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Über dieses Buch

This book presents the post-proceedings, including all revised versions of the accepted papers, of the 2017 European Alliance for Innovation (EAI) International Conference on Body Area Networks (BodyNets 2017). The goal of BodyNets 2017 was to provide a world-leading and unique forum, bringing together researchers and practitioners from diverse disciplines to plan, analyze, design, build, deploy and experiment with/on Body Area Networks (BANs).

Inhaltsverzeichnis

Frontmatter

Characterization and Identification of Driver Distraction During Naturalistic Driving: An Analysis of ECG Dynamics

One of the most contributing factors to the accidents on the roadways is distracted driving. While in-vehicle, driver may get distracted by variety of ways such as talking on the cellphone, conversing with the accompanying passengers, texting while driving, etc. In order to reduce potential chances of road-accidents, it is highly essential to characterize and identify distracted situations in real-time. In this paper, we investigate Electrocardiogram (ECG) signals as the physiological measure to characterize driver distraction. We aim to provide an empirical guideline for accurate and in real-time analysis irrespective of the body physic. ECG-based driver distraction identification has significant advantages in practice such as being easy to capture, minimally intrusive, and reliable in biometric patterns. ECG dynamics encompass multiple descriptors that we examine in this investigation for efficient characterization of driver state toward real-time identification of distracted driving. In this effort, six drivers were actively participated in our naturalistic driving experiments, where the distraction is introduced by the cellphone conversation and the conversation with the passenger. Our study mainly focuses on the efficient characterization of distraction by localizing R-R interval based on temporal features as well as spectral features. In addition to this, we further investigated the real-time predictive ability of the extracted features through state of the art predictive algorithms. Our experimental results demonstrated ∼83% average predictive accuracy of driver distraction identification in near real-time.

Shantanu V. Deshmukh, Omid Dehzangi

Wearable Galvanic Skin Response for Characterization and Identification of Distraction During Naturalistic Driving

Fatalities due to road accidents are mainly caused by distracted driving. Driving demands continuous attention of the driver. Certain levels of distraction while driving can cause the driver lose his/her attention which might lead to a fatal accident. Thus, early detection of distraction will help reduce the number of accidents. Several researches have been conducted for automatic detection of driver distraction. Many previous approaches have employed camera based techniques. However, these methods might detect the distraction rather late to warn the drivers. Although neurophysiological signals using Electroencephalography (EEG) have shown to be another reliable indicator of distraction, EEG signals are very complex and the technology is intrusive to the drivers, which creates serious doubt for its implementation. In this study we investigate Galvanic Skin Responses (GSR) using a wrist band wearable and conduct an empirical characterization of driver GSR signals during a naturalistic driving experiment. We explored time and frequency domain to extract relevant features to capture the changes/patterns at the physiological level. Due to the fact that feature extraction is a manual process and to normalize the feature space toward the identification task, we then transform the feature space using linear discriminant dimensionality reduction to discover discriminative bases of the GSR multivariate feature space that identify distraction. That would eliminate both the computational complexity and the redundancies in the manually generated feature space. Due to multi-class nature of the identification task, there might be biases between the distracted and non-distracted categories that can bias the estimation of between- and within-class scatter matrices. Therefore, we incorporated a class dependent weight to calculate the within class scatter matrices. The proposed weight aims to increase the flexibility of the discriminative bases vectors to capture the factors that focus on eliminating the overlap between distracted versus non-distracted in the generalization phase. Our experimental results demonstrated high cross validation accuracies of distraction identification using GSR signals (i.e. 85.19%). Conducting dimensionality reduction using LDA resulted in slight improvement in accuracy (i.e. 85.94%) using only two discriminant bases. The generalization accuracy was further improved by applying our proposed weighting mechanism (i.e. 88.92%).

Omid Dehzangi, Vikas Rajendra

A Wearable Multi-sensor IoT Network System for Environmental Monitoring

People spend more than 90% of their time indoor in Australia. Poor indoor air quality can cause severe health problems to individuals. It is necessary to develop a reliable and wearable systems for environmental monitoring. This chapter presents a low-power wearable sensor node for environmental Internet of Things (IoT) applications, forming wireless sensor network (WSN) based on XBee. Environmental data are monitored by the wearable sensor node and then transmitted to a remote cloud server via WSN. The data are displayed to authorized users through a web-based application located in cloud server. The experimental results indicate that the presented wearable sensor network system is able to monitor environmental conditions reliably.

