可穿戴传感器的特点与趋势

从分析可穿戴产品入手,解析可穿戴传感器的趋势。     

A wearable health care system based on knitted integrated sensors.

A comfortable health monitoring system named WEALTHY is presented. The system is based on a textile wearable interface implemented by integrating sensors, electrodes, and connections in fabric form, advanced signal processing techniques, and modern telecommunication systems. Sensors, electrodes and connections are realized with conductive and piezoresistive yarns. The sensorized knitted fabric is produced in a one step process. The purpose of this paper is to show the feasibility of a system based on fabric sensing elements. The capability of this system to acquire simultaneously several biomedical signals (i.e. electrocardiogram, respiration, activity) has been investigated and compared with a standard monitoring system. Furthermore, the paper presents two different methodologies for the acquisition of the respiratory signal with textile sensors. Results show that the information contained in the signals obtained by the integrated systems is comparable with that obtained by standard sensors. The proposed system is designed to monitor individuals affected by cardiovascular diseases, in particular during the rehabilitation phase. The system can also help professional workers who are subject to considerable physical and psychological stress and/or environmental and professional health risks.     

Ultrasensitive skin‐like wearable optical sensors based on glass micro/nanofibers

Electronic skin,a class of wearable electronic sensors that mimic the functionalities of human skin,has made remarkable success in applications including health monitoring,human-machine interaction and electronic-biological interfaces.While electronic skin continues to achieve higher sensitivity and faster response,its ultimate performance is fundamentally limited by the nature of low-frequency AC currents.Herein,highly sensitive skin-like wearable optical sensors are demonstrated by embedding glass micro/nanofibers(MNFs)in thin layers of polydimethylsiloxane(PDMS).Enabled by the transition from guided modes into radiation modes of the waveguiding MNFs upon external stimuli,the skin-like optical sensors show ultrahigh sensitivity(1870 k·Pa^-1),low detection limit(7 mPa)and fast response(10μs)for pressure sensing,significantly exceeding the performance metrics of state-of-the-art electronic skins.Electromagnetic interference(EMI)-free detection of high-frequency vibrations,wrist pulse and human voice are realized.Moreover,a five-sensor optical data glove and a 2×2-MNF tactile sensor are demonstrated.These initial results pave the way toward a new category of optical devices ranging from ultrasensitive wearable sensors to optical skins.     

基于压电薄膜和片上可编程系统的可穿戴体征监测系统

随着社会经济的发展,人口与建筑密度日益增大,火灾发生的概率越来越大,扑救难度也越来越高。传统的消防防护服属功能服装只能实现防火,隔热和阻燃等基本功能。而消防战斗员的生命体征无法被消防指挥员及时获知,这对保障消防战斗员的生命安全,和消防现场的应急指挥非常重要。正是在这种形势下,可穿戴体征监测系统的研究才具有重要的实用意义。研究的可穿戴体征监测系统,是可装配于救援防护服本体上,基于压电薄膜技术和片上可编程系统技术搭建的,具有生命体征监测和告警功能的电子系统。装配了该系统的救援防护服,不仅能够实现传统防护服对救援队员的保护作用,还可以实时监测救援队员的生命体征,通过声光告警电路将现场信息反馈给救援队员,提高了消防救援和指挥效率。     

Neural network based real-time heart sound monitor using a wireless wearable wrist sensor

A new method is presented using a wearable wrist sensor to estimate acoustic parameters S1 and S2 of the heart sounds based on the neural network technique. Using the signal processing method, the heart conditions can be analyzed and monitored in real time and potentially in a long term with a wrist device. The velocities and time delays of the cardiac pulse waves in blood vessels were experimentally acquired and calculated at different artery locations on the human body. Signal attenuation of the pulses from the heart to the wrist radial artery was analyzed and a pulse-waveform travel model in blood vessels was proposed. A band-pass filter is applied to the pulse waves at various artery locations to reveal the heart sound features S1 and S2 existed in the pulse waves. In order to obtain accurate acoustic parameters, a neural network with two layers and 500 nonlinear tansig neurons was employed to estimate the heart sounds using the pulse waveforms from the wrist radial artery. It is encouraging to find that the acoustic parameters of estimated heart sounds by the trained neural network have only 1% average errors compared with the original heart sounds. The effects of various analog-to-digital conversion resolutions and sample rates were empirically analyzed. When the maximum value of errors is allowed within 2.15%, a 10,000-Hz sample rate and 12-bit resolution should be an appropriate selection for lower power consumption. Using the trained neural network, the new estimation method has been verified by a sensor with Bluetooth communication strapped on the wrist, thus mobility is not limited for the person whose heart sounds need to be monitored.     

