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., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA-KNN-SVM classifier was 96.45%, and the LDA-KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.

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. However, it was observed that a "bump" behavior of decoder output (i.e., the discontinuity) occurred during the switching between two submodels. The bumps might cause unexpected impacts on the affected limb and thus potentially injure patients. To improve this undesired transient behavior on decoder outputs, we attempt to maintain the continuity of the outputs during the switching between multiple submodels. A bumpless switching mechanism is proposed by parameterizing submodels with all shared states and applied in the construction of the EMG decoder. Numerical simulation and real-time experiments demonstrated that the bumpless decoder shows high estimation accuracy in both offline and online EMG decoding. Furthermore, the outputs achieved by the proposed bumpless decoder in both testing and verification phases are significantly smoother than the ones obtained by a multimodel decoder without a bumpless switching mechanism. Therefore, the bumpless switching approach can be used to provide a smooth and accurate motion intent prediction from multi-channel EMG signals. Indeed, the method can actually prevent participants from being exposed to the risk of unpredictable loads.

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. To investigate the performance of EACS, a series of sinusoidal tracking tasks were conducted with 12 healthy participants and 4 stroke survivors in an ankle exoskeleton in comparison with the EMG-based open-loop control scheme (EOCS). The experimental results indicated that both EACS and EOCS could improve stroke survivors' ankle range of motion (ROM). The experimental results of both healthy participants and stroke survivors showed that the assistance torque, tracking error and jerk values of EACS were lower than those of EOCS. The interaction torque of EACS decreased towards the increasing assistance ratio while that of EOCS increased. Moreover, the EMG levels of tibialis anterior (TA) decreased towards the increasing assistance ratio but were higher than those of EOCS. EACS was effective in improving movements stability, and had the potential to be applied in robot-assisted rehabilitation training to address the foot-drop problem.

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. This study explored a novel automated algorithm to detect apnea and hypopnea events from airflow and pulse oximetry signals, extracted from 30 polysomnography records of the Sleep Heart Health Study. Apneas and hypopneas were manually scored by a trained sleep physiologist according to the updated 2017 American Academy of Sleep Medicine respiratory scoring rules. From pre-processed airflow, the peak signal excursion was precisely determined from the peak-to-trough amplitude using a sliding window, with a per-sample digitized algorithm for detecting apnea and hypopnea. For apnea, the peak signal excursion drop was operationalized at ≥85% and for hypopnea at ≥35% of its pre-event baseline. Using backward shifting of oximetry, hypopneas were filtered with ≥3% oxygen desaturation from its baseline. The performance of the automated algorithm was evaluated by comparing the detection with manual scoring (a standard practice). The sensitivity and positive predictive value of detecting apneas and hypopneas were respectively 98.1% and 95.3%. This automated algorithm is applicable to any portable sleep monitoring device for the accurate detection of sleep apnea.

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. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.

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. Power spectral analysis centered on two 10-s EEG epochs, before apnea termination (BAT) and after apnea termination (AAT), for each apnea event. The relative power changes in EEG frequency bands were compared with changes in apnea duration, defined as Short (between 10 and 20 s), Moderate (between 20 and 30 s) and Long (between 30 and 40 s). A significant reduction in EEG relative powers for lower frequency bands of alpha and sigma were observed for the Long compared to the Moderate and Short apnea duration groups at BAT, and reduction in relative theta, alpha and sigma powers for the Long compared to the Moderate and Short groups at AAT. The proportion of apnea events showed a significantly decreased trend with increased apnea duration for non-rapid eye movement sleep but not rapid eye movement sleep. The proportion of central apnea events decreased with increased apnea duration, but not obstructive episodes. The findings suggest EEG arousal occurred both before and at apnea termination and these transient arousals were associated with a reduction in relative EEG powers of the low-frequency bands: theta, alpha and sigma. The clinical implication is that these transient EEG arousals, without awakenings, are protective of sleep. Further studies with large datasets and different age groups are recommended.

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. Several advanced signal processing techniques are used to simplify the data and extract the features involving local binary patterns (LBP), Compressed Sensing (CS) and Wavelet Transform (WT). A novel machine learning algorithm, combined Fuzzy Cognitive Maps (FCM) and Support Vector Machine (SVM) are implemented to recognise the feature patterns. The result illustrates a stable emotion classification system with 75.64% accuracy. This design can provide fast and precise emotional feedback, which would further improve the communication between human and computer.

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. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods.

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. We demonstrated our system's viability with a simple data set consists of breath collected under three different scenarios from one volunteer. Our preliminary results show the popular classifier SVM can discriminate NOS.E's responses under the three scenarios with high performance. In future work, we will aim to gather a more varied data set to test NOS.E's abilities.

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. Both the onset and offset of the jogging exercises are investigated. We compare the identified nonparametric models, which include both impulse response models and step response models for the two periods, as well as the relationship between oxygen consumption and carbon dioxide production. The result statistically indicates that the steady-state gain of the carbon dioxide production in the onset of exercise is bigger than that in the offset while the response time of both onset and offset are similar. Compared with oxygen consumption, the response speed of carbon dioxide production is slightly slower in both onset and offset period while its steady-state gains are similar for both periods. The effectiveness of the kernel-based method for the dynamic modelling of cardiorespiratory response to exercise is also well demonstrated. Graphical Abstract Comparison between VO2 and VCO2 during onset and offset of exercise.