Fan Wu, Christoph Rüdiger, Jean-Michel Redouté, Mehmet Rasit Yuce

Fabric Sensor Array Monitoring Pressure Distribution

To improve the sensing performance of fabric sensor array monitoring pressure distribution, the structure of fabric sensor array was designed and optimized. The fabric sensor array was fabricated by seamlessly laminating multi-layers clothing fabric, and the optimized sensor array can sense both the size and position of the distributed dynamic forces. The performance of the designed fabric sensor array was evaluated. When the force is 5–25 N, the fabric sensor array monitoring pressure distribution has highly sensitive to the applied force. In terms of the material and construction of fabric sensor array, the mesh size of isolating layer determines the sensitivity and response range.

Jiyong Hu, Hele Zhang, Yuanyuan Gu, Yinda Zhu, Xuyuan Guo, Xudong Yang

Automatic EEG Blink Detection Using Dynamic Time Warping Score Clustering

The electroencephalograph (EEG) is a powerful tool, involving multiple electrodes placed on the scalp, with the intention of measuring brain activity through the scalp. One significant application for EEG is to analyze the mental state of a subject. One of the challenges involved in using the EEG for identifying mental state in practical settings is ocular artifacts e.g. eye blinking. Eye blinks cause high amplitude noise in electroencephalograms (EEGs), the noise from these blinks can cause interference in several very important frequency bands and confuse predictive modeling e.g. introduce false positives. Prior works have employed independent component analysis (ICA) to decompose the noisy EEG signals into constituting sources and identify the eye blink sources. However, ICA requires off-line signal processing and is not suitable for online applications. More recently, time domain autoregressive features were used to model eye blink related segments in the recorded EEG data. While the autoregressive method showed high identification accuracy in isolated short trials, the goal of this work is to create a more advanced system capable of identifying and filtering blink noise in continuous trials during long and complex tasks. The proposed method detailed in this paper conducts automatic detection of eye blink noise using dynamic time warping (DTW) score clustering during wearable EEG-based cognitive workload assessment tests. The proposed eye blink detection system only uses EEG data for training and identification and does not require electrooculogram (EOG) data, which is particularly important for wearable systems. Our experimental results demonstrated the effectiveness of the proposed blink detection methodology by achieving 96.42% average accuracy of blink detection in the recorded EEG dynamics during a continuous workload assessment task.

Omid Dehzangi, Alexander Melville, Mojtaba Taherisadr

Continuous Blood Pressure Estimation Using PPG and ECG Signal

Continuous blood pressure monitor can detect the potential risk of cardiovascular disease and provide a gold standard for clinical diagnosis. The features extracted from photoplethysmography (PPG) and electrocardiogram (ECG) signals can reflect the dynamics of cardiovascular system. In this paper, 39 features are extracted from PPG and ECG signals and 10 features are chosen by analyzing their correlations with blood pressure. Several machine learning algorithms are used to predict the continuous and cuff-less estimation of the diastolic blood pressure and systolic blood pressure. The results shows that compared with linear regression and support vector regression methods, the artificial neural network optimized by genetic algorithm gives a better accuracy for 1 h prediction under Advancement of Medical Instrumentation and the British Hypertension Society standard.

Bo Wang, Zhipei Huang, Jiankang Wu, Zhongdi Liu, Yuanyuan Liu, Pengjie Zhang

The Prospect and Analysis of Nanogenerator for Wearable Devices

With the emergence of wearable devices such as Google glasses, I Watch, Active trackers and etc., people are becoming increasingly interested in wearable device and an increasing number of electronics and functionalized components are being applied to wearable devices. Even though wearable devices do not consume too much energy, the requirement for sustainability still brings a big challenge for power supplying. In fact, the battery life of most wearable devices cannot satisfy the users’ demand. On the other hand, because of the devices’ characteristic of being “wearable”, there is much higher demand for safety, life span, pollution and comfort in power supply components. Therefore, how to provide continuous and environmentally-friendly power supply for wearable devices becomes a popular research topic. In 2012, when the Triboelectric Nano generator (TENG) was first introduced, it drew everyone’s attention because of its advantages of lightweight, safety, cleanness and sustainability. The possibility of using Nano generator technology as a self-driven power supply for wearable devices will be discussed in this paper.

Jing-Yan Yu, Li Liu

EEG-Based Driver Distraction Detection via Game-Theoretic-Based Channel Selection

In recent years, there has been much effort to estimate drivers’ state with the goal of improving their driving behavior and preventing vehicle crashes in the first place. Physiological based detection has shown to be the most direct method of measuring driver state among which, electroencephalogram (EEG) is the most comprehensive method. However, EEG-based driver state detection faces the challenge of computational complexity of data mining algorithms given high density and resolution of EEG signals recorded from multiple channels. On the other hand, in order to early detection and prevention of driver critical states real-time responsiveness of the monitoring system is necessary. This challenges can be tackled by localizing the regional impact by selecting a small subset of coherent channels and reducing the processing load on all channels. In this paper, we present and investigate a Game-Theoretic-Based approach for EEG channel selection, in order to localize the most efficient sub-set of channels in addition to maximizing the driver distraction detection accuracy. In this way, we apply game theory based channel selection algorithm based on the utility measure, Shapley value, in exact to estimate overall usefulness of each EEG channel. We then consider the combination of channels and evaluate their performance. Empirical comparison of best combination of channels, best ordered channel based on Shapley value with another existing feature selection method shows that the sub-set of channels leads to the best detection performance in terms of accuracy (90.12% accuracy).

