Estimation of HIV incidence in a large, community-based, randomized clinical trial: NIMH project accept (HIV Prevention Trials Network 043).

BACKGROUND: National Institute of Mental Health Project Accept (HIV Prevention Trials Network [HPTN] 043) is a large, Phase III, community-randomized, HIV prevention trial conducted in 48 matched communities in Africa and Thailand. The study intervention included enhanced community-based voluntary counseling and testing. The primary endpoint was HIV incidence, assessed in a single, cross-sectional, post-intervention survey of >50,000 participants. METHODS: HIV rapid tests were performed in-country. HIV status was confirmed at a central laboratory in the United States. HIV incidence was estimated using a multi-assay algorithm (MAA) that included the BED capture immunoassay, an avidity assay, CD4 cell count, and HIV viral load. RESULTS: Data from Thailand was not used in the endpoint analysis because HIV prevalence was low. Overall, 7,361 HIV infections were identified (4 acute, 3 early, and 7,354 established infections). Samples from established infections were analyzed using the MAA; 467 MAA positive samples were identified; 29 of those samples were excluded because they contained antiretroviral drugs. HIV prevalence was 16.5% (range at study sites: 5.93% to 30.8%). HIV incidence was 1.60% (range at study sites: 0.78% to 3.90%). CONCLUSIONS: In this community-randomized trial, a MAA was used to estimate HIV incidence in a single, cross-sectional post-intervention survey. Results from this analysis were subsequently used to compare HIV incidence in the control and intervention communities. TRIAL REGISTRATION: ClinicalTrials.gov NCT00203749.

Integrated electromyogram and eye-gaze tracking cursor control system for computer users with motor disabilities.

This research pursued the conceptualization, implementation, and testing of a system that allows for computer cursor control without requiring hand movement. The target user group for this system are individuals who are unable to use their hands because of spinal dysfunction or other afflictions. The system inputs consisted of electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an eye-gaze tracking (EGT) system. Each input was processed by an algorithm that produced its own cursor update information. These algorithm outputs were fused to produce an effective and efficient cursor control. Experiments were conducted to compare the performance of EMG/EGT, EGT-only, and mouse cursor controls. The experiments revealed that, although EMG/EGT control was slower than EGT-only and mouse control, it effectively controlled the cursor without a spatial accuracy limitation and also facilitated a reliable click operation.

The integration of electromyogram and eye gaze tracking inputs for hands-free cursor control.

Electromyogram (EMG)-based and eye gaze tracking (EGT)-based hands-free cursor control input systems have been developed in the past as independent forms of cursor control. Each form of control possesses its own advantages and disadvantages in terms of usability. This paper presents a novel form of hands-free cursor control that integrates these two inputs in order to provide the user with the ability to manipulate the cursor more efficiently than the individual input systems operating in isolation. An experiment was conducted to compare the performance of this new EMG/EGT input system to EGT and mouse input systems in point-and-click trials. The results showed that while the EMG/EGT system was slower than the EGT system and the mouse, it produced a significantly smaller error rate than EGT input alone and therefore, EMG/EGT input could be considered to be a more usable form of hands-free cursor control when compared to EGT input.

Enhanced real-time cursor control algorithm, based on the spectral analysis of electromyograms.

This paper presents a new version of an EMG-based, hands-free, cursor control system, and compares its performance to that of a previous version. Both systems use classification algorithms that rely on the periodogram estimation of the power spectral density (PSD) of electromyogram (EMG) signals from muscles in the face. The older system requires three electrodes for EMG input, and utilizes an algorithm that calculates partial power accumulations over the frequency ranges of 0Hz - 145Hz and 145Hz - 600Hz in the PSDs of the EMG signals. The new system requires four electrodes for EMG input, and utilizes an algorithm that calculates mean power frequency (MPF) values to assist in distinguishing the cranial muscle that contracted. An experiment was devised to gauge the point-and-click capabilities of both systems. The experimental results were evaluated using Fitts' Law analysis. The results show that the new algorithm provides improved point-and-click performance over the old algorithm.