长周期光纤光栅温度传感器的研究进展

长周期光纤光栅（LPFG）具有与短周期光纤光栅所不同的光谱特性。它无背向反射光谱,不对光源的稳定性产生影响。基于纤芯基模与包层模的谐振耦合原理和热光效应,LPFG温度传感器利用谐振波长的漂移变化实现对温度的测量,具有很高的灵敏度。介绍了LPFG的制作方法、LPFG温度传感器的工作原理及最新研究进展。由于具有插入损耗小、可用于遥测、精度高和抗电磁干扰能力强等优点,LPFG温度传感器将有广阔的应用前景。     

An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors

Human activity recognition by using wearable sensors has gained tremendous interest in recent years among a range of health-related areas. To automatically recognize various human activities from wearable sensor data, many classification methods have been tried in prior studies, but most of them lack the incremental learning abilities. In this study, an incremental learning method is proposed for sensor-based human activity recognition. The proposed method is designed based on probabilistic neural networks and an adjustable fuzzy clustering algorithm. The proposed method may achieve the following features. 1) It can easily learn additional information from new training data to improve the recognition accuracy. 2) It can freely add new activities to be detected, as well as remove existing activities. 3) The updating process from new training data does not require previously used training data. An experiment was performed to collect realistic wearable sensor data from a range of activities of daily life. The experimental results showed that the proposed method achieved a good tradeoff between incremental learning ability and the recognition accuracy. The experimental results from comparison with other classification methods demonstrated the effectiveness of the proposed method further.     

Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.     

L波段电离层闪烁与TEC监测系统研制

介绍了我们研制的一种L波段电离层闪烁和TEC监测仪,并给出初步观测结果.文中提出了基于载噪比计算S4的方法和一种新的用TEC的标准差定义的闪烁活动指数.与GSV4004型GPS电离层冈烁和TEC监测仪在同一地点的对比观测表明,本文研制的监测仪测量得到的数据可靠,能够有效地监测电离层振幅/相位闪烁和TEC扰动现象.     

一种基于绿光的可穿戴式光电容积脉搏波测量系统

脉搏波中蕴含丰富的血流动力学信息。利用无创的方法检测脉搏波并推导出人体心血管系统生理、病理特征成为研究的热点。设计一套可穿戴式的脉搏波提取设备,该设备包括MCU控制模块、信号采集模块、蓝牙模块、电源模块。分析光电容积法获取脉搏波的原理,采用523 nm绿光和环境光学传感器作为设备核心,详解描述了硬件开发模型,给出了信号处理部分的关键代码。实验结果表明,所设计的脉搏波提取设备在手腕、手指和额头等部位,能够很好地描绘脉搏波,具有很强的应用性。     

Secure and efficient anonymous authentication scheme for three-tier mobile healthcare systems with wearable sensors

The mobility and openness of wireless communication technologies make Mobile Healthcare Systems (mHealth) potentially exposed to a number of potential attacks, which significantly undermines their utility and impedes their widespread deployment. Attackers and criminals, even without knowing the context of the transmitted data, with simple eavesdropping on the wireless links, may benefit a lot from linking activities to the identities of patient’s sensors and medical staff members. These vulnerabilities apply to all tiers of the mHealth system. A new anonymous mutual authentication scheme for three-tier mobile healthcare systems with wearable sensors is proposed in this paper. Our scheme consists of three protocols: Protocol-1 allows the anonymous authentication nodes (mobile users and controller nodes) and the HSP medical server in the third tier, while Protocol-2 realizes the anonymous authentication between mobile users and controller nodes in the second tier, and Protocol-3 achieves the anonymous authentication between controller nodes and the wearable body sensors in the first tier. In the design of our protocols, the variation in the resource constraints of the different nodes in the mHealth system are taken into consideration so that our protocols make a better trade-off among security, efficiency and practicality. The security of our protocols are analyzed through rigorous formal proofs using BAN logic tool and informal discussions of security features, possible attacks and countermeasures. Besides, the efficiency of our protocols are concretely evaluated and compared with related schemes. The comparisons show that our scheme outperforms the previous schemes and provides more complete and integrated anonymous authentication services. Finally, the security of our protocols are evaluated by using the Automated Validation of Internet Security Protocols and Applications and the SPAN animator software. The simulation results show that our scheme is secure and satisfy all the specified privacy and authentication goals.     