Mojtaba Taherisadr, Omid Dehzangi

EEG Based Driver Inattention Identification via Feature Profiling and Dimensionality Reduction

More than 90% of the persistently increasing traffic fatalities is related to human choice/error. Monitoring driver attention has a direct effect on decreasing injury/fatality rates. In recent years, there has been much effort to estimate drivers’ state with the goal of improving their driving behavior and preventing vehicle crashes in the first place. Physiological based detection has shown to be the most direct method of measuring driver state among which, electroencephalogram (EEG) is the most comprehensive method. EEGs are recorded from multiple channels that are processed separately. However, contribution of a fairly large number of the channels might be minimal to the target application. The computational load and the redundancy induced by those channels can hurt the identification performance. In this study, we propose an EEG-based systematic methodology for the assessment of driver state of inattention. Our proposed framework includes three major modules: (1) We first characterize each EEG channel rigorously via extraction of various categories of descriptors as features, (2) we then capture the contribution of each channel toward the identification task via channel specific feature dimensionality reduction, (3) we then conduct channel selection in order to find key brain regions of impact. Eight subjects participated in our naturalistic driving study. Our proposed method resulted in the accuracy of 98.99 ± 1.2% inattention identification accuracy. We also discovered that the first and second best channels are consistently selected from frontal and parietal regions for participating subjects.

Omid Dehzangi, Mojtaba Taherisadr

Context-Aware Sensor Solution for Remote Monitoring of Adolescent Idiopathic Scoliosis Brace Treatment

The medical condition of Scoliosis occurs when an individual’s spine develops curvature in adolescents. A Brace treatment is used to control the lateral curvature of the spine in scoliosis. However, brace treatment is a long and inconvenient process that demands strict compliance by the patients. In this work, we designed a wearable sensor solution to monitor the brace treatment compliance. The hardware is embedded into the patient’s brace. The custom designed hardware consists of a sensor board, multiple sensors. The force sensor collects the force being exerted on the patient’s back, while the motion sensor generates cues to determine the patient’s activities and context. We aim to evaluate monitoring of the effectiveness of the brace treatment pervasively based on fusion of continuous force and motion recordings. The proposed method evaluates the duration of brace wear through the process of segmentation and calculates the level of tightness of brace by estimating the baseline force per segment in the presence of different activities including sitting, standing, climbing, walking, running and lying. We investigated an experimental scenario in which, the patient performs a series of pre-defined activities at home during day long segments of brace wear, during pervasive sensor data recordings. The experimental results demonstrated that we achieved an overall accuracy of a 96.1% for unsupervised activity detection. Our trained model estimated a reduction in the level of tightness of brace by 30% during a period of 2 weeks while the compliance of brace treatment gradually increased.

Omid Dehzangi, Bhavani Anantapur Bache, Omar Iftikhar, Jeffrey Wensman, Ying Li

Gait Analysis for Physical Rehabilitation via Body-Worn Sensors and Multi-information Fusion

How to effectively use wearable sensors for medical rehabilitation is an interdisciplinary research hotspot of control subjects and biomedical engineering. This paper intends to integrate accelerometer, gyroscope and magnetometer to build a low-cost, intelligent and lightweight wearable human gait analysis platform. On account of complexity and polytopes of walking motion characteristics, the key is to solve the existing robustness and adaptability problems of current gait analysis algorithm. This project is starting from the sensor physical properties and human physiology structure, aiming to establish lower limb kinematics model constraint, and solving the applicability problem of the traditional zero velocity update algorithm. Digital filter and error correction of gait parameters could be done with multi-level data fusion algorithm. Preliminary clinical gait experiments results indicated the proposed method has great potential as an auxiliary for medical rehabilitation. The ultimate target is to realize auxiliary diagnosis and exercise rehabilitation plan formulation for patients with abnormal gait.

