Electromyography, EMG is an experimental technique concerned with the development, recording and analysis of myoelectric signals. Myoelectric signals are formed by physiological variations in the state of the muscle fiber membranes. EMG is also a diagnostic method used to evaluate the muscle status and influence on the locomotor systems and human mobility. In the paper, the processes of the acquisition, processing, and comparison of the different EMG signals captured during a patient training was carried out. In result of processed data based on real surface EMG reads, the objective models of the gait cycles for different muscles were obtained to support medical and physiotherapeutic diagnosis by the computer-aided tools. After four-weeks VR game training, the gait examination based on EMG signals were performed again, to study and compare the muscle activation terms for gait cycle status improvement by the assessment in the terms of objective data-driven model.
The main aim of this paper is to verify an objective method of averaging electromyographic signals for cyclic processes, which are presented on Symposium Wilga 2016 . The process of creating an objective EMG signal model will be considered. The filtration, the method of smoothing the signal, the sequence of actions and the way they were carried out were taken into account. It is also attempts to measure and analyze the electromyographic signals obtained from the muscles of the lower limbs during the cyclic activity of walking with different loads. This research is based on the data collected in the Laboratory of Movement Analysis at the Medical University of Warsaw. The object of research was a healthy man in the age of 35. Based on the collected data, 130 objective EMG models were created. They correspond to ten muscles of the lower limbs. There were 13 different models calculated for each muscle, corresponding to its activity during the unloaded gait cycle and with left-, right- and double-sided loading for 2,4,6 and 8 kg. The signal models created in this way became the basis for the analysis, as a function of the load, of certain parameters of EMG signals generated during the walk. For each of the EMG signal models, the following parameters were calculated: a moment of muscle activation, duration of muscle activation, physiological costs incurred during muscle activation. Collected data could compere muscle activities and calculate the phases of their interactions.
EMG signals are small potentials appearing at the surface of human skin during muscle work. They arise due to changes in the physiological state of cell membranes in the muscle fibers. They are characterized by a relatively low frequency range (500 Hz) and a low amplitude signal (of the order of μV), making it difficult to record. Raw EMG signal is inherently random shape. However we can distinguish certain features related to the activation of the muscles of a deterministic or quasi-deterministic associated with the movement and its parametric description. Objective models of EMG signals were created on the base of actual data obtained from the VICON system installed at the University of Physical Education in Warsaw. The object of research (healthy woman) moved repeatedly after a fixed track. On her body 35 reflective markers to record the gait kinematics and 8 electrodes to record EMG signals were placed. We obtained research data included more than 1,000 EMG signals synchronized with the phases of gait. Test result of the work is an algorithm for obtaining the average EMG signal received from the multiple registration gait cycles carried out in the same reproducible conditions. The method described in the article is essentially a pre-finding measurement data from the two quasi-synchronous signals at different sampling frequencies for further processing. This signal is characterized by a significant reduction of high frequency noise and emphasis on the specific characteristics of individual records found in muscle activity.
In the age of modern, hyperconnected society that increasingly relies on mobile devices and solutions, implementing a reliable and accurate biometric system employing iris recognition presents new challenges. Typical biometric systems employing iris analysis require expensive and complicated hardware. We therefore explore an alternative way using visible spectrum iris imaging.
This paper aims at answering several questions related to applying iris biometrics for images obtained in the visible spectrum using smartphone camera. Can irides be successfully and effortlessly imaged using a smartphone's built-in camera? Can existing iris recognition methods perform well when presented with such images? The main advantage of using near-infrared (NIR) illumination in dedicated iris recognition cameras is good performance almost independent of the iris color and pigmentation. Are the images obtained from smartphone's camera of sufficient quality even for the dark irides?
We present experiments incorporating simple image preprocessing to find the best visibility of iris texture, followed by a performance study to assess whether iris recognition methods originally aimed at NIR iris images perform well with visible light images. To our best knowledge this is the first comprehensive analysis of iris recognition performance using a database of high-quality images collected in visible light using the smartphones flashlight together with the application of commercial off-the-shelf (COTS) iris recognition methods.