Mood States Recognition of Rowing Athletes Based on Multi-Physiological Signals Using PSO-SVM

Athletes have various emotions before competition, and mood states have impact on the competi- tion results. Recognition of athletes’ mood states could help athletes to have better adjustment before competition, which is significant to competition achievements. In this paper, physiological signals of female rowing athletes in pre- and post-competition were collected. Based on the multi-physiological signals related to pre- and post-competition, such as heart rate and respiration rate, features were extracted which had been subtracted the emotion baseline. Then the particle swarm optimization (PSO) was adopted to optimize the feature selection from the feature set, and combined with the least squares support vector machine (LS-SVM) classifier. Positive mood states and negative mood states were classified by the LS-SVM with PSO feature optimization. The results showed that the classification accuracy by the LS-SVM algorithm combined with PSO and baseline subtraction was better than the condition without baseline subtraction. The combination can contribute to good classification of mood states of rowing athletes, and would be informative to psychological adjustment of athletes.

Physiological signal acquisition system based on wireless sensor networks

Based on wireless sensor networks, a physiological signal acquisition system is proposed. The system is used in classroom education in order to understand the physiological changes in the students. In the system,the biological electrical signal related to student attention and emotion states can be measured by electrocardiography signals. The bioelectrical signal is digitalized at a 200 Hz sampling rate and is transmitted by the ZigBee protocol. Simultaneously, the Bluetooth technology is also embedded in the nodes so as to meet the high sampling rate and the high-bandwidth transmission. The system can implement the monitoring tasks for 30 students, and the experimental results of using the system in the classroom are proposed. Finally, the applications of wireless sensor networks used in education is also discussed.

Breathable Electronic Skins for Daily Physiological Signal Monitoring

With the aging of society and the increase in people’s concern for personal health,long-term physiological signal monitoring in daily life is in demand.In recent years,electronic skin(e-skin)for daily health monitoring applications has achieved rapid development due to its advantages in high-quality physiological signals monitoring and suitability for system integrations.Among them,the breathable e-skin has developed rapidly in recent years because it adapts to the long-term and high-comfort wear requirements of monitoring physiological signals in daily life.In this review,the recent achievements of breathable e-skins for daily physiological monitoring are systematically introduced and discussed.By dividing them into breathable e-skin electrodes,breathable e-skin sensors,and breathable e-skin systems,we sort out their design ideas,manufacturing processes,performances,and applications and show their advantages in long-term physiological signal monitoring in daily life.In addition,the development directions and challenges of the breathable e-skin are discussed and prospected.

Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health care.Design considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human tissues.In addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and hemocompatibility.The wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease diagnosis.Lastly,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

Wearable Wireless Body Area Nodes for Remote Physiological Signal Monitoring System

Wearable remote health monitoring systems have gained significant prominence in the recent years due to their growth in technological advances. One form of the Wearable Physiological Monitoring System (WPMS) is the Wearable Body Area Networks (WBAN) used to monitor the health status of the wearer for long durations. The paper discusses a prototype WBAN based wearable physiological monitoring system to monitor physiological parameters such as Electrocardiogram (ECG) and Electroencephalogram (EEG) acquired using a textile electrode, Photoplethysmogram (PPG), Galvanic Skin Response (GSR), Blood Pressure derived from analysis of Pulse Transmit Time (PTT) and body temperature. The WBAN consists of three sensor nodes that are placed strategically to acquire the physiological signals and the sensor nodes communicate to a chest/wrist worn sink node also known as wearable data acquisition hardware. The sink node receives physiological data from the sensor nodes and is transmitted to a remote monitoring station. The remote monitoring station receives the raw data and it is processed to remove noises, such as power line interference, baseline wander and tremor in the signals and the information is extracted and displayed. The WBANs are designed using the ZigBee wireless communication modules to transmit and receive the data. At the remote monitoring station the physiological parameters such as heart rate, pulse rate, systolic, diastolic blood pressure, GSR and body temperature are continuously monitored from the wearer. The data acquired from the wearable monitoring system is statically validated using a qualified medical device on 34 subjects.