Biometric identification using 3D face scans.

Biometrics is an emerging area of bioengineering that pursues the characterization of a person by means of something that the person is or produces. Face recognition is a particularly attractive biometric challenge. Most of the face recognition research performed in the past used 2D intensity images. However, algorithms based on 2D images are not robust to changes of illumination in the environment or orientation of the subject. The ability to acquire 3D scans of human faces removes those ambiguities, since they capture the exact geometry of the subject, invariant to illumination and orientation changes. Unencumbered by those limitations, research in 3D face recognition is now beginning to address a different source of error in biometric recognition: facial geometry deformation caused by facial expressions, which can make 3D algorithms which treat 3D faces as rigid surfaces fail. In this paper, a 3D face recognition framework is proposed to tackle this problem. The framework is composed of three subsystems: expression recognition system, expressional face recognition system and neutral face recognition system. In particular, a system for the recognition of faces with one type of expression (smile) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

Enhanced hybrid electromyogram/Eye Gaze Tracking cursor control system for hands-free computer interaction.

This paper outlines the development and initial testing of a new hybrid computer cursor control system based on Eye Gaze Tracking (EGT) and electromyogram (EMG) processing for hands-free control of the computer cursor. The ultimate goal of the system is to provide an efficient computer interaction mechanism for individuals with severe motor disabilities (or specialized operators whose hands are committed to other tasks, such as surgeons, pilots, etc.) The paper emphasizes the enhancements that have been made on different areas of the architecture, with respect to a previous prototype developed by our group, and demonstrates the performance improvement verified for some of the enhancements.

Hands-free human computer interaction via an electromyogram-based classification algorithm.

A four-electrode system for hands-free computer cursor control, based on the digital processing of Electromyogram (EMG) signals is proposed. The electrodes are located over the right frontalis, the procerus, the left temporalis and the right temporalis muscles in the head. This system is meant to enable individuals paralyzed from the neck down (e.g., due to Spinal Cord Injury) to interact with computers using point-and-click graphic interfaces. The intention is to translate electromyograms derived from muscle contractions associated with specific facial movements into five cursor actions, namely: Left, Right, Up, Down and Left-click. This translation is accomplished by a digital signal processing classification algorithm that takes advantage of the divergent spectral nature of the EMG signals produced by the frontalis, temporalis, and procerus muscles, respectively. The effectiveness of the algorithm is evaluated by comparing its performance to that of a previously developed three-electrode EMG-based algorithm, using Matlab simulations. The results indicate that the algorithm classifies with great accuracy and provides a marked improvement over the previous three-electrode system.

User stress detection in human-computer interactions.

The emerging research area of Affective Computing seeks to advance the field of Human-Computer Interaction (HCI) by enabling computers to interact with users in ways appropriate to their affective states. Affect recognition, including the use of psychophysiologcal measures (e.g. heart rate), facial expressions, speech recognition etc. to derive an assessment of user affective state based on factors from the current task context, is an important foundation required for the development of Affective Computing. Our research focuses on the use of three physiological signals: Blood Volume Pulse (BVP), Galvanic Skin Response (GSR) and Pupil Diameter (PD), to automatically monitor the level of stress in computer users. This paper reports on the hardware and software instrumentation development and signal processing approach used to detect the stress level of a subject interacting with a computer, within the framework of a specific experimental task, which is called the 'Stroop Test'. For this experiment, a computer game was implemented and adapted to make the subject experience the Stroop Effect, evoked by the mismatch between the font color and the meaning of a certain word (name of a color) displayed, while his/her BVP, GSR and PD signals were continuously recorded. Several data processing techniques were applied to extract effective attributes of the stress level of the subjects throughout the experiment. Current results indicate that there exists interesting similarity among changes in those three signals and the shift in the emotional states when stress stimuli are applied to the interaction environment.