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  • 1.
    Abbaspour, Sara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Losing a limb causes difficulties in our daily life. To regain the ability to live an independent life, artificial limbs have been developed. Hand prostheses belong to a group of artificial limbs that can be controlled by the user through the activity of the remnant muscles above the amputation. Electromyogram (EMG) is one of the sources that can be used for control methods for hand prostheses. Surface EMGs are powerful, non-invasive tools that provide information about neuromuscular activity of the subjected muscle, which has been essential to its use as a source of control for prosthetic limbs. However, the complexity of this signal introduces a big challenge to its applications. EMG pattern recognition to decode different limb movements is an important advancement regarding the control of powered prostheses. It has the potential to enable the control of powered prostheses using the generated EMG by muscular contractions as an input. However, its use has yet to be transitioned into wide clinical use. Different algorithms have been developed in state of the art to decode different movements; however, the challenge still lies in different stages of a successful hand gesture recognition and improvements in these areas could potentially increase the functionality of powered prostheses. This thesis firstly focuses on improving the EMG signal’s quality by proposing novel and advanced filtering techniques. Four efficient approaches (adaptive neuro-fuzzy inference system-wavelet, artificial neural network-wavelet, adaptive subtraction and automated independent component analysis-wavelet) are proposed to improve the filtering process of surface EMG signals and effectively eliminate ECG interferences. Then, the offline performance of different EMG-based recognition algorithms for classifying different hand movements are evaluated with the aim of obtaining new myoelectric control configurations that improves the recognition stage. Afterwards, to gain proper insight on the implementation of myoelectric pattern recognition, a wide range of myoelectric pattern recognition algorithms are investigated in real time. The experimental result on 15 healthy volunteers suggests that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) outperform other classifiers. The real-time investigation illustrates that in addition to the LDA and MLE, multilayer perceptron also outperforms the other algorithms when compared using classification accuracy and completion rate.

  • 2.
    Abbaspour, Sara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Evaluation of surface EMG-based recognition algorithms for decoding hand movementsManuscript (preprint) (Other academic)
  • 3.
    Abbaspour, Sara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Proposing Combined Approaches to Remove ECG Artifacts from Surface EMG Signals2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Electromyography (EMG) is a tool routinely used for a variety of applications in a very large breadth of disciplines. However, this signal is inevitably contaminated by various artifacts originated from different sources. Electrical activity of heart muscles, electrocardiogram (ECG), is one of sources which affects the EMG signals due to the proximity of the collection sites to the heart and makes its analysis non-reliable. Different methods have been proposed to remove ECG artifacts from surface EMG signals; however, in spite of numerous attempts to eliminate or reduce this artifact, the problem of accurate and effective de-noising of EMG still remains a challenge. In this study common methods such as high pass filter (HPF), gating method, spike clipping, hybrid technique, template subtraction, independent component analysis (ICA), wavelet transform, wavelet-ICA, artificial neural network (ANN), and adaptive noise canceller (ANC) and adaptive neuro-fuzzy inference system (ANFIS) are used to remove ECG artifacts from surface EMG signals and their accuracy and effectiveness is investigated. HPF, gating method and spike clipping are fast; however they remove useful information from EMG signals. Hybrid technique and ANC are time consuming. Template subtraction requires predetermined QRS pattern. Using wavelet transform some artifacts remain in the original signal and part of the desired signal is removed. ICA requires multi-channel signals. Wavelet-ICA approach does not require multi-channel signals; however, it is user-dependent. ANN and ANFIS have good performance, but it is possible to improve their results by combining them with other techniques. For some applications of EMG signals such as rehabilitation, motion control and motion prediction, the quality of EMG signals is very important. Furthermore, the artifact removal methods need to be online and automatic. Hence, efficient methods such as ANN-wavelet, adaptive subtraction and automated wavelet-ICA are proposed to effectively eliminate ECG artifacts from surface EMG signals. To compare the results of the investigated methods and the proposed methods in this study, clean EMG signals from biceps and deltoid muscles and ECG artifacts from pectoralis major muscle are recorded from five healthy subjects to create 10 channels of contaminated EMG signals by adding the recorded ECG artifacts to the clean EMG signals. The artifact removal methods are also applied to the 10 channels of real contaminated EMG signals from pectoralis major muscle of the left side. Evaluation criteria such as signal to noise ratio, relative error, correlation coefficient, elapsed time and power spectrum density are used to evaluate the performance of the proposed methods. It is found that the performance of the proposed ANN-wavelet method is superior to the other methods with a signal to noise ratio, relative error and correlation coefficient of 15.53, 0.01 and 0.98 respectively.

