Effects