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). Two innovative designs are involved in this project: a novel variable is established as a new feature and a combined SVM and HMM classifier is developed. The result shows that the joined features raise the accuracy by 5% on valence axis and 1.5% on arousal axis. The combined classifier can improve the accuracy by 3% comparing with SVM classifier. One of the important applications for high accuracy emotion classification system is offering a powerful tool for psychologists to diagnose emotion related mental diseases and the system developed in this project has the potential to serve such purpose.

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. The obtained results revealed a significant difference in goodness-of-fit for the considered parameter estimation schemes. As a result, we conclude that the proposed AMB-DRLS method is able to identify the heart rate response dynamics in a real-time manner while preventing the blowup phenomenon.

Nonparametric modelling of VO2 response to exercise.

This paper investigates the modelling of oxygen consumption (VO2) response to jogging exercise on treadmill. Unlike most of the previous methods, which often use simple parametric models, e.g., first order linear time invariant model, this study applied a nonparametric kernel based regularised method to estimate VO2 to address the ill-conditioned modelling problem and achieve accurate estimation. In particular, it is worthy to be noted that the selection of kernels will affect the results for different modelling scenarios. Therefore, in this research, both radial basis kernel and stable spline kernel were selected for testing. In order to select the favourable kernel for this system, a simulation related to VO2-jogging speed was carried out. The results of simulation indicated that spline kernel can achieve higher accuracy comparing to radial basis function kernel. Experimentally, the kernel based estimation method and spline kernel were tested using six participants. From the results, an average impulse response is obtained. It showed the VO2 estimation, based on the average finite impulse response, is fitted well to the six observations collected from the participants.

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. A trial of combination with different features and muscles is also developed. The results demonstrate that the classification accuracy between vision loss and the normal condition is higher than the one between vestibular sense loss and normal condition. When using different features and muscles, the impact on classification results is also different. The outcomes of this study could aid the development of sEMG based classification for the function of sensory systems during human balance movement.

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. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which outperforms other feature models.

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. However, in such a case, for the purposes of effectively communicating to the exercising subject a change in the required exercise intensity, the timing of this feedback signal relative to the position of the pedals becomes critical. A feedback signal delivered when the pedals are not in a suitable position to efficiently exert force may be ineffective and this may, in turn, lead to the cognitive disengagement of the user from the feedback controller. This note examines a novel form of control system which has been expressly designed for this project. The system is called an "actuator-based event-driven control system". The proposed control system was experimentally verified using 24 healthy male subjects who were randomly divided into two separate groups, along with cross-validation scheme. A statistical analysis was employed to test the generalisation of the PID tunes, derived based on the average transfer functions of the two groups, and it revealed that there were no significant differences between the mean values of root mean square of the tracking error of two groups (3.9 vs. 3.7 bpm, [Formula: see text]). Furthermore, the results of a second statistical hypothesis test showed that the proposed PID controller with novel synchronised biofeedback mechanism has better performance compared to a conventional PID controller with a fixed-rate biofeedback mechanism (Group 1: 3.9 vs. 5.0 bpm, Group 2: 3.7 vs. 4.4 bpm, [Formula: see text]).

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. To achieve online calibration, the model as well as the cost function are linearized at the beginning, and an online recursive method is then utilized to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is based on damped recursive least square estimation (DRLS), which can significantly reduce the calculation complexity comparing to nonlinear optimization method. In addition, the unknown parameters can be solved in a short time and the estimated parameters can remain stable during calibration. Experimentally, this method was tested by comparing the output results before and after calibration in different condition. It showed that the output, after calibrated by the proposed method, is more accurate with respect to raw output using default factory parameters.

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.g. recursive least squares (RLS) method) can avoid the occurrence of the so-called blowup phenomena. The calculated control signals are transmitted to the subjects employing a synchronized biofeedback mechanism. The proposed control and estimation scheme were experimentally verified using twelve healthy male subjects and the results demonstrated that the designed scheme is able to regulate the HR of the exercising subjects to a predetermined HR profile preventing overshooting in the HR responses.

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. recursive least squares (RLS) method) can avoid the occurrence of the so-called blowup phenomena. By incorporation of a weighting upon 1) parameter variation relative to a priori HR response knowledge, 2) one-step parameter variation, into the objective function, an estimation scheme is obtained that in the absence of exciting data can avoid blowup. The proposed estimation scheme were experimentally verified using eight healthy male subjects and the results demonstrated that the designed scheme is able to identify the HR response of the exercising subjects in a real-time manner. As HR response is naturally a time-varying dynamics, the proposed online modelling method is of importance for the HR regulation during exercises, using a feedback controller with a desirable level of performance.

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. The experimental design is carried out with a newly developed wearable health monitoring device and desired experimental results have been achieved.