Vered Aharonson

Respiration Effect on Peripheral Microcirculation Dynamics

We investigated the correlation between changes in PM and breathing using a commercial device that guides its user from normal- to slow breathing. Capillary blood flow and transcutaneous oxygen tension (tcPO2) signals were measured before, during and after the breathing guiding. We investigated the interaction between respiration and the PM-related measures at different respiration rates. We found that 1) Respiration entrained dynamically the capillary flow at respiration rate of 6-8 breaths/min but not at other rates, and 2) As respiration became slow tcPO2 increased if it initial level was high and decreased if initially low. These results enhance our insight regarding the manner, in which skin blood flow is controlled by respiration at an individual subject and therefore may have important clinical potential regarding the PM functionality under various physiological and clinical conditions.

A real-time phoneme counting algorithm and application for speech rate monitoring

Adults who stutter can learn to control and improve their speech fluency by modifying their speaking rate. Existing speech therapy technologies can assist this practice by monitoring speaking rate and providing feedback to the patient, but cannot provide an accurate, quantitative measurement of speaking rate. Moreover, most technologies are too complex and costly to be used for home practice. We developed an algorithm and a smartphone application that monitor a patients speaking rate in real time and provide user-friendly feedback to both patient and therapist. Our speaking rate computation is performed by a phoneme counting algorithm which implements spectral transition measure extraction to estimate phoneme boundaries. The algorithm is implemented in real time in a mobile application that presents its results in a user-friendly interface. The application incorporates two modes: one provides the patient with visual feedback of his/her speech rate for self-practice and another provides the speech therapist with recordings, speech rate analysis and tools to manage the patients practice. The algorithms phoneme counting accuracy was validated on ten healthy subjects who read a paragraph at slow, normal and fast paces, and was compared to manual counting of speech experts. Test-retest and intra-counter reliability were assessed. Preliminary results indicate differences of -4% to 11% between automatic and human phoneme counting. Differences were largest for slow speech. The application can thus provide reliable, user-friendly, real-time feedback for speaking rate control practice.

An Age Adapting Electrolarynx – A Feasibility Study

We propose a mathematical model for voice aging that could be used in the design of an age-adapting Electrolarynx. Voice data from public figures, at the ages of 30, 40, 50 and 60 years old, were acquired from a YouTube corpus. The voice processing consisted of an extraction of 70 Mel-Frequency Cepstral Coefficients (MFCCs) and a computation of their statistical features. ANOVA F-tests were used to determine which of these features change with age. Significant differences between age groups were found only for the first 40 MFCCs. The aging model was then constructed using non-linear regression and an averaged quadratic polynomial fit on these coefficients. Model age-adapted voices were reconstructed from the young dataset speakers’ voices and compared to their voices at older ages. The model was validated by the correlation between speakers’ MFCCs at older ages and the model-aged MFCCs. The average correlation results were in the range of 0.62 to 0.93. The results imply that the first 40 MFCCs are more susceptible to age related changes and that the proposed model has the potential to enhance the Electrolarynx by providing age adaptation as the speaker grows older.

Automated Smartphone Keyboard Error Corrections

The usability of smartphone’s touch keyboard is often hampered by typing mistakes resulting from the small size of the virtual keys relatively to the user’s finger size. Although this problem has been addressed in various methods, an optimal solution in terms of both accuracy and user experience, however, has not been achieved. We developed an algorithm that predicts users typing intentions based on a statistical geometrical modeling of the touch points area. The algorithm builds a user-adaptive virtual location of the key based on deviations probability computation. An uncertainly measure activates a language statistics engine to enhance the prediction. The algorithm was integrated into the default Android® keyboard and was tested on users. Typing error rate using the implemented algorithms was reduced by 23.1% on average. The proposed method can enhance typing accuracy and user experience and may facilitate and improve the design of smaller and cheaper touch based smartphones.

Harnessing Music to Enhance Speech Recognition

The performance of automatic speech recognition highly depends upon the speaker’s intelligibility and is affected by speech intensity and rate. Lombard reflex is an auditory feedback mechanism which is encountered when speakers spontaneously increase their voice in a noisy environment. We studied the feasibility of employing Lombard reflex to improve speech recognition without the speaker’s conscious awareness of the process. Whereas previous studied employed noises to produce this reflex, which may be unpleasant to the speakers, we studied the effects of music-induced Lombard reflex. Twenty speakers were recorded when listening to two music types: a rhythmic dance music or a calm yoga music, as well as to white noise, metronome sound and silence, and the differences in the speakers’ speech rate and intensity while listening to the different sounds were compared. Several cohort trends were observed: Speech intensity was particularly stronger in the rhythmic dance music condition for most subjects. This change was not observed for the metronome sound which had a similar rhythm. Speech rate was decreased for the yoga music condition for female speakers only. An examination of the changes in these prosodic variables for individual speakers yielded that most of them exhibited an increase in speech power and/or a decrease in speaking rate for at least one of the music types. This effect, when further explored, may be implemented in a personalized speech recognition engine, to enhance the usability of voice commands, dictation, and other speech based applications.