Sen Qiu, Zhelong Wang, Hongyu Zhao, Long Liu, Jiaxin Wang, Jie Li

An Embedded Risk Prediction System for Wheelchair Safety Driving

Development of intelligent wheelchairs can increase mobility and independence of impaired individuals. As there exist dangerous driving risks such as improper driving postures, moving too fast or on rough road, it is useful to monitor the wheelchair user’s driving conditions and, particularly, predict potential driving risks. This paper proposes a risk prediction system for the wheelchair safety driving. A novel designed smart cushion is used to evaluate dangerous drivings risks. Our cushion is able to better combine the pressure sensors and accelerometer and it can detect sitting postures, wheelchair accelerations, and terrain conditions. In addition, we propose a prediction system to monitor the wheelchair driving status. Features are extracted and a fuzzy inference system is used to quantify the dangerous driving risks. Warnings or intervention control strategies will be trigged to increase wheelchair driving safety.

Congcong Ma, Wenfeng Li, Qimeng Li, Raffaele Gravina, Yi Yang, Giancarlo Fortino

A Wearable, Low-Power, Real-Time ECG Monitor for Smart T-shirt and IoT Healthcare Applications

A wearable health monitoring system combined with Internet of Things (IoT) is going to be a promising alternative to the conventional healthcare systems. In this work, a small, flexible and wearable real-time electrocardiograph (ECG) monitoring system integrated on a T-shirt is proposed. It uses an off-the-shelf biopotential analog front end (AFE) chip, AD8232, to collect subjects’ ECG data with satisfactory quality. The collected ECG data are transmitted through Bluetooth low energy (BLE) to an end device for real-time display. A PC graphical user interface (GUI) and a smartphone application are designed for indoor and outdoor real-time visualisation respectively. The power consumption of the proposed wearable ECG monitoring system can be as low as 5.2 mW. Powered by a 240 mAh rechargeable battery, it can operate for more than 110 h continuously. To prolong the lifetime of the battery, a flexible solar energy harvester is also adopted within this system.

Taiyang Wu, Jean-Michel Redouté, Mehmet Yuce

JMMM: A Mobility Model for WBANs Based on Human Joint Movements

In wireless body area networks (WBAN), the sensors are usually attached on or implanted in the human body to monitor different vital signals. During routine activities, there is high mobility in WBANs, which results in frequent changes of the network topology. An accurate mobility model plays a vital role in protocol simulation and performance evaluation for WBANs. In this paper, we propose JMMM, a mobility model for WBANs according to the movement of human joints. Via mimicking the real motions of the human body, the proposed model can model the movement of any nodes in any place on body more accurately, and the changes in distance between different nodes are more realistic compared to previous models, which is of great importance in accurate simulation for WBANs. Moreover, the model is configurable, thus it is usable for a large variety of applications.

Chengjie Guan, Bin Liu, Zhiqiang Liu, Yufei Zhang, Xiaoyu Zhang

A Noninvasive Continuous Fetal Heart Rate Monitoring System for Mobile Healthcare Based on Fetal Phonocardiography

Although the noninvasive continuous fetal heart rate (FHR) monitor is often recommended, the Doppler Ultrasonographic Cardiotocography (CTG) is improper for long-term monitor due to the less safety and the requirement of professional operation skill. In this paper, we design a noninvasive, continuous and real-time FHR monitoring system based on fetal phonocardiography by stationary wavelet denoising and cyclostationary process. Good agreement with CTG is obtained by Bland Altman analysis. Besides, quantitative results show that the FHR has an average accuracy of 97% compared with CTG on clinical data sets. The proposed system provides an alternative for CTG.

Pengjie Zhang, Shiwei Ye, Zhipei Huang, Dina Jiaerken, Shuxia Zhao, Lingyan Zhang, Jiankang Wu

Medical Quality of Service Optimization over Joint Body Sensor Networks and Internet of Multimedia Things

This paper proposes novel Rate Control Video Transmission Algorithm (RCVTA) to optimize medical quality of service (m-QoS) in terms of network metrics such as, standard deviation (Std dev), throughput, peak-to-mean ratio (PMR), delay, average delay, jitter and average jitter during transmission of high-definition (HD) video stream named ‘Tracking and Retargeting in GI endoscopy’ over joint Internet of Multimedia Things (IoMT) and Body Sensor Networks (BSNs). Experimental results reveal that m-QoS is optimized with workahead transmission over joint BSN and IoMT networks for Tele-surgery.

Ali Hassan Sodhro, Aicha Sekhari, Yacine Ouzrout, Gul Hassan Sodhro, Noman Zahid, Sandeep Pirbhulal, M. Irfan Younas

An Encryption Method for BAN Using the Channel Characteristics

The protection of information security is a very important technique requirement in Body area networks (BANs). According to the limitation of energy, the traditional encryption methods which adopt the complex algorithm and the large consumption are not suitable for BANs. This paper proposes a new encryption method based on the channel model of BANs, which adopts the path loss from the BAN systems to form the initial key, utilizes the LFSR (Linear Feedback Shift Register) circuit to generate the key stream, and then encrypts the data in the coordinator of the BAN system. This new encryption method has the advantages of low energy consumption, simple hardware implementation, and dynamic key updating.