  • 4.
    Abbaspour, Sara
    et al.
    Amirkabir University of technology,Tehran, Iran.
    Fallah, Ali
    Amirkabir University of technology,Tehran, Iran.
    Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique2014In: Biomedical Physics and Engineering, ISSN 2251-7200, Vol. 4, no 1, p. 33-38Article in journal (Refereed)
    Abstract [en]

    Background: The electrocardiogram artifact is a major contamination in the electromyogram signals when electromyogram signal is recorded from upper trunk muscles and because of that the contaminated electromyogram is not useful. Objective: Removing electrocardiogram contamination from electromyogram signals. Methods: In this paper, the clean electromyogram signal, electrocardiogram artifact and electrocardiogram signal were recorded from leg muscles, the pectoralis major muscle of the left side and V4, respectively. After the pre-processing, contaminated electromyogram signal is simulated with a combination of clean electromyogram and electrocardiogram artifact. Then, contaminated electromyogram is cleaned using adaptive subtraction method. This method contains some steps; (1) QRS detection, (2) formation of electrocardiogram template by averaging the electrocardiogram complexes, (3) using low pass filter to remove undesirable artifacts, (4) subtraction. Results: Performance of our method is evaluated using qualitative criteria, power spectrum density and coherence and quantitative criteria signal to noise ratio, relative error and cross correlation. The result of signal to noise ratio, relative error and cross correlation is equal to 10.493, 0.04 and %97 respectively. Finally, there is a comparison between proposed method and some existing methods. Conclusion: The result indicates that adaptive subtraction method is somewhat effective to remove electrocardiogram artifact from contaminated electromyogram signal and has an acceptable result.

  • 5.
    Abbaspour, Sara
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gholamhosseini, Hamid
    Auckland University of Technology, New Zealand.
    ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA2015In: Studies in Health Technology and Informatics, Volume 211, 2015, p. 91-97Conference paper (Refereed)
    Abstract [en]

    This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.

  • 6.
    Afshar, Sara Zargari
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Real-time and offline evaluation of myoelectric pattern recognition for upper limb prosthesis controlManuscript (preprint) (Other academic)
  • 7.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlström, Christer
    MFT, Linköping Sweden.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Automated EEG Artifact Handling with Application in Driver Monitoring2017In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 5, p. 1350-1361Article in journal (Refereed)
    Abstract [en]

    Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

  • 8.
    Du, Jiaying
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. -.
    Real-time signal processing in MEMS sensor-based motion analysis systems2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This PhD thesis focuses on real-time signal processing for hardware-limited micro-electro-mechanical system (MEMS) sensor-based human motion analysis systems. The aim of the thesis is to improve the signal quality of MEMS gyroscopes and accelerometers by minimizing the effects of signal errors, considering the hardware limitations and the users' perception.

    MEMS sensors such as MEMS gyroscopes and MEMS accelerometers are important components in motion analysis systems. They are known for their small size, light weight, low power consumption, low cost, and high sensitivity. This makes them suitable for wearable systems for measuring body movements. The data can further be used as input for advanced human motion analyses. However, MEMS sensors are usually sensitive to environmental disturbances such as shock, vibration, and temperature change. A large portion of the MEMS sensor signals actually originate from error sources such as noise, offset, null drift and temperature drift, as well as integration drift. Signal processing is regarded as the major key solution to reduce these errors. For real-time signal processing, the algorithms need to be executed within a certain specified time limit. Two crucial factors have to be considered when designing real-time signal processing algorithms for wearable embedded sensor systems. One is the hardware limitations leading to a limited calculation capacity, and the other is the user perception of the delay caused by the signal processing.

    Within this thesis, a systematic review of different signal error reduction algorithms for MEMS gyroscope-based motion analysis systems for human motion analysis is presented. The users’ perceptions of the delay when using different computer input devices were investigated. 50 ms was found as an acceptable delay for the signal processing execution in a real-time motion analysis system. Real-time algorithms for noise reduction, offset/drift estimation and reduction, improvement of position accuracy and system stability considering the above mentioned requirements, are presented in this thesis. The algorithms include a simplified high-pass filter and low-pass filter, a LMS algorithm, a Kalman filter, a WFLC algorithm, two simple novel algorithms (a TWD method and a velocity drift estimation method), and a novel combination method KWT.  Kalman filtering was found to be efficient to reduce the problem of temperature drift and the WFLC algorithm was found the most suitable method to reduce human physiological tremor and electrical noise. The TWD method resulted in a signal level around zero without interrupting the continuous movement signal. The combination method improved the static stability and the position accuracy considerably.  The computational time for the execution of the algorithms were all perceived as acceptable by users and kept within the specified time limit for real-time performance.  Implementations and experiments showed that these algorithms are feasible for establishing high signal quality and good system performance in previously developed systems, and also have the potential to be used in similar systems.

