Viral load suppression and acquired HIV drug resistance in adults receiving antiretroviral therapy in Viet Nam: results from a nationally representative survey

Objective@#The purpose of this survey was to estimate the prevalence of viral load (VL) suppression and emergence of HIV drug resistance (HIVDR) among individuals receiving antiretroviral therapy (ART) for 36 months or longer in Viet Nam using a nationally representative sampling method.@*Methods@#The survey was conducted between May and August 2014 using a two-stage cluster design. Sixteen ART clinics were selected using probability proportional to proxy size sampling, and patients receiving ART for at least 36 months were consecutively enrolled. Epidemiological information and blood specimens were collected for HIV-1 VL and HIVDR testing; HIVDR was defined by the Stanford University HIVDR algorithm.@*Results@#Overall, 365 eligible individuals were recruited with a mean age of 38.2 years; 68.4% were men. The mean time on ART was 75.5 months (95% confidence interval [CI]: 69.0–81.9 months), and 93.7% of the patients were receiving non-nucleoside reverse transcriptase inhibitor-based regimens. Of the 365 individuals, 345 (94.7%, 95% CI: 64.1–99.4%) had VL below 1000 copies/mL and 19 (4.6%, 95% CI: 2.8-–7.5) had HIVDR mutations.@*Discussion@#Our nationally representative survey found a high level of VL suppression and a low prevalence of HIVDR among individuals who received ART for at least 36 months in Viet Nam. Continued surveillance for HIVDR is important for evaluating and improving HIV programs.

Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

Real-time transmission of panoramic images for a telepresence wheelchair.

This paper proposes an approach to transmit panoramic images in real-time for a telepresence wheelchair. The system can provide remote monitoring and assistive assistance for people with disabilities. This study exploits technological advancement in image processing, wireless communication networks, and healthcare systems. High resolution panoramic images are extracted from the camera which is mounted on the wheelchair. The panoramic images are streamed in real-time via a wireless network. The experimental results show that streaming speed is up to 250 KBps. The subjective quality assessments show that the received images are smooth during the streaming period. In addition, in terms of the objective image quality evaluation the average peak signal-to-noise ratio of the reconstructed images is measured to be 39.19 dB which reveals high quality of images.

A novel target following solution for the electric powered hospital bed.

The paper proposes a novel target following solution for an electric powered hospital bed. First, an improved real-time decoupling multivariable control strategy is introduced to stabilize the overall system during its operation. Environment laser-based data are then collected and pre-processed before engaging a neural network classifier for target detection. Finally, a high-level control algorithm is implemented to guarantee safety condition while the hospital bed tracks its target. The proposed solution is successfully validated through real-time experiments.

Advanced portable remote monitoring system for the regulation of treadmill running exercises.

OBJECTIVE: This study aims to develop an advanced portable remote monitoring system to supervise high intensity treadmill exercises. MATERIALS AND METHODS: The supervisory level of the developed hierarchical system is implemented on a portable monitoring device (iPhone/iPad) as a client application, while the real-time control of treadmill exercises is accomplished by using an on-line adaptive neural network control scheme in a local computer system. During training or rehabilitation exercises, the intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient. In order to achieve adaptive tracking performance, a neural network controller has been designed and implemented. RESULTS: Six real-time experiments have been conducted to test the performance of the developed monitoring system. Experimental results obtained in real-time with heart-rate set-point varying from 145 bpm to 180 bmp, demonstrate that the proposed system can quickly and accurately regulate exercise intensity of treadmill running exercises with desired performance (no overshoot, settling time Ts ≤ 100 s). Subjects aged from 29 to 38 years old participated in different set-point experiments to confirm the system's adaptability to inter- and intra-model uncertainty. The desired system performance under external disturbances has also been confirmed in a final real-time experiment demonstrating a user carrying the 10 kg bag then removing it during the exercise. CONCLUSION: In contrast with conventional control approaches, the proposed adaptive controller achieves better heart rate tracking performance under inter- and intra-model uncertainty and external disturbances. The developed system can automatically adapt to various individual exercisers and a range of exercise intensity.

Neural network based diagonal decoupling control of powered wheelchair systems.

