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  • 1.
    Gerdtman, Christer
    Mälardalen University, School of Innovation, Design and Engineering.
    Avancerade alternativa inmatningsenheter till datorer för funktionshindrade2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Computers are important tools. People with motion disabilities sometimes are dependent on a computer and used as a technical aid the computer has improved the possibilities to perform meaningfull tasks, as writing, reading and communicating. However, disabled often need an alternative input device to control a computer.

     

    The aim with this licentiate theses has been to develope techniques to support persons with motion limitations. Focus has been to develope alternative techniqes to control a computer. Important aspects have been user-friendlieness, possiblilities to perform individual adaptions and incorporatation of specifications from the intended users. Further, the input device has been evaluated by users and applied as a rehabilitation tool for a smaller patient group of persons with whiplash associated disorders.

     

    Further, production aspects are important. To make the unit into a product, it has to be possible to produce and sell to a reasonable price. This has to be considered during the whole development process.

     

    An alternative computer mouse based on a MEMS gyroscope has been developed. The specifications made by the users has been used as a starting point in the development and the unit has been evaluated and improved in an iterative process, so called user centric development. MEMS-gyros were the type of motion sensors most corresponding to the demands. The users that participated in a longer field test were all satisfied and wanted to keep the mouse.

     

    To improve the process to choose right kind of gyro and to be able to evaluate their stability depending on factors as temperature and vibration, a test-rigg for gyros has been developed. Human motion pattern differs from industrial applications and therefor a special test-rigg was needed. The testrigg rotates the gyros and measures the sensor signal. Several gyros can be tested simultaneously and data can be stored and analysed afterwards.

     

    An interactive computerbased training program has been developed and evaluated in a pilot study together with the altrenative computer mouse. The aim has been to let people with neck injuries perform head motions and get feedback that they perform the right kind of training. The result is promising.

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    kappa
  • 2.
    Kawnine, Tanzim
    Mälardalen University, Department of Computer Science and Electronics.
    A Radial-Ulnar Deviation and Wrist-Finger Flexion Analysis Based on Electromyography2008Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This study is aimed to determine the electromyographic signals of the forearm, using Ag/AgCl electrodes. The four major muscles of forearm, which are providing the bioelectrical currents, have been displayed and analysed to determine the different activities. In order to record the signals, an EMG device has been developed and installed and a schematic has also been presented in this paper.

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    FULLTEXT01
  • 3.
    Tidare, Jonatan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Temporal representation of Motor Imagery: towards improved Brain-Computer Interface-based strokerehabilitation2021Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Practicing Motor Imagery (MI) with a Brain-Computer Interface (BCI) has shown promise in promoting motor recovery in stroke patients. A BCI records a person’s brain activity and provides feedback to the person in real time, which allows the person to practice his or her brain activity. By imagining a movement (performing MI) such as gripping with their hand, cortical areas in the brain are activated that largely overlaps with those activated during the actual hand movement. A BCI can provide positive feedback when the hand-related cortical areas are activated during MI, which helps a person to learn how to perform MI. Despite evidence that stroke patients may recover some motor function from practicing MI with BCI feedback thanks to the feedback provided from a BCI, the effectiveness and reliability of BCI-based rehabilitation are still poor. 

    A BCI can detect MI by analyzing patterns of features from the brain activity. The most common features are extracted from the oscillatory activity in the brain.  In BCI research, MI is often treated as a static pattern of features, which is detected by using machine learning algorithms to assign activity into a binary state. However, this model of MI may be inaccurate. Analyzing brain activity as dynamically varying over time and with a continuous measure of strength could better represent the cortical activity related to MI. 

    In this Licentiate thesis, I explore a method for analyzing the temporal dynamic of MI-activity with a continuous measure of strength. Brain activity was recorded with electroencephalography (EEG) and subject-specific feature patterns were extracted from a group of healthy subjects while they performed MI of two opposing hand movements: opening and closing the hand. Although MI of the two same-hand movements could not be discriminated, the continuous output from a machine learning algorithm was shown to correlate well with MI-related feature patterns. The temporal analysis also revealed that MI is dynamically encoded early, but later stabilizes into a more static pattern of brain activity. Last, to accommodate for higher temporal resolution of MI, I designed and evaluated a BCI framework by its feedback delay and uncertainty as a function of the stress on the system and found a non-linear correlation. These results could be essential for developing a BCI with time-critical feedback.

    To summarize, in this Licentiate thesis I propose a promising method for analyzing and extracting a temporal representation of MI, enabling relevant and continuous neurofeedback which may contribute to clinical advances in BCI-based stroke rehabilitation.

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  • 4.
    Trobec, R.
    et al.
    Department of Communication Systems, Jožef Stefan Institute, Ljubljana, Slovenia.
    Jan, M.
    Department of Cardiovascular Surgery, University Medical Centre, Ljubljana, Slovenia.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tomasic, Ivan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Detection and Treatment of Atrial Irregular Rhythm with Body Gadgets and 35-channel ECG2019In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 301-308Conference paper (Refereed)
    Abstract [en]

    The atrial irregular rhythm, often reflected in atrial fibrillation, undulation or flutter, is recognized as one of the major causes of brain stroke and entails an increased risk of thromboembolic events because it increases the likelihood of blood clots formation. Its early detection is becoming an increasingly important preventive measure. The paper presents a simple methodology for the detection of atrial irregular rhythm by ECG body gadget that can perform long-term measurements, e.g. several weeks or more. Multichannel ECG, on the body surface, gives a more detailed insight into the atrial activity in comparison to standard 12-lead ECG. The information from MECG is compared with single-channel patch ECG. The obtained results suggest that the proposed methodology could be useful in treatments of atrial irregular rhythm. One can obtain a reliable information about the time and duration of fibrillation events, or determine arrhythmic focuses and conductive pathways in heart atria, or study the effects of antiarrhythmic drugs on existing arrhythmias and on an eventual development of new types of arrhythmias. 

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