Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine.

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g.

Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot.

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major difficulty of the EMG decoder modeling is to decode EMG signals with high accuracy in real-time. Our recent study presented a switching mechanism for carving up a complex task into simple subtasks and trained different submodels with low nonlinearity.

A Per-sample Digitized Algorithm for Automatically Detecting Apnea and Hypopnea Events from Airflow and Oximetry.

Sleep apnea is a common sleep disorder that can significantly decrease the quality of life. An accurate and early diagnosis of sleep apnea is required before getting proper treatment. A reliable automated detection of sleep apnea can overcome the problems of manual diagnosis (scoring) due to variability in recording and scoring criteria (for example across Europe) and to inter-scorer variability.

Voluntary Control of an Ankle Joint Exoskeleton by Able-Bodied Individuals and Stroke Survivors Using EMG-Based Admittance Control Scheme.

Control schemes based on electromyography (EMG) have demonstrated their superiority in human-robot cooperation due to the fact that motion intention can be well estimated by EMG signals. However, there are several limitations due to the noisy nature of EMG signals and the inaccuracy of EMG-force/torque estimation, which might deteriorate the stability of human-robot cooperation movement. To improve the movement stability, an EMG-based admittance control scheme (EACS) was proposed, comprised of an EMG-driven musculoskeletal model (EDMM), an admittance filter and an inner position controller.

A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier.

Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitatively. Based on the human emotion representation on physiological signal, this paper offers an hybrid approach combining electroencephalogram (EEG) and facial expression together to classify the human emotion.

Continuous Description of Human 3D Motion Intent Through Switching Mechanism.

Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises.

Dynamic changes in electroencephalogram spectral power with varying apnea duration in older adults.

Sleep apnea elicits brain and physiological changes and its duration varies across the night. This study investigates the changes in the relative powers in electroencephalogram (EEG) frequency bands before and at apnea termination and as a function of apnea duration. The analysis was performed on 30 sleep records (375 apnea events) of older adults diagnosed with sleep apnea.

Air Quality Monitoring for Vulnerable Groups in Residential Environments Using a Multiple Hazard Gas Detector.

This paper presents a smart "e-nose" device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multiple hazard gas detector (MHGD) system that is mounted on a motor vehicle-based robot which can be remotely controlled.

NOS.E: A New Fast Response Electronic Nose Health Monitoring System.

We present a practical electronic nose (e-nose) sys-tem, NOS.E, for the rapid detection and identification of human health conditions. By detecting the changes in the composition of an individual's respiratory gases, which have been shown to be linked to changes in metabolism, e-nose systems can be used to characterize the physical health condition.

Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint.

This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration.

Nonparametric dynamical model of cardiorespiratory responses at the onset and offset of treadmill exercises.

This paper applies a nonparametric modelling method with kernel-based regularization to estimate the carbon dioxide production during jogging exercises. The kernel selection and regularization strategies have been discussed; several commonly used kernels are compared regarding the goodness-of-fit, sensitivity, and stability. Based on that, the most appropriate kernel is then selected for the construction of the regularization term.

The classification for "equilibrium triad" sensory loss based on sEMG signals of calf muscles.

Surface Electromyography (sEMG) has been commonly applied for analysing the electrical activities of skeletal muscles. The sensory system of maintaining posture balance includes vision, proprioception and vestibular senses. In this work, an attempt is made to classify whether the body is missing one of the sense during balance control by using sEMG signals.

Nonparametric modelling of VO response to exercise.

This paper investigates the modelling of oxygen consumption (VO) response to jogging exercise on treadmill. Unlike most of the previous methods, which often use simple parametric models, e.g.

Real-time identification of heart rate responses via auxiliary-model-based damped RLS scheme.

This study was devoted to developing a new auxiliary-model-based damped recursive least squares (AMB-DRLS) by which the heart rate dynamics can be identified in a real-time manner. Unlike the current conventional schemes for heart rate dynamics modeling, the proposed scheme can simultaneously identify the HR response dynamics and compensate for the existing HR variability while it can also cope with the blowup phenomenon. The performance of the proposed AMB-DRLS scheme was experimentally verified using fifteen healthy male participants who performed treadmill trials following single-cycle square wave protocol.

