Beena Ahmed

Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors

Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects.

An automatic sleep spindle detector based on wavelets and the teager energy operator

Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.

Chill-Out: Relaxation Training through Respiratory Biofeedback in a Mobile Casual Game

We present Chill-Out, an adaptive biofeedback game that teaches relaxation skills by monitoring the breathing rate of the player. The game uses a positive feedback loop that penalizes fast breathing by means of a proportional-derivative control law: rapid (and/or increasing) breathing rates increase game difficulty and reduce the final score of the game. We evaluated Chill-Out against a conventional non-biofeedback game and traditional relaxation based on deep breathing. Measurements of breathing rate, electrodermal activity, and heart rate variability show that playing Chill-Out leads to lower arousal during a subsequent task designed to induce stress.

Computer based sleep staging: Challenges for the future

Studies have shown that patients suffering from sleep deprivation have a risk for hypertension, diabetes and depression that is higher than normal sleepers. Treatment for all these problems requires accurate analysis of the sleep stages and patterns in the polysomnographic signals collected in overnight recording over several months. However, manual sleep staging is a repetitive and time-consuming process as marking one typical eight hours overnight polysomnographic recording can take up to two hours to complete. Due to increased processing capabilities, it is now possible to automate this process and assist the sleep expert. A large number of algorithms have been proposed during the last few decades. This review article presents an overview of the existing automatic sleep staging methods, discusses the different challenges and proposes future prospects for new research opportunities.

Automatic classification of unequal lexical stress patterns using machine learning algorithms

Technology based speech therapy systems are severely handicapped due to the absence of accurate prosodic event identification algorithms. This paper introduces an automatic method for the classification of strong-weak (SW) and weak-strong (WS) stress patterns in children speech with American English accent, for use in the assessment of the speech dysprosody. We investigate the ability of two sets of features used to train classifiers to identify the variation in lexical stress between two consecutive syllables. The first set consists of traditional features derived from measurements of pitch, intensity and duration, whereas the second set consists of energies of different filter banks. Three different classifiers were used in the experiments: an Artificial Neural Network (ANN) classifier with a single hidden layer, Support Vector Machine (SVM) classifier with both linear and Gaussian kernels and the Maximum Entropy modeling (MaxEnt). these features. Best results were obtained using an ANN classifier and a combination of the two sets of features. The system correctly classified 94% of the SW stress patterns and 76% of the WS stress patterns.

A new mHealth communication framework for use in wearable WBANs and mobile technologies.

Driven by the development of biomedical sensors and the availability of high mobile bandwidth, mobile health (mHealth) systems are now offering a wider range of new services. This revolution makes the idea of in-home health monitoring practical and provides the opportunity for assessment in “real-world” environments producing more ecologically valid data. In the field of insomnia diagnosis, for example, it is now possible to offer patients wearable sleep monitoring systems which can be used in the comfort of their homes over long periods of time. The recorded data collected from body sensors can be sent to a remote clinical back-end system for analysis and assessment. Most of the research on sleep reported in the literature mainly looks into how to automate the analysis of the sleep data and does not address the problem of the efficient encoding and secure transmissions of the collected health data. This article reviews the key enabling communication technologies and research challenges for the design of efficient mHealth systems. An end-to-end mHealth system architecture enabling the remote assessment and monitoring of patients sleep disorders is then proposed and described as a case study. Finally, various mHealth data serialization formats and machine-to-machine (M2M) communication protocols are evaluated and compared under realistic operating conditions.

Architecture of an automated therapy tool for childhood apraxia of speech

We present a multi-tier system for the remote administration of speech therapy to children with apraxia of speech. The system uses a client-server architecture model and facilitates task-oriented remote therapeutic training in both in-home and clinical settings. Namely, the system allows a speech therapist to remotely assign speech production exercises to each child through a web interface, and the child to practice these exercises on a mobile device. The mobile app records the childs utterances and streams them to a back-end server for automated scoring by a speech-analysis engine. The therapist can then review the individual recordings and the automated scores through a web interface, provide feedback to the child, and adapt the training program as needed. We validated the system through a pilot study with children diagnosed with apraxia of speech, and their parents and speech therapists. Here we describe the overall client-server architecture, middleware tools used to build the system, the speech-analysis tools for automatic scoring of recorded utterances, and results from the pilot study. Our results support the feasibility of the system as a complement to traditional face-to-face therapy through the use of mobile tools and automated speech analysis algorithms.

Development of a Remote Therapy Tool for Childhood Apraxia of Speech

We present a multitier system for the remote administration of speech therapy to children with apraxia of speech. The system uses a client-server architecture model and facilitates task-oriented remote therapeutic training in both in-home and clinical settings. The system allows a speech language pathologist (SLP) to remotely assign speech production exercises to each child through a web interface and the child to practice these exercises in the form of a game on a mobile device. The mobile app records the childs utterances and streams them to a back-end server for automated scoring by a speech-analysis engine. The SLP can then review the individual recordings and the automated scores through a web interface, provide feedback to the child, and adapt the training program as needed. We have validated the system through a pilot study with children diagnosed with apraxia of speech, their parents, and SLPs. Here, we describe the overall client-server architecture, middleware tools used to build the system, speech-analysis tools for automatic scoring of utterances, and present results from a clinical study. Our results support the feasibility of the system as a complement to traditional face-to-face therapy through the use of mobile tools and automated speech analysis algorithms.

Improved spindle detection through intuitive pre-processing of electroencephalogram.

BACKGROUND Numerous signal processing techniques have been proposed for automated spindle detection on EEG recordings with varying degrees of success. While the latest techniques usually introduce computational complexity and/or vagueness, the conventional techniques attempted in literature have led to poor results. This study presents a spindle detection approach which relies on intuitive pre-processing of the EEG prior to spindle detection, thus resulting in higher accuracy even with standard techniques. NEW METHOD The pre-processing techniques proposed include applying the derivative operator on the EEG, suppressing the background activity using Empirical Mode Decomposition and shortlisting candidate EEG segments based on eye-movements on the EOG. RESULTS/COMPARISON Results show that standard signal processing tools such as wavelets and Fourier transforms perform much better when coupled with apt pre-processing techniques. The developed algorithm also relies on data-driven thresholds ensuring its adaptability to inter-subject and inter-scorer variability. When tested on sample EEG segments scored by multiple experts, the algorithm identified spindles with average sensitivities of 96.14 and 92.85% and specificities of 87.59 and 84.85% for Fourier transform and wavelets respectively. These results are found to be on par with results obtained by other recent studies in this area.

Robotics: Its effectiveness as a tool to teach engineering design and computer programming

In this paper we will discuss the experience of using simple robotics projects to introduce first year students to engineering. The key learning objectives and tools used to implement them and the evaluation results will be detailed. Evaluation results show that robotics projects are effective in engaging the students and an effective active learning tool.