  • 9.
    Ericsson, Kenneth
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Grann, Robert
    Mälardalen University, School of Innovation, Design and Engineering.
    Image optimization algorithms on an FPGA2009Student thesis
    Abstract [en]

     

    In this thesis a method to compensate camera distortion is developed for an FPGA platform as part of a complete vision system. Several methods and models is presented and described to give a good introduction to the complexity of the problems that is overcome with the developed method. The solution to the core problem is shown to have a good precision on a sub-pixel level.

     

  • 10.
    Forsberg, Axel
    Mälardalen University, School of Innovation, Design and Engineering.
    A Wavelet-Based Surface Electromyogram Feature Extraction for Hand Gesture Recognition2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The research field of robotic prosthetic hands have expanded immensely in the last couple of decades and prostheses are in more commercial use than ever. Classification of hand gestures using sensory data from electromyographic signals in the forearm are primary for any advanced prosthetic hand. Improving classification accuracy could lead to more user friendly and more naturally controlled prostheses. In this thesis, features were extracted from wavelet transform coefficients of four channel electromyographic data and used for classifying ten different hand gestures. Extensive search for suitable combinations of wavelet transform, feature extraction, feature reduction, and classifier was performed and an in-depth comparison between classification results of selected groups of combinations was conducted. Classification results of combinations were carefully evaluated with extensive statistical analysis. It was shown in this study that logarithmic features outperforms non-logarithmic features in terms of classification accuracy. Then a subset of all combinations containing only suitable combinations based on the statistical analysis is presented and the novelty of these results can direct future work for hand gesture recognition in a promising direction.

  • 11.
    Holmström, Johnny
    Mälardalen University, School of Innovation, Design and Engineering.
    GOVERNOR ELECTRONICS FOR DIESEL ENGINES: High availability platform for real-time control and advanced fuel efficiency algorithms2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Fossil fuel is a rare commodity and the combustion of this fuel results in negative environmental effects. This paper evaluates and validates the electronics needed to run intelligent algorithms to lower the fuel consumption for commercial vessels. This is done by integrating advanced fuel saving functions into an electronic device that controls the fuel injection of large diesel engines, as known as a diesel engine governor. The control system is classified as a safety critical system. This means that the electronics needs to be designed for fail safe operation. To allow for future research and development, the platform needs flexibility in respect to hardware reconfiguration and software changes, i.e. this is the basis for a system that allows for hardware-software co-design. For efficient installation and easy commissioning, the system shall allow for auto-calibration combined with programmable jumper selections to attain a cost effective solution. The computation of the fuel saving algorithm require accurate data to build a model of the vessels motions. This is achieved by integrating state of the art sensors and a multitude of communication interfaces. Among other things gyroscopes contra accelerometers where evaluated to find the best solution in respect to cost and performance. This design replace the current product DEGO III. The new product requires the same functionality and shall allow for more functions. Focus has been spent on communication, methods of accruing sensor data and more computation speed. In creating a new generation of a product there are tasks like selecting components, questions pertaining to layout of the printed circuit board and an evaluation of supply chains. The manufacturing aspects are considered to rationalize production and testing.

  • 12.
    Lindh, Fredrik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Wennerström, Jessica
    Mälardalen University, School of Innovation, Design and Engineering.
    Otnes, Thomas
    Mälardalen University, School of Innovation, Design and Engineering.
    Electronic Design Optimization of Vibration Monitor Instrument2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Vibrations in machines increase friction on moving parts which cause chafing that will tear down the fabric of the machine components when given time, thus monitoring and analysis of machine vibrations are important for preventive maintenance. Vibration analysis utilizes time domain as well as frequency domain analysis for which there have been analog solutions for quite some time. This work has been about moving a predominantly analog mixed signal system onto an FPGA and making it mostly digital. Vibration analysis on an FPGA have its own challenges and benefits compared to other methods. The inherent parallelism of the FPGA makes it suitable for high performance signal analysis. This report shows through two proof-of-concept solutions that the translation of a predominantly analog system is viable, economic and can deliver improved performance. The two solutions have utilized two different units from Xilinx, the Spartan-6 FPGA and the Zynq-7000 system on chip FPGA. The solution implemented on Spartan-6 produces a result in 9.32 ms and the other implementation based on Zynq-7000 produces a result in 9.39 ms, which is more than a 10-fold increase in performance of the current system. The results obtained show that both solutions can perform the calculations for the proof of concept within 20% of the allotted time. Costs of both solutions as well as other qualities of each solution are presented in this paper.