Test chip for the development and evaluation of sensors for measuring stress in metal interconnects

The development of a new test chip is presented, containing structures for the direct measurement of stress in metallic interconnect layers associated with silicon integrated circuit technology. The rotation of the structures provides a simple method of differentiating between tensile and compressive stress. This test chip design has been used to fabricate working structures allowing the study of stresses in aluminum layers before and after sample sintering. The results are presented together with the design, fabrication, and measurement considerations that have arisen during the research. The problems experienced in removing the sacrificial layer material, necessary to release the structures, are discussed along with potential solutions. The sensor structure is suitable for fabrication within a CMOS facility and its inherent scalability makes it potentially suitable for in-line testing of state-of-the-art processes.     

Assessment of tremor activity in the Parkinson's disease using a set of wearable sensors

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.     

面向可穿戴设备的空气质量监测系统的设计与实现

实时监测的实现主要依赖于对空气质量数据的收集、处理、存储以及显示,而空气质量预测功能是项目开发的难点。这里所采用的预测算法,其核心是针对影响空气质量指数的污染因子的历史浓度做出分析,根据其变化规律建立预测模型,通过非线性最小二乘法拟合各个污染因子的变化曲线,求出其预测值,进而计算出空气质量预测值。所提出的预测算法是建立在统计计算的基础之上,对已有数据进行挖掘和分析,通过机器学习的方法来做出预测。在可穿戴设备的开发方面,需要考虑到其交互特点,结合Android开发技术开发出便捷的应用。经过测试,该算法的空气质量等级预测的准确率达到了47.77%,而首要污染物预测的准确率则达到了73.66%,满足基本需求。     

基于ZigBee的穿戴式医疗监护系统节点的设计与实现

为了满足当今社会对穿戴式医疗监护的需求,设计并制作出了一种基于ZigBee技术的穿戴式医疗监护系统节点。该节点硬件部分采用了CC2530单片机和多种传感器,软件部分使用TI公司的Z-Stack协议栈,最终以相对低的成本实现了低生理、心理负荷下对人体体温、脉搏、生理姿态的获取。     

CMOS stress sensors on (100) silicon

CMOS analog stress sensor circuits based upon the piezoresistive behavior of MOSFET's are presented. On the (100) surface, these circuits provide temperature-compensated outputs that are proportional to the in-plane normal stress difference (σ(11)'-σ₂₂') and the in-plane shear stress σ₂₂'. The circuits provide high sensitivity to stress, well-localized stress-state measurement, and direct voltage or current outputs that eliminate the need for tedious ΔR/R measurements required with more traditional resistor rosettes. The theoretical and experimental results also provide design guidance for calculating and minimizing the sensitivity of traditional analog circuits to packaging-induced die stress     

基于LabVIEW的温度监测仪设计

采用LabVIEW开放式虚拟仪器开发平台,设计了一个基于虚拟仪器的数字化温度监测和控制系统,阐述了温度监测仪开发过程中数据的采集和软硬件的设计.利用LabVIEW提供的DAQ技术,将仿真电路变为实际有用的仪器,在PC机上实现了实时温度监测显示和报警功能.     

基于肌电感应的可穿戴运动分析系统设计与实现

表面肌电信号是皮下肌肉活动在皮肤表面处表现出的微弱的电压信号,它反映了人体运动时肌肉动作的时间和空间信息.对于不同的运动动作,人体各个肌肉群产生的肌电信号的强度也各有差异.本文设计了一种基于肌电信号感应的可穿戴运动分析系统.通过提取采集到的多路肌电信号的时域、频域特征值,用加权归一化的方法给信号强度评级,从而确定人体运动时指定肌肉群的运动强度.可以利用这套系统设计目的性较强的运动动作.     

基于矿用可穿戴设备与智能分析算法的数据处理技术研究

针对采矿作业人员的身体健康监测问题,文中提出了一种基于矿用穿戴设备智能数据分析的采矿作业人员健康状态评估方法.该方法结合了多源数据融合方法和支持向量机(SVM)算法,对矿用可穿戴设备采集的数据进行预处理;在数据分析层面进行多源数据融合;采用小波变换方法提取数据特征参数,并将特征参数作为支持向量机算法的输入;计算出采矿作...