Detecting Human Mood from Physiological Signal and Data Usage

As the days go by, there are technologies that are being introduced everyday, whether it is a tiny music player iPod nano or a robot “Asimo” that runs 6 kilometers per hour. These technologies entertain, facilitate and make the day easier for the human being. It is not arguable anymore that the people need these technologies with the smart systems to lead their regular life smoothly. The smarter the system is;the more people like to use it. One major part of this smartness of the system depends on how well the system can interact with the person or the user. It is not a dream anymore that a system will be able to interact with a human just the way that one human interacts with another. To make that happen, it is obvious that the system must be intelligent enough to understand a human being. For example, if we need a Robot that can have a random conversation with a human, the system must recognize and understand the spoken word to reply the human. And the reply will be based on the current mood and behavior of the human. In this scenario, a human uses his senses to receive the inputs such as voice through the hearing senses, behavior and movement of the body parts, and facial expression through seeing sense from the speaking human. And it is now apparently possible to take such inputs for a system which can be stored as data;later it is possible to analyze the data using various algorithms and also to teach the system through Machine Learning algorithms. We will briefly discuss issues related to the relevance and the possible impact of research in the field of Artificial Intelligence, with special attention to the Computer Vision and Pattern Recognition, Natural Language Processing, Human Computer Interaction, Data Warehouse and Data Mining that is used to identify and analyze data like psychological signals, voice, conversation, geo location, and geo weather, etc. In our research, we have used heart rate that is a successful physiological signal to detect human mood and used smartphone usage data to train the system and detect mood more accurately than other methods.

Physiological signal processing in heart rate variability measurement:A focus on spectral analysis

Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under conditions of rest or work,the temporal distances of successive heartbeats are subject to fluctuations,thereby forming the basis of Heart Rate Variability(HRV).In normal conditions,the human is persistently exposed to highly changing and dynamic situational demands.With these demands in mind,HRV can,therefore,be considered as the human organism’s ability to cope with and adapt to continuous situational requirements,both physiologically and emotionally.Fast Fourier Transform(FFT)is used in various physiological signal processing,such as heart rate variability.FFT allows a spectral analysis of HRV and is great help in HRV analysis and interpretation.

RFID Positioning and Physiological Signals for Remote Medical Care

The safety of patients and the quality of medical care provided to them are vital for their wellbeing.This study establishes a set of RFID(Radio Fre-quency Identification)-based systems of patient care based on physiological sig-nals in the pursuit of a remote medical care system.The RFID-based positioning system allows medical staff to continuously observe the patient's health and location.The staff can thus respond to medical emergencies in time and appropriately care for the patient.When the COVID-19 pandemic broke out,the proposed system was used to provide timely information on the location and body temperature of patients who had been screened for the disease.The results of experiments and comparative analyses show that the proposed system is superior to competing systems in use.The use of remote monitoring technology makes user interface easier to provide high-quality medical services to remote areas with sparse populations,and enables better care of the elderly and patients with mobility issues.It can be found from the experiments of this research that the accuracy of the position sensor and the ability of package delivery are the best among the other related studies.The presentation of the graphical interface is also the most cordial among human-computer interaction and the operation is simple and clear.

Salt tolerance in rice:Physiological responses and molecular mechanisms

Crop yield loss due to soil salinization is an increasing threat to agriculture worldwide.Salt stress drastically affects the growth,development,and grain productivity of rice(Oryza sativa L.),and the improvement of rice tolerance to salt stress is a desirable approach for meeting increasing food demand.The main contributors to salt toxicity at a global scale are Na^(+)and Cl^(-)ions,which affect up to 50%of irrigated soils.Plant responses to salt stress occur at the organismic,cellular,and molecular levels and are pleiotropic,involving(1)maintenance of ionic homeostasis,(2)osmotic adjustment,(3)ROS scavenging,and(4)nutritional balance.In this review,we discuss recent research progress on these four aspects of plant physiological response,with particular attention to hormonal and gene expression regulation and salt tolerance signaling pathways in rice.The information summarized here will be useful for accelerating the breeding of salt-tolerant rice.

The Physiological and Molecular Responses of Arabidopsis thaliana to the Stress of Oxalic Acid

Many fungal phytopathogens can secrete oxalic acid （OA）, which is the crucial pathogenic determinant and plays important roles in pathogenicity and virulence of pathogen during infection process. However, how plants respond to OA stress still needs further characterization. In this study, we observed the physiological and molecular responses of Arabidopsis thaliana to OA stress. The leaves of 6-wk-old A. thaliana were sprayed with OA and distilled water respectively, and 0, 2, 4, 8, 12, and 24 h later, the leaves were collected and the contents of MDA, H2O2, and GSH, and the activities of CAT, SOD, and POD were determined and the expressions of PR1 and PDF1.2 were also studied. Under the stress of 30 mmol L-1 OA, SOD activity was first enhanced to reduce the accumulation of O2.-. But immediately, POD, CAT, and GSH all decreased extremely resulting in the accumulation of H2O2, and the MDA content increased 24 h later. GSH activity was enhanced significantly at 24 h after OA used. However, H2O2 wasn＇t eliminated at the same time, suggesting that the activity inhibitions of POD and CAT might be the reasons that caused Arabidopsis cells＇ impairment under OA stress. RT-PCR results indicated that PDF1.2, a marker gene of the JA/ET signaling was significantly induced; PR1, an indicator gene in SA signaling, was slighlty induced from 8 to 12 h after OA stress. In conclusion, Arabidopsis may recruit metabolism of reactive oxygen, both JA/ET and SA signaling pathways to respond to OA stress. These results will facilitate our further understanding the mechanisms of plant response to OA and OA-dependent fungal infection.

