Neuza Nunes

Generic Biometry Algorithm Based on Signal Morphology Information: Application in the Electrocardiogram Signal

This work presents the development, test, and implementation of a new biometric identification procedure based on electrocardiogram (ECG) signal morphology. ECG data were collected from 63 subjects during two data-recording sessions separated by six months (Time Instance 1, T1, and Time Instance 2, T2). Two tests were performed aiming at subject identification, using a distance-based method with the heartbeat patterns. In both tests, the enrollment template was composed by the averaging of all the T1 waves for each subject. Two testing datasets were created with five meanwaves per subject. While in the first test the meanwaves were composed with different T1 waves, in the second test T2 waves were used. The T2 waves belonged to the same subjects but were acquired in different time instances, simulating a real biometric identification problem. The classification was performed through the implementation of a kNN classifier, using the meanwave’s Euclidean distances as the features for subject identification. The accuracy achieved was 95.2 % for the first test and 90.5 % for the second. These results were achieved with the optimization of some crucial parameters. In this work we determine the influence of those parameters, such as, the removal of signal outliers and the number of waves that compose the test meanwaves, in the overall algorithm performance. In a real time identification problem, this last parameter is related with the length of ECG signal needed to perform an accurate decision. Concerning the study here depicted, we conclude that a distance-based method using the subject’s ECG signal morphology is a valid parameter for classification in biometric applications.

Time Series Clustering Algorithm for Two-Modes Cyclic Biosignals

In this study, an automatic algorithm which computes a meanwave is introduced. The meanwave is produced by averaging all cycles of a cyclic signal, sample by sample. With that information, the signal’s morphology is captured and the similarity among its cycles is measured. A k-means clustering procedure is used to distinguish different modes in a cyclic signal, using the distance metric computed with the meanwave information. The algorithm produced is signal-independent, and therefore can be applied to any cyclic signal with no major changes in the fundamental frequency. To test the effectiveness of the proposed method, we’ve acquired several biosignals in context tasks performed by the subjects with two distinct modes in each. The algorithm successfully separates the two modes with 99.3% of efficiency. The fact that this approach doesn’t require any prior information and its preliminary good performance makes it a powerful tool for biosignals analysis and classification.

Multi-biosignals Analysis

Objectives: This study aims to evaluate the influence of standard electrical stimulation on human electrophysiology. Methods: A total of 10 healthy subjects were submitted to the same protocol. The electrical stimuli were applied on the median nerve of the left wrist. Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) signals were acquired from the index finger through an oximeter and from both the abductor pollicis muscle and the 3rd palmar interosseous muscle of the right hand, respectively. Nerve stimulation was performed using increasing intensities current: range from 5 to 30 mA, with 1mA step and applying 20 stimuli per step. Heart Rate (HR) and Heart Rate Variability (HRV) were computed, from the analysis of the latency between BVP pulses, in basal state and during stimulation. EDA parameters response latency, response rise time and readaptation slope were computed for each burst. Discussion: Electrical stimulation reveals to influence several parameters of the Autonomic Nervous System (ANS). It was easily detected an EDA rise response for each of the applied bursts and also an increase of the HRV during stimulation.

Quadriceps Muscle Fatigue and Comfort Generated by Neuromuscular Electrical Stimulation with Current Modulated Waveforms

Introduction: Neuromuscular electrical stimulation (NMES) is used by physical therapists in the clinic. The efficacy of NMES is limited by the rapid onset muscle fatigue. The role of NMES parameters is muscle fatigue is not clear. Objective: To determine the effects of shape waveform on muscle fatigue, during NMES. Methods: Twelve healthy subjects participated in the study. Subjects were assigned to 1 of 3 groups, randomly. Group assignment determined the order in which they were tested using 3 different shape waveforms. Maximal voluntary isometric contraction (MVIC) was measured during the first session. Fatigue test was applied with amplitude required to elicit 50% of the MVIC. In each 3 testing sessions torque of contraction and level comfort were measured, and percent fatigue was calculated. Analysis of variance tests for dependent samples was used to determine the effect of shape waveform on muscle fatigue and comfort scores Results: The results showed no one shape waveform was most fatigable and that SQ wave induced more uncomfortable stimulus.

Evaluation of Motor Neuron Excitability by CMAP Scanning with Electric Modulated Current

Introduction. Compound Muscle Action Potential (CMAP) scan is a noninvasive promissory technique for neurodegenerative pathologies diagnosis. In this work new CMAP scan protocols were implemented to study the influence of electrical pulse waveform on peripheral nerve excitability. Methods. A total of 13 healthy subjects were tested. Stimulation was performed with an increasing intensities range from 4 to 30 mA. The procedure was repeated 4 times per subject, using a different single pulse stimulation waveform: monophasic square and triangular and quadratic and biphasic square. Results. Different waveforms elicit different intensity-response amplitude curves. The square pulse needs less current to generate the same response amplitude regarding the other waves and this effect is gradually decreasing for the triangular, quadratic, and biphasic pulse, respectively. Conclusion. The stimulation waveform has a direct influence on the stimulus-response slope and consequently on the motoneurons excitability. This can be a new prognostic parameter for neurodegenerative disorders.

Knowledge Acquisition System based on JSON Schema for Electrophysiological Actuation

Data stored and transferred through the Internet increases every day. The problem with these data begins with the lack of structure, making information disperse, uncorrelated, non-transparent and difficult to access and share. The World Wide Web Consortium (W3C), proposed a solution for this problem, Semantic Web, promoting semantic structured data, like ontologies, enabling machines to perform more work involved in finding, combining, and acting upon information on the web. Using this to our advantage we created a Knowledge Acquisition System, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and JSON Schema to define that structure, enabling new ways of acquiring and storing knowledge semantically structured. A novel Human Computer Interaction framework was developed based on this knowledge system, providing a Electrophysiological Actuation Mechanism. We tested this mechanism by controlling an electrostimulator.

WIRELESS PLATFORM FOR ERGONOMICS EVALUATION OF OCCUPATIONAL BIOMECHANICAL EXPOSURE

Work-related musculoskeletal disorders (WRMSDs) are a global problem, both from the health or the social and economic point of view. These pathologies are the result of cumulative exposure to unfavourable working condition, namely repetitiveness, awkward postures, vibration, high force demands, work pace and insufficient recovery periods. The clinical syndromes include tendons inflammation, compression injuries, osteoarthritis or chronic pain conditions not assigned to a known pathology [Silverstein, 2001]. We present a wireless platform targeted at work ergonomics improvement through the assessment of the occupational exposure in a normal daily work routine associated to the risk of WRMSDs. The platform currently comprises two different protocols, one of electromyography (EMG) – for a comparative analysis of the work performed and the muscular load demanded from it –, and other of vibration exposure – tools transmit intense vibrations that makes vulnerable the worker’s health. The objective of this system is to make use of a good security practice at work, facilitating an ergonomics evaluation and through a nice interface, enhance the perception of risks the worker is subject during a normal work day.