Liangguang Peng, Jinzhao Lin, Tong Bai, Yu Pang, Guoquan Li, Huiquan Wang, Xiaoming Jiang, Junchao Wang, Zeljko Zilic

Experimental Performance Evaluation of BLE 4 Versus BLE 5 in Indoors and Outdoors Scenarios

This paper focus on an experimental performance evaluation of the recently published Bluetooth Low Energy (BLE) 5 technology. Measurements have been conducted both in indoors and outdoors scenarios. Performance of BLE 5 is compared to a previous release of BLE 4 which is currently the most used technology in commercial wireless healthcare and medical devices. This new improved BLE version may continue fostering the success of BLE use in those application scenarios as well as enable novel Internet of Things solutions. The main goal of this work was to evaluate, experimentally, the communications range and throughput performance of BLE 5 coded version which claims to provide fourfold improvement to the previous version of BLE. Measurement results obtained using the Nordic Semiconductor nRF52840 chipset are reported for indoor and outdoor cases relevant to healthcare and medical scenarios. Results show the practical communications range and throughput of the BLE 5 coded version, giving insight about the possible application space improvements for BLE technology. Specifically, our measurements showed that BLE 5 coded mode provides approximately 9 dB radio link budget gain compared to BLE 4, which leads to more than twofold communications range improvement in line-of-sight outdoor scenario and 10–20% improvement in non-line-of-sight indoor scenario.

Heikki Karvonen, Carlos Pomalaza-Ráez, Konstantin Mikhaylov, Matti Hämäläinen, Jari Iinatti

Electrode Impedance Modeling for Channel Characterization for Intra-body Communication

This paper discusses techniques for modeling the electrode/human contact impedances for Intra-body communication applications. Factors that affect the electrode impedance are considered and tuned in order to study their impact on the channel model (gain/attenuation profile). Finally, an explanation is provided for the relation between the different basic impedances and blocks that are considered in the channel model, and the sensitivity of the channel gain to the variation in such parameters.

Ahmed E. Khorshid, Ibrahim N. Alquaydheb, Ahmed M. Eltawil

Analysis and Estimation of Intra-body Communications Path Loss for Galvanic Coupling

The desire to have ultra-compact, low power patient monitoring techniques that include intercommunicating wearable and implanted sensors/actuators encourages researchers to develop new communication methods that can replace current Radio Frequency (RF) wireless communication links. RF links require power and area hungry analog circuitry that limits the usability of such systems. This paper evaluates different techniques for Intra-body communication (IBC) where the signal is coupled galvanically to the human tissue. Finite element method (FEM) technique is utilized to determine the path loss of the human channel (human arm model) and to examine the current density distribution in human tissues using both a full and a reduced order model. In addition, we investigate the effect of bone fracture internal fixation implant effect on the channel parameters.

Ibrahim N. Alquaydheb, Ahmed E. Khorshid, Ahmed M. Eltawil

An Improved Mathematical Model for the Autonomic Regulation of Cardiovascular System

The activity of the autonomic nervous system is hardly measurable. This study presents an improved mathematical model to estimate the baroreceptor nerve firing rate, the efferent parasympathetic and sympathetic response in a scenario of postural change from sitting to standing, based on observed blood pressure and heart rate changes. An optimization step is then applied to find the model parameters best fitting to observed cardiac data of healthy people and hypertensive patients using unbiased estimation and Nelder-Mead method. The experimental results on 59 subjects have shown that the improved model can describe autonomic regulation mechanism well and the estimated system parameters have clear clinical meaning.

Yuanyuan Liu, Yingfei Sun, Zhipei Huang, Yu Meng, Jiankang Wu, Xinxia Cai

Design of Fall Test System Based on Arduino 101

This paper designs a fall detection system based on Arduino 101. It is mainly composed of NNs (Neural Networks) and IMU (Inertial measurement unit). It is used to detect if an oldman falls down. When an old man falls, It can call the police to help the man get help in time. The core chip of Arduino 101 is the Intel Curie module. Intel Curie module microprocessor is Intel x86 Quark SE. It also carries GPRS wireless communication and GPS satellite positioning module. It analyzes and studies the characteristic parameters of the old man when he falls and does daily activities. It mainly uses the RBF (Radial Basis Function) algorithm to identify if a fall occurs. Experimental results show that: the system is able to identify most of the motion states correctly, with low reporting and false alarm rate. And it can quickly distinguish between daily activities and falls. For the old man, the detection accuracy rate can reach 95.5%. It has a high recognition rate, reliability and stability.