Emotions States Recognition Based on Physiological Parameters by Employing of Fuzzy-Adaptive Resonance Theory

This paper is an investigation on negative emotions states recognition by employing of Fuzzy Adaptive Resonance Theory (Fuzzy-ART) considering the changes in activities of autonomic nervous system (ANS). Specific psychological experiments were designed to induce appropriate physiological responses on individuals in order to acquire a suitable database for training, validating and testing the proposed procedure. In this research, the three physiological applied signals are Galvanic Skin Response (GSR), Heart Rate (HR) and Respiration Rate (RR). The first experiment which is named Shock was designed to determine a criterion for the change of physiological signals of each individual. In the second one, a combination of two sets of questions has been asked from the subjects to induce their emotions. Finally, Physiological responses were analyzed by Fuzzy-ART to recognize which question excites the negative emotions. Detecting negative emotions from neutral is obtained with total accuracy of 94%.

Physiological and pharmacological functions of G protein coupled receptor 124:A review

G protein-coupled receptors(GPCRs)are the largest protein superfamily in the body,expressed in various tissues and organs,and are currently one of the most important clinical drug targets.Recently,a class of GPCRs without endogenous ligands(orphan GPCRs)have been discovered.They exhibit different physiological functions in the body and act extensively on the cardiovascular and cerebrovascular systems.Among them,G protein-coupled receptor 124(GPR124)is an orphaned member of the G protein coupled receptor adhesion family that has attracted much attention.It plays a key role in promoting cerebral angiogenesis and maintaining the stability of the blood-brain barrier.It also associated with cardiovascular and cerebrovascular diseases such as cerebral ischemia and atherosclerosis.However,the role of GPR124 in these diseases,the associated signaling pathways,and possible drug intervention targets are still unclear.This article summarizes the physiological effects,pharmacological effects and related signal pathways of GPR124 published in the field of cardiovascular and cerebrovascular diseases published in recent years,in order to provide a reference for the study of the role of GPR124 in the occurrence and development of diseases.

Flexible bioelectronics for physiological signals sensing and disease treatment

Flexible bioelectronics,including wearable and implantable electronics,have revolutionized the way of human-machine interaction due to the fact that they can provide natural and seamless interactions with humans and keep stable and durable at strained states.As sensor elements or biomimetic actuators,flexible bioelectronics can dynamically sense and monitor physiological signals,reveal real-time physical health information and provide timely precise stimulations or treatments.Thus,the flexible bioelectronics are playing increasingly important roles in human-health monitoring and disease treatment,which will significantly change the future of healthcare as well as our relationships with electronics.This review summarizes recent major progress in the development of flexible substrates or encapsulation materials,sensors,circuits and energy-autonomous powers toward digital healthcare monitoring,emphasizing its role in biomedical applications in vivo and problems in practical applications.A future perspective into the challenges and opportunities in emerging flexible bioelectronics designs for the next-generation healthcare monitoring systems is also presented.

Flexible Piezoresistive Sensors based on Conducting Polymer-coated Fabric Applied to Human Physiological Signals Monitoring

This paper describes a flexible pressure sensor based on polypyrrole(PPy)-Cotton composites,in which PPy is grown on cellulose fibers of cotton pads via an in situ vapor growth method,which is beneficial to the homogeneity of the composites.The resulting devices exhibits rapid response and recovery speed,the response and recovery times are 220 ms and 240 ms,respectively.The optimal PPy-Cotton Pads(PCPs)sensor shows low detection limit,which is about 50 Pa.At the same time,it exhibits excellent durability in the measurement of repeated loading-unloading pressure over 1000 cycles.The resultant sensor can be attached on different positions of body and applied to recording physiological signals,such as wrist pulse,vocal cord vibration,respiration and eyes blinking.Finally,a 4×4 pressure sensor array shows that the PCPs sensor has capability in pressure distribution detection and represents great potential in the fields of wearable electronics and biomedical devices.