EEG-based emotion classification using innovative features and combined SVM and HMM classifier.

Emotion classification is one of the state-of-the-art topics in biomedical signal research, and yet a significant portion remains unknown. This paper offers a novel approach with a combined classifier to recognise human emotion states based on electroencephalogram (EEG) signal. The objective is to achieve high accuracy using the combined classifier designed, which categorises the extracted features calculated from time domain features and Discrete Wavelet Transform (DWT).

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited.

Real-time modelling of heart rate response during exercise using a novel constrained parameter estimation method.

This paper is devoted to the problem of real-time heart rate (HR) response modelling during treadmill exercise. A novel recursive constrained parameter estimation method is developed which in contrast to the conventional parameter estimation schemes (e.g.

Heart rate regulation during cycle-ergometer exercise using damped parameter estimation method.

This paper is devoted to the problem of heart rate regulation using a model-based control strategy and a realtime damped parameter estimation scheme. The controller is a time-varying integral sliding mode controller. A recursive damped parameter estimation method is also developed, by incorporation of a weighting upon the one-step parameter variation, which in contrast to the conventional parameter estimation schemes (e.

An online recursive autocalibration of triaxial accelerometer.

In this paper, we proposed a novel method for autocalibration of triaxial Micro-Electro-Mechanical systems (MEMS) accelerometer that does not require any sophisticated laboratory facilities. In particular, this method is an online calibration method which can be conveniently implemented with the accuracy of MEMS accelerometer being significantly improved. The procedure exploits the fact that the output vector of the accelerometer must match the local gravity in static state condition.

Heart rate regulation during cycle-ergometer exercise via event-driven biofeedback.

This paper is devoted to the problem of regulating the heart rate response along a predetermined reference profile, for cycle-ergometer exercises designed for training or cardio-respiratory rehabilitation. The controller designed in this study is a non-conventional, non-model-based, proportional, integral and derivative (PID) controller. The PID controller commands can be transmitted as biofeedback auditory commands, which can be heard and interpreted by the exercising subject to increase or reduce exercise intensity.

Designing adaptive integral sliding mode control for heart rate regulation during cycle-ergometer exercise using bio-feedback.

This paper considers our developed control system which aims to regulate the exercising subjects' heart rate (HR) to a predefined profile. The controller would be an adaptive integral sliding mode controller. Here it is assumed that the controller commands are interpreted as biofeedback auditory commands.

Heart rate regulation during cycle-ergometer exercise via bio-feedback.

This paper explains our developed control system which regulates the heart rate (HR) to track a desired trajectory. The controller is indeed a non-conventional non-model-based proportional, integral and derivative (PID) controller. The controller commands are interpreted as biofeedback auditory commands.

Optimum Experimental Design applied to MEMS accelerometer calibration for 9-parameter auto-calibration model.

Optimum Experimental Design (OED) is an information gathering technique used to estimate parameters, which aims to minimize the variance of parameter estimation and prediction. In this paper, we further investigate an OED for MEMS accelerometer calibration of the 9-parameter auto-calibration model. Based on a linearized 9-parameter accelerometer model, we show the proposed OED is both G-optimal and rotatable, which are the desired properties for the calibration of wearable sensors for which only simple calibration devices are available.

On heart rate regulation in cycle-ergometer exercise.

In this paper, we have focused on the issue of regulating the human heart rate (HR) to a predefined reference trajectory, especially for cycle-ergometer exercise used for training or rehabilitation. As measuring HR is relatively easy compared to exercise intensity, it has been used in the wide range of training programs. The aim of this paper is to develop a non-model-based control strategy using proportional, integral and derivative (PID) controller/relay controller to regulate the HR to track a desired trajectory.

An equivalent circuit model for onset and offset exercise response.

Background: The switching exercise (e.g., Interval Training) has been a commonly used exercise protocol nowadays for the enhancement of exerciser's cardiovascular fitness.