mm IED) to 3±13.5% compared to the continuous image. Larger IED introduces RMS estimation error up to ±18% for the single sEMG image (15mm IED). However, taking the mean of a group of maps, the error of the mean is negligible (<3%).
Furthermore, the envelope of the rectified EMG has been investigated. Five digital low pass filters (Butterworth, Chebyshev, Inverse Chebyshev, and Elliptic) with five different orders, four cut off frequencies and one or bi-directional filtering were tested using simulated sEMG interference signals. The results show that different filters are optimal for different applications. Power line interference is one of the sources of impurity of the sEMG signals. Notch filter, spectral interpolation, adaptive filter, and adaptive noise canceller with phase locked loop were compared. Another factor that affects the amplitude of sEMG is the subcutaneous layer thickness (ST). Higher contraction level and greater elbow joint angle lead to thinner ST. RMS values tend to decrease for thicker ST at a rate of 1.62 decade/decade
A Study of Myoelectric Signal Processing
This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based methods are superior to conventional filter-based methods in the absence of decomposition error, but they unacceptably degrade in the presence of even the smallest decomposition errors present in real EMG data, which is typically around 3-5%. Optimal parameters for each method are found, and with normal decomposition error rates, ranks of these methods with their optimal parameters are given. Overall, Hanning filtering and Berger methods exhibit consistent and significant advantages over other methods. In the second investigator-led project, the technique of signal whitening was applied prior to motion classification of upper limb surface EMG signals previously collected from the forearm muscles of intact and amputee subjects. The motions classified consisted of 11 hand and wrist actions pertaining to prosthesis control. Theoretical models and experimental data showed that whitening increased EMG signal bandwidth by 65-75% and the coefficients of variation of temporal features computed from the EMG were reduced. As a result, a consistent classification accuracy improvement of 3-5% was observed for all subjects at small analysis durations (\u3c 100 ms). In the first team-based project, advanced modeling methods of the constant posture EMG-torque relationship about the elbow were studied: whitened and multi-channel EMG signals, training set duration, regularized model parameter estimation and nonlinear models. Combined, these methods reduced error to less than a quarter of standard techniques. In the second team-based project, a study related biceps-triceps surface EMG to elbow torque at seven joint angles during constant-posture contractions. Models accounting for co-contraction estimated that individual flexion muscle torques were much higher than models that did not account for co-contraction
A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton
A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion&-extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm&-exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient's intention.The research was funded by RoboHealth (DPI2013-47944-C4-3-R) and the EDAM (DPI2016-75346-R) Spanish research projects
A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue
Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results
Blind Source Separation Based Classification Scheme for Myoelectric Prosthesis Hand
For over three decades, researchers have been working on using surface electromyography (sEMG) as a means for amputees to use remaining muscles to control prosthetic limbs (Baker, Scheme, Englehart, Hutcinson, & Greger, 2010; Hamdi, Dweiri, Al-Abdallat, & Haneya, 2010; Kiguchi, Tanaka, & Fukuda, 2004). Most research in this domain has focused on using the muscles of the upper arms and shoulders to control the gross orientation and grasp of a low-degree-of-freedom prosthetic device for manipulating objects (Jacobsen & Jerard, 1974). Each measured upper arm muscle is typically mapped directly to one degree of freedom of the prosthetic. For example, tricep contraction could be used for rotation while bicep flexion might close or open the prosthetic. More recently, researchers have begun to look at the potential of using the forearm muscles in hand amputees to control a multi-fingered prosthetic hand. While we know of no fully functional hand prosthetic, this is clearly a promising new area of EMG research. One of the challenges for creating hand prosthetics is that there is not a trivial mapping of individual muscles to finger movements. Instead, many of the same muscles are used for several different fingers (Schieber, 1995)
High performance wearable ultrasound as a human-machine interface for wrist and hand kinematic tracking
Objective: Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding. Methods: US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task. Results: In the offline analysis, the wearable US system achieved an average R2 of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of R2=0.06 . In online control, the participants achieved an average 93% completion rate of the targets. Conclusion: When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces. Significance: Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings
A denoising algorithm for surface EMG decomposition
The goal of the present thesis was to investigate a novel motor unit potential train (MUPT) editing routine, based on decreasing the variability in shape (variance ratio, VR) of the MUP ensemble. Decomposed sEMG data from 20 participants at 60% MVC of wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the positive and negative peaks for the MUP template were dependent on the level of denoising (p’s 0.05). The same was true between denoising criteria (p>0.05). Editing the MUPT based on MUP shape resulted in significant differences in measures extracted from the MUP template, with trivial difference between the standard error of estimate for mean IDIs between the complete and denoised MUPTs
Subtle hand gesture identification for HCI using temporal decorrelation source separation BSS of surface EMG
Hand gesture identification has various human computer interaction (HCI) applications. This paper presents a method for subtle hand gesture identification from sEMG of the forearm by decomposing the signal into components originating from different muscles. The processing requires the decomposition of the surface EMG by temporal decorrelation source separation (TDSEP) based blind source separation technique. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other HCI based devices. The proposed model based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an a priori mixing matrix based on known hand muscle anatomy. The paper reports experimental results, where the system was able to reliably recognize different subtle hand gesture with an overall accuracy of 97%. The advantage of such a system is that it is easy to train by a lay user, and can easily be implemented in real time after the initial training. The paper also highlights the importance of mixing matrix analysis in BSS technique
Madala maksumusega elektromüograafide rakendatavus ergonoomikalises hindamises
A thesis
for applying for the degree of Doctor of Philosophy
in Engineering Sciences.Every year a considerable amount of gross domestic product in several countries is lost due to work-related musculoskeletal disorders (WMSDs). Thus, one of the goals of ergonomics is to prevent WMSDs. A body of knowledge required to prevent WMSDs has existed for decades; however, the exploitation of this knowledge is hindered by the shortcomings in the risk assessment methods. As a rule, objective methods should be preferred to subjective methods, though often access to objective methods is restricted by the cost of the apparatus. The potential to make one of such devices more accessible by reducing the costs was investigated in the thesis. The thesis focused on the electromyograph – a device to study and monitor the electrical activity produced by skeletal muscles. Nowadays one can assemble an electromyograph from low-cost semi-universal components; however, the functionality and usability of such a device is unknown. At first the technical characteristics of components that can be used to assemble an electromyograph were evaluated. Then the electromyographs were assembled and tested in the laboratory and in the field. The results showed that the low-cost electromyographs may be partially utilised in ergonomic risk assessment; however, the use of such equipment in comparison to commercial high-cost apparatus increases the demands on user knowledge, skills and time expenditure. On the other hand, the functionality of the do-it-yourself electromyograph may exceed the commercial device.Tööga seotud luu- ja lihaskonna ülekoormushaiguste tõttu kaotavad riigid igal aastal märkimisväärse osa sisemajanduse kogutoodangust. Seetõttu on üheks ergonoomika eesmärgiks luu- ja lihaskonna ülekoormushaiguste ennetamine. Teadmised töötaja ülekoormuse ennetamiseks on olemas juba aastakümneid. Paraku takistavad teadmiste tõhusat rakendamist puudused riskihindamise meetodites. Riskide hindamisel tuleb subjektiivsetele meetoditele eelistada objektiivseid meetodeid, kuid sageli piirab objektiivsete meetodite kasutamist mõõteseadmete maksumus. Doktoritöös uuriti ühe sellist liiki mõõteseadme, lihaste elektrilise aktiivsuse uurimiseks mõeldud seireseadme ehk elektrimüograafi kättesaadavuse ja rakendamise suurendamise võimalust seadme maksumuse vähendamisega. Nüüdisajal on võimalus elektromüograafe kokku panna madala maksumusega ja pool-universaalsetest komponentidest. Samas pole selge, milline on sellisel viisil valmistatud elektromüograafi funktsionaalsus ja kasutatavus. Doktoritöös hinnati esmalt elektromüograafi madala maksumusega komponentide tehnilisi omadusi ning seejärel katsetati koostatud elektromüograafe laboris ja töökeskkonnas. Doktoritöö andis kinnitust, et madala maksumusega elektromüograafe on võimalik riskihindamisel osaliselt rakendada, kuid selliste seadmete kasutamine eeldab riskihindajalt põhjalikumaid teadmisi ja oskusi ning suuremat ajakulu kui kallite kommertsseadmete kasutamine. Samas võib spetsialisti kokkupandud elektromüograafi funktsionaalsus kommertsseadmeid ületada.Publication of this thesis is supported by the Estonian University of
Life Sciences. This research was supported by European Regional
Development Fund’s Doctoral Studies and Internationalisation
Programme DoR
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