Use of multimodal physiological signals to explore pilots’cognitive behaviour during flight strike task performance

This study explored the use of multi-physiological signals and simultaneously recorded high-density electroencephalography(EEG),electrocardiogram(ECG),and eye movements to better understand pilots’cognitive behaviour during flight simulator manoeuvres.Multimodal physiological signals were collected from 12 experienced pilots with international aviation qualifications under the wide-angle and impressive vision simulation.The data collection spanned two flight strike missions,each with three mission intensities,resulting in a data set of EEG,ECG,and eye movement signals from six subtasks.The multimodal data were analysed using signal processing methods.The results indicated that,when the flight missions were performed,the pilots’physiological characteristics exhibited rhythmic changes in the power spectrum ofθwaves in the EEG,r-MSSD in the ECG,and average gaze duration.Furthermore,the pilots’physiological signals were more sensitive during the target mission than during the empty target mission.The results also showed correlations between different physiological characteristics.We showed that specific multimodal features are useful for advancing neuroscience research into pilots’cognitive behaviour and processes related to brain activity,psychological rhythms,and eye movement.

Modeling of Biological Tissues Response to Radio Frequency （RF）： Towards Remote Sensing of Electrocardiography Signal

Premature newborns are at high risk of developing infections, so they require continuous monitoring of vital parameters for long periods of time, until they approximately reach the pregnancy due date. ECG （electrocardiography） is one of the most widely used method for evaluating the structure-function relationship of the heart in health and in sickness. Due to incomplete skin development, premature newborns have some special requirements to the ECG monitoring electrodes. Contact ECG monitoring adversely affects the health and comfort of the newborns. The goal of this study is to determine the feasibility of using RF （radio frequency） in ECG signal remote sensing. This requires studying the interaction mechanisms between RF fields and biological tissues The ECG current propagated from the heart through the skin has an effect on the permittivity of the skin which is frequency dependent. Thus, the feasibility of detecting the change of the relative permittivity in the presence of ECG signal is also discussed. The RF biological tissues response is simulated using MATLAB software in preparation for experimental validation.

基于GA-BLS方法的手势识别研究

为进一步提升人机交互领域中手势识别的精度和速度,探究肌肉疲劳对手势识别的影响规律,提出了改进的GA-BLS方法,利用遗传算法(genetic algorithms,GA)优化宽度学习(broad learning system,BLS)模型参数,并使用弹性网络回归改进传统的BLS模型。利用所提模型对8种手势下的A型超声信号和肌电信号进行手势识别分析,并与SVM、KNN、RF、LDA等方法进行对比,以验证所研究方法的有效性;将长时间段下的A型超声信号和肌电信号切分成4个数据段,发现随着肌肉疲劳程度的增加,手势识别的准确率均呈现出明显下降的趋势,而且A型超声信号相较于肌电信号具有更好的抗疲劳特性。

基于嗅觉和听觉刺激的驾驶员疲劳唤醒方法研究

针对驾驶员在疲劳状态下易引发交通事故的问题,提出一种基于嗅觉和听觉刺激的驾驶员疲劳唤醒方法,研究以薄荷气体和阿尔法脑波音乐作为刺激源的唤醒效果,采用主观疲劳问卷和心电(ECG)、脉搏(PPG)以及呼吸(RESP)生理信号作为疲劳唤醒有效性判断指标。结果表明,两种方案的心电、脉搏以及呼吸生理数据均显示能有效干预驾驶疲劳,与主观疲劳问卷调查结果相一致,验证了基于嗅觉和听觉刺激的驾驶员疲劳唤醒方法的有效性,且基于听觉刺激的唤醒方案唤醒效果更佳。

Nacre-inspired MXene-based film for highly sensitive piezoresistive sensing over a broad sensing range

As the main component of wearable electronic equipment,flexible pressure sensors have attracted wide attention due to their excellent sensitivity and their promise with respect to applications in health monitoring,electronic skin,and human-computer interactions.However,it remains a significant challenge to achieve epidermal sensing over a wide sensing range,with short response/recovery time and featuring seamless conformability to the skin simultaneously.This is critical since the capture of minute electrophysiological signals is important for health care applications.In this paper,we report the preparation of a nacre-like MXene/sodium carboxymethyl cellulose(CMC)nanocomposite film with a“brick-and-mortar”interior structure using a vacuum-induced self-assembly strategy.The synergistic behavior of the MXene“brick”and flexible CMC“mortar”contributes to attenuating interlamellar self-stacking and creates numerous variable conductive pathways on the sensing film.This resulted in a high sensitivity over a broad pressure range(i.e.,0.03-22.37 kPa:162.13 kPa^(-1);22.37-135.71 kPa:127.88 kPa^(-1);135.71-286.49 kPa:100.58 kPa^(-1)).This sensor also has a low detection limit(0.85 Pa),short response/recovery time(8.58 ms/34.34 ms),and good stability(2000 cycles).Furthermore,we deployed pressure sensors to distinguish among tiny particles,various physiological signals of the human body,space arrays,robot motion monitoring,and other related applications to demonstrate their feasibility for a variety of health and motion monitoring use cases.