Enhancing Voice Quality in Vocal Tract Rehabilitation Device

The assistive devices used for vocal rehabilitation by patients after Laryngectomy produce a distinctly robotic sounding speech. This study aims at introducing human-like qualities into the synthetically generated voices. A simplified source filter model, LPC coefficients and line spectral frequencies were used to characterize the vocal tract and manipulate the acoustic properties of speech. Two different mapping functions were employed: A Gaussian mixture model (GMM) and a linear regression model (LR). Objective and subjective testing showed that both mapping functions produced significant changes in the re-synthesised speech, with the LR mapping producing slightly better results. However, the subjective listening tests indicated that re- synthesized voices improved on the synthetic voice but still lacked human quality. This may imply that the vocal tract model contains only partial information pertaining to the subjective perception of artificiality in speech. Future work is aimed at investigating an elaborate model containing the speech production excitation and radiation signals.

The response of peripheral microcirculation to gravity-induced changes

Background: The peripheral microcirculation supplies fresh blood to the small blood vessels, providing oxygen and nutrients to the tissues, removing waste, and maintaining normal homeostatic conditions. The goal of this study was to characterize the response of the peripheral microcirculation, in terms of blood flow and tissue oxygenation variables, to gravity‐induced changes. Methods: The study included 20 healthy volunteers and the experiment involved monitoring central and peripheral variables with the right hand positioned at different heights. These positions correspond to various gravitational levels. Peripheral perfusion and oxygenation were monitored using a laser Doppler flowmeter, photoplethysmograph, and transcutaneous oxygen tension monitor. Local blood pressure and respiration rate were also measured. Findings: At the heart level, tissue oxygenation displayed a nadir, while capillary flow displayed a peak. Similar but weaker changes were observed at the control hand. In contrast, the photoplethysmographs amplitude strongly decreased upon reducing the arm position below heart level. Both systolic and diastolic pressures decreased linearly between the highest to lowest arm position. Interpretation: The results may reflect peripheral compensation mechanisms, as well as an interaction between the central and peripheral cardiovascular systems, in response to local changes in blood pressure. The observed dependence of the oxygenation pattern on height could lead to important new insights for the diagnosis and treatment of problems in the regulation of tissue perfusion. HIGHLIGHTSLocal compensation mechanisms, due to local blood pressure changes, are presented.Interactions between the peripheral and central systems are described.Tissue oxygenation displayed strong dependence on hand height.A bilateral effect was observed in response to a unilateral change in pressure.The results may have impact on the clinical treatment of limb injuries.

Determining Visual Pattern Complexity for Improved Usability in Computerized Cognitive Testing

Computerized cognitive tests often entail tasks related to visual stimuli. Most tests are performed by elderly, computer naïve, and sometimes cognitively impaired subjects. Cognitive ergonomics in designing these tests can alleviate the subjects anxiety, and enhance their cognitive evaluation accuracy. One method to achieve this goal is to implement a complexity measure for the cognitive tasks and adjust the complexity level to each subjects capabilities, thus creating an adaptive psychological test. This paper details the design, implementation, and testing of a visual pattern complexity determination algorithm. The patterns used for the study were taken from computerized cognitive assessments. The algorithm was tested using three hundred binary images and compared to the complexity perceived via pair-comparison by human judges. Correlations of 72%, 74%, and 61% between human perception and the algorithms predictions were obtained for easy, medium, and hard complexity levels, respectively. The algorithm has the potential to become an accurate measure of visual pattern complexity in computerized assessment, and could improve the usability of these tests for psychometric and cognitive evaluations.

A Practical Measurement of Parkinson's Patients Gait Using Simple Walker Based Motion Sensing and Data Analysis

Copyright: 2018 ASME. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publishers website.

Visual Pattern Complexity Determination for Enhanced Usability in Cognitive Testing

Computerized cognitive tests often entail tasks related to visual stimuli. An efficient complexity measure for these tasks can enhance their cognitive evaluation accuracy, specifically for elderly and cognitively impaired subjects. This paper details the design, implementation, and testing of a visual pattern complexity determination algorithm. The patterns used for the study are sixteen-bit binary patterns taken from computerized cognitive assessments. Three complexity levels were defined based on the visual perception of human subjects: easy, medium, and hard. The algorithm was tested on three hundred patterns and the results were compared to the parallel complexities perceived by human judges. Correlations of 72%, 74%, and 61% between human perception and the algorithm’s predictions were obtained for the easy, medium, and hard patterns, respectively. The algorithm has potential to become an accurate measure of visual pattern complexity in computerized assessment, and could improve the usability of these tests for psychometric and cognitive evaluations.