This paper proposes an advanced diagonal decoupling control method for powered wheelchair systems. This control method is based on a combination of the systematic diagonalization technique and the neural network control design. As such, this control method reduces coupling effects on a multivariable system, leading to independent control design procedures. Using an obtained dynamic model, the problem of the plant's Jacobian calculation is eliminated in a neural network control design. The effectiveness of the proposed control method is verified in a real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty effects.

An advanced control strategy of an electrical--powered hospital bed.

This paper develops a multivariable control technique for low-level control of an intelligent hospital bed. First, multivariable hospital bed models, nominal, upper bounded and lower bounded models, are obtained via an experimental identification procedure. Based on the obtained nominal model, the triangular diagonal dominance (TDD) decoupling technique is applied to reduce a complex multivariable system into a series of scalar systems. For each scalar system, an online adaptive control strategy is then developed to cope with system uncertainties. Compared to the conventional control method, real-time experimental results showed that our proposed multivariable control technique achieved better performance. Experimental results also confirmed that desirable system performance was guaranteed under system uncertainty conditions.

Development of an assistive patient mobile system for hospital environments.

This paper presents an assistive patient mobile system for hospital environments, which focuses on transferring the patient without nursing help. The system is a combination of an advanced hospital bed and an autonomous navigating robot. This intelligent bed can track the robot and routinely navigates and communicates with the bed. The work centralizes in building a structure, hardware design and robot detection and tracking algorithms by using laser range finder. The assistive patient mobile system has been tested and the real experiments are shown with a high performance of reliability and practicality. The accuracy of the method proposed in this paper is 91% for the targeted testing object with the error rate of classification by 6%. Additionally, a comparison between our method and a related one is also described including the comparison of results.

Real-time performance of a hands-free semi-autonomous wheelchair system using a combination of stereoscopic and spherical vision.

This paper is concerned with the operational performance of a semi-autonomous wheelchair system named TIM (Thought-controlled Intelligent Machine), which uses cameras in a system configuration modeled on the vision system of a horse. This new camera configuration utilizes stereoscopic vision for 3-Dimensional (3D) depth perception and mapping ahead of the wheelchair, combined with a spherical camera system for 360-degrees of monocular vision. The unique combination allows for static components of an unknown environment to be mapped and any surrounding dynamic obstacles to be detected, during real-time autonomous navigation, minimizing blind-spots and preventing accidental collisions with people or obstacles. Combining this vision system with a shared control strategy provides intelligent assistive guidance during wheelchair navigation, and can accompany any hands-free wheelchair control technology for people with severe physical disability. Testing of this system in crowded dynamic environments has displayed the feasibility and real-time performance of this system when assisting hands-free control technologies, in this case being a proof-of-concept brain-computer interface (BCI).

Robust neuro-sliding mode multivariable control strategy for powered wheelchairs.

This paper proposes an advanced robust multivariable control strategy for a powered wheelchair system. The new control strategy is based on a combination of the systematic triangularization technique and the robust neuro-sliding mode control approach. This strategy effectively copes with parameter uncertainties and external disturbances in real-time in order to achieve robustness and optimal performance of a multivariable system. This novel strategy reduces coupling effects on a multivariable system, eliminates chattering phenomena, and avoids the plant Jacobian calculation problem. Furthermore, the strategy can also achieve fast and global convergence using less computation. The effectiveness of the new multivariable control strategy is verified in real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty and external disturbance effects.

Robust online adaptive neural network control for the regulation of treadmill exercises.

The paper proposes a robust online adaptive neural network control scheme for an automated treadmill system. The proposed control scheme is based on Feedback-Error Learning Approach (FELA), by using which the plant Jacobian calculation problem is avoided. Modification of the learning algorithm is proposed to solve the overtraining issue, guaranteeing to system stability and system convergence. As an adaptive neural network controller can adapt itself to deal with system uncertainties and external disturbances, this scheme is very suitable for treadmill exercise regulation when the model of the exerciser is unknown or inaccurate. In this study, exercise intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient in order to achieve fast tracking for which a single input multi output (SIMO) adaptive neural network controller has been designed. Real-time experiment result confirms that robust performance for nonlinear multivariable system under model uncertainties and unknown external disturbances can indeed be achieved.