Nan Wang, Yaxia Liu

Data Reliability-Aware and Cloud-Assisted Software Infrastructure for Body Area Networks

Cloud-assisted body area networks have been the focus of researchers in past years as a response to the development of robust wireless body area networks (WBANs). While software such as Signal Processing in Node Environment (SPINE) provide Application Programming Interfaces (APIs) to manage heterogeneous biomedical sensor networks, others have focused on data analysis within networks, laying the groundwork for a scalable cloud-assisted infrastructure. However, recent work in cloud-assisted architectures have revealed several issues, specifically pertaining to applications in the biomedical field. Data-reliability and context aware adaptations are paramount to the success of biomedical applications, due to the field’s data quality needs when seeking in-depth analyses of the data sets. In addition, the cloud server must have a way to organize heterogeneous biomedical body sensor data and perform different types of biomedical body sensor research. The software infrastructure presented in this paper proposes several feedback mechanisms built off of dynamic variables within the system including data importance, data quality and network layout in order to provide researchers an optimal quality of service. The implementation of a domain specific language (DSL) will enable diverse biomedical data processing operations. Furthermore, a robust set of APIs will give researchers the ability to build flexible and unique biomedical applications.

Joseph Reeves, Carlos Moreno, Ming Li, Chengyu Hu, B. Prabhakaran

Genetic-Algorithm-Based Feature-Selection Technique for Fall Detection Using Multi-placement Wearable Sensors

For a machine-learning-based fall-detection approach using wearable sensors, having a high number of features can not only cause a reduction in the detection rate because of irrelevant features, but it can also cause a high computational cost. Therefore, the number of features needs to be reduced through a feature-selection technique. However, current studies in fall-detection only consider features that can give an optimum detection rate without considering their computational cost. Having features with a high computational cost on wearable devices can cause their battery to drain fast. This paper presents a genetic-algorithm-based feature-selection technique that can search for a subset of low-computational-cost features from different sensor placements, where those features can give a relatively good detection rate in terms of F-score. The experimental results show that our technique is able to select a subset of low-computational-cost features that can achieve up to 97.7% of F-score on average. Compared to the SelectKBest and Recursive Feature Elimination (RFE) techniques (a filter and an embedded feature-selection technique, respectively), our approach is able to select features that can give a comparable F-score and significantly lower computational cost.

I Putu Edy Suardiyana Putra, Rein Vesilo

Link-Level Performance of FM-UWB in the Interfered IEEE 802.15.6 Channel

This paper provides simulation results of the frequency modulated ultra wideband (FM-UWB) system with channel coding in an interfered IEEE 802.15.6 channel. The paper discusses a FM-UWB receiver structure and presents the developed simulator model. The receiver applies a delay-line modulator followed by an amplitude modulation projection detection. A channel model used is the IEEE 802.15.6 channel model 3 for an on-body link. An interference is modeled as a colored Gaussian noise presenting an in-band IEEE 802.15.4 interferer. The simulation results show that the FM-UWB system can tolerate as low as −6 dB signal-to-interference power ratios (SIR) in the studied scenarios.

Harri Viittala, Matti Hämäläinen, Jari Iinatti

Impedance Characteristics of the Skin-Electrode Interface of Dry Textile Electrodes for Wearable Electrocardiogram

Long-term dynamic Electrocardiogram (ECG) monitoring is considered as one of the main methods of preventing heart diseases. Ag/AgCl wet electrodes, although used clinically, are not suitable for long-time wearing. Dry textile electrodes, however, have won much attention for surmounting these drawbacks. This essay explains the impedance characteristics of the skin-electrode interface of wearable dry textile electrodes for measuring ECG. Specifically, through analyzing the characteristics of dry textile electrodes, the skin-electrode interface equivalent circuit models were built, the textile electrodes were made and the electrochemical impedance spectroscopy (EIS) for the skin-electrode interface was measured. Finally, the influence of each parameter to the interface was assessed. The research illustrated that interface of dry textile electrodes were more complicated than that of standard Ag/AgCl electrodes. The interface impedance |Z| and the interface phase were relevant to the signal frequency and the key of descending the interface impedance was to lower the polarization resistance. The textile electrodes have the Constant Phase Angle Element (CPE) behavior due to the dispersion effect of the time constant within the Frequency of ECG measuring.

Fan Xiong, Dongyi Chen, Zhenghao Chen, Chen Jin, Shumei Dai

Compact Antipodal Vivaldi Antennas for Body Area Communication

In this paper, compact on-body antipodal Vivaldi antennas (AVAs) are proposed for body area network (BAN), which work at the lower ultra-wide-band (UWB). The antennas are modified by employing the tapered slot edge (TSE) to lower the resonance frequency and improve the radiation characteristics. In order to increase the gain of antenna further so that improve the transimission efficiency from in-body to on-body, two compact on-body AVA arrays are designed operating at the lower UWB. The polarization of the two antenna arrays are linear and mutually perpendicular with each other. In addition, each of antenna arrays are composed of four single elements which are fed by optimized 1x4 Wilkinson power divider. The performance of S11 and far field patterns show that the operating band of the antenna arrays covers the lower UWB band, which signifies that the benefits of introducing TSE to lower the resonance frequency is obvious. Furthermore, the influence of the human body on antenna array is discussed and the results satisfies the requirement of in-body to on-body communication.

Xiao Fang, Mehrab Ramzan, Qiong Wang, Dirk Plettemeier

Study of a Dipole Antenna in the Vicinity of Lossless and Lossy Medium for On-body Antenna Analysis

In this paper, a detailed study of a dipole antenna in the close proximity of lossy and lossless human modeled structure is discussed. The main goal of the analysis is that which factors should be taken into account to design better on-body antennas and highlight the challenges associated with these kind of antennas in the vicinity of the lossy and lossless medium as compared to free space designs. The analysis is based on dipole separation distance from the equivalent human body structure. The antenna is analyzed in terms of shift in the frequency, reflection coefficient variation, input impedance and gain of the antenna. This analysis is very beneficial in terms of designing on body antennas and reliable wireless wearable devices. A full wave EM solver is used to demonstrate this study.

Mehrab Ramzan, Xiao Fang, Qiong Wang, Dirk Plettemeier

Estimating Eavesdropping Risk for Next Generation Implants

Implanted medical devices are expected to be wireless in near future. Wireless nature of sensing, controlling and transmission brings along different security threats. In this work, an analysis of eavesdropping risk is performed for an unencrypted data transmissions from an implanted medical device such as cardiac leadless pacemaker. This work utilizes statistical attenuation model along with measures of capacity, information rate and outage probability. Results show that eavesdropping risk depends on pathloss with shadow fading, distance and information rate (R). In addition, probability of successful eavesdropping increases if legitimate nodes transmits at lower rate. Thus, a proper tradeoff between information rate (R) and eavesdropping risk should be made. Numerical results show that at an information rate of 650 kbps, an IMD has a 5% risk of successful eavesdropping at a distance of 500 mm. This work also consider different transmission parameters like heart rate, blood pressure, ECG and EMG with their information rates and find probability of successful eavesdropping at different distances. This study provide basis for designing secure implantable cardiac leadless pacemaker with associated risks involved due to wireless nature of transmission.

Muhammad Faheem Awan, Kimmo Kansanen

A Study of Implant Antenna for FSK-Based Impulse Radio System in Human Body Communication Band

This paper aims to develop a miniaturized implant antenna for a Frequency Shift Keying (FSK)-based impulse radio system in Human Body Communication (HBC) band. The proposed implant antenna is realized with two layers of helical radiating elements formed on magnetic sheets, which contributes to a compact cylindrical shape with 1-cm diameter and 3-cm length for medical capsule endoscope application. The double-resonant return loss with 2 MHz bandwidth indicate the suitability of this antenna for a FSK-based impulse radio system. Moreover, the transmission characteristics and required transmit power using this antenna has also been investigated to clarify the feasibility of realizing real-time image transmission in HBC band.

Qiancheng Liang, Jingjing Shi, Atomu Nakashima, Jianqing Wang

Motion-Based Gait Identification Using Spectro-temporal Transform and Convolutional Neural Networks

The wide range of usage and application of wearable sensors like as smart watches provide access to precious inertial sensor data that is usable in human identification based on their gait pattern. A large number of studies have been conduced on extracting high-level and various heuristic features out of inertial sensor data to identify discriminative gait signatures and distinguish the target individual from others. However, complexity of the collected data from inertial sensors, detachment between the predictive learning models and intuitive feature extraction module increase the error rate of manual feature extraction. We propose a new method for the task of human gait identification based on spectro-temporal two dimensional expansion of gait cycle. Then, we design a deep convolutional neural network learning in order to extract discriminative features from the two dimensional expanded gait cycles and also jointly optimize the identification model simultaneously. We propose a systematic approach for processing nonstationary motion signals with the application of human gait identification with 3 main elements: first gait cycle extraction, second spectro-temporal representation of gait cycle, and third deep convolutional learning. We collect motion signal from 5 inertial sensors placed at different locations including lower-back, chest, right knee, right ankle, and right hand wrist. We pre-process the acquired raw signals by motion signal processing and then we propose an efficient heuristic segmentation methodology and extract gait cycle from the segmented and processed data. Spectro-temporal two dimensional features are extracted by merging key instantaneous temporal and spectral descriptors in a gait cycle which is capable of characterizing the non-stationarities in each gait cycle inertial data. The two dimensional time-frequency distribution representation of the gait cycle extracted from acquired inertial sensor data from 10 subjects are fed as input to the designed and proposed 10 layers DCNN architecture. Based on our experimental analysis, 93.36% accuracy was achieved for subject identification task.

Omid Dehzangi, Mojtaba Taherisadr, Raghvendar ChangalVala, Priyanka Asnani

Voluntary EMG-to-Force Estimation in Shoulder and Elbow During the Movement of Feeding Oneself

Muscle force estimation opens up the possibility of objective evaluating human motion in both mechanical and physiological ways. This paper proposes an EMG-adjusted method to predict individual muscle force in the shoulder and elbow during a purposeful daily activity: feeding oneself. Two male subjects were asked to flex and extend their shoulders and elbows to simulate the movement of getting food from the pocket to the mouth. Three inertial sensors and six surface electromyography (sEMG) sensors were used to synchronously collect motion and sEMG data during the movement. A Hill-type musculotendon model was then employed to predict individual muscle force by the fusion of motion and adjusted sEMG data. The result shows that our method can predict individual muscle force accurately with the ability to cover subject-specific joint dynamics and neural control strategies in multi-joints movement.

Jiateng Hou, Yingfei Sun, Lixin Sun, Bingyu Pan, Zhipei Huang, Jiankang Wu

Muscle Synergistic Pattern and Kinematic Sensor Data Analysis During Upper-Limb Reaching in Stroke Patients

Quantitative and efficient measurement of motor impairment level is of vital importance in stroke rehabilitation. This paper investigates the muscle synergistic patterns and kinematic sensor data of upper limb reaching in stroke patients with different impairment level. Thirty-three stroke patients and nineteen healthy age-matched subjects serving as the control group were asked to do voluntary upward reaching. Inertial sensors and surface electromyography (sEMG) sensors were attached to subjects’ upper limb to obtain the real-time joint angle through segment position by the inertial sensory data fusion and extract synergistic patterns from sEMG data by applying principal components analysis at the same time. The experimental results show that stroke patients not only have abnormal range of shoulder joint motion, which was correlated with the degree of clinical impairment level; but also have different muscle synergistic patterns at different impairment level, which can be used as a quantitative measurement of functional recovery status.

Bingyu Pan, Yingfei Sun, Zhipei Huang, Jiateng Hou, Jiankang Wu, Zhen Huang, Bin Xie, Yijun Liu

Inertial Sensor Based Human Activity Recognition via Reduced Kernel PCA

In the past decade, wearable inertial sensor based human activity recognition has attracted lots of attention from researchers in the world. High-dimensional feature set will increase the computation and memory cost. In this paper, kernel PCA has been utilized for dimensionality reduction to deal with inertial sensor based human activity recognition. However, kernel method may increase the computation and memory cost. Thus, reduced kernel method is proposed. The real dataset has been utilized to evaluate the proposed reduced kernel PCA (RKPCA) method. Experimental results demonstrate the efficacy of the proposed method, which achieves better results than traditional PCA method.

Donghui Wu, Huanlong Zhang, Cong Niu, Jing Ren, Wanwan Zhao

Design Practice of Visual Art Forms Based on Targeted Selection of Microcosmic Appearance

As a visual field that human eyes cannot directly observe, the microcosmic appearance is a rich element in the field of art and design, and it has great creative value. According to the design concept of microcosmic appearance of visual art form design, microscopic appearance imaging is obtained from the experiment. Furthermore, the microcosmic appearance of target selection is carried on the art transformation. As the elements of fashion design, diversification application of microcosmic appearance in clothing design. Thus, the visual effects of clothing and sustainable design concepts are reflected.

Xiaonong Qian, Ying Wang, Caixia Du, Yuhui Yang

Practice Research on Chaos-Theory-Based Algorithmic Composition

Chaotic music composition requires a series of “musical events” which constitute music to continuously take place, develop and evolve in time and space sequence orbit, the cross and integration of its certainty and uncertainty offer a new manner for music composition-Algorithmic Composition and extend to the domain of colorful music so as to form one kind of design thinking with randomness. In this paper, we summarize several Chaos composer algorithm design a set of installation art by using its randomness features. The results of the random combination of the music clips are visualized according to the emotional color of the interval, and realize the conversion between music and color. This research provides new ideas for the development of new media art.

Xiaonong Qian, Yiwen Sun, Caixia Du, Yuhui Yang
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