  • 13.
    Martinsson, Jonas
    Mälardalen University, School of Innovation, Design and Engineering.
    Examine vision technology for small object recognition in an industrial robotics application2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis explains the development of a computer vision system able to find and orient relatively small objects. The motivations is exchanging a monotonous work done by hand and replace it with an automation system with help of an ABB IRB 140 industrial robot. The vision system runs on a standard PC and is developed using the OpenCV environment, originally made by Intel in Russia. The algorithms of the system is written in C++ and the user interface in C++/CLI. With a derived test case, multiple vision algorithms is tested and evaluated for this kind of application. The result shows that SIFT/SURF works poorly with multiple instances of the search object and HAAR classifiers produces many false positives. Template matching with image moment calculation gave a satisfying result regarding multiple object in the scene and produces no false positives. Drawbacks of the selected algorithm developed where sensibility to light invariance and lack of performance in a skewed scene. The report also contains suggestions on how to precede with further improvements or research.

  • 14.
    Persson, Anders
    Mälardalen University, School of Innovation, Design and Engineering.
    Platform development of body area network for gait symmetry analysis using IMU and UWB technology2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Having a device with the capability of measure motions from gait produced by a human being, could be of most importance in medicine and sports. Physicians or researchers could measure and analyse key features of a person's gait for the purpose of rehabilitation or science, regarding neurological disabilities. Also in sports, professionals and hobbyists could use such a device for improving their technique or prevent injuries when performing. In this master thesis, I present the research of what technology is capable of today, regarding gait analysis devices. The research that was done has then help the development of a suggested standalone hardware sensor node for a Body Area Network, that can support research in gait analysis. Furthermore, several algorithms like for instance UWB Real-Time Location and Dead Reckoning IMU/AHRS algorithms, have been implemented and tested for the purpose of measuring motions and be able to run on the sensor node device. The work in this thesis shows that a IMU sensor have great potentials for generating high rate motion data while performing on a small mobile device. The UWB technology on the other hand, indicates a disappointment in performance regarding the intended application but can still be useful for wireless communication between sensor nodes. The report also points out the importance of using a high performance micro controller for achieving high accuracy in measurements.

  • 15.
    Tomasic, Ivan
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Petrovic, Nikola
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Rashkovska, A.
    Jožef Stefan Institute, Department of Communication Systems, Ljubljana, Slovenia.
    Comparison of publicly available beat detection algorithms performances on the ECGs obtained by a patch ECG device2019In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 275-278Conference paper (Refereed)
    Abstract [en]

    Eight ECG beat detection algorithms, from the PhysioNet's WFDB and Cardiovascular Signal toolboxes, were tested on twenty measurements, obtained by the Savvy patch ECG device, for their accuracy in beat detection. On each subject, one measurement is obtained while sitting and one while running. Each measurement lasted from thirty seconds to one minute. The measurements obtained while running were more challenging for all the algorithms, as most of them almost perfectly detected all the beats on the measurements obtained in sitting position. However, when applied on the measurements obtained while running, all the algorithms have performed with decreased accuracy. Considering overall percentage of the faulty detected peaks, the four best algorithms were jqrs, from the Cardiovascular Signal Toolbox, and ecgpuwave, gqrs, and wqrs, from the WFDB Toolbox, with percentages of faulty detected beats 1.7, 2.3, 2.9, and 3, respectively. 

  • 16.
    Östberg, Micael
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Norgren, Mikael
    Mälardalen University, School of Innovation, Design and Engineering.
    Intelligent Gripper2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The human hand is a great generic gripper as it can grasp objects of unknown shapes, weights and surfaces. Most robotic grippers in today's industry have to be custom made and tuned for each application by engineers, thus many man hours are required to get the desired behavior and repeatability. To be able to adapt some of the capabilities of the human hand into robust industrial robotic grippers would enhance their usability and ease the tuning by engineers once installed.

    This thesis discusses the development of a robust intelligent gripper for industrial use, based on piezo sensors which have the ability to both sense slippage and detect objects. First, an experimental sensor prototype was developed successfully using an amplification circuit and algorithms implemented in LabView. Secondly, a final prototype containing a signal board, an FPGA board, a simple gripper with linear units and more robust sensor modules where developed.

    The thesis further discusses which parts of the intelligent gripper that have been successfully implemented within the project time frame and which parts that needs to be further implemented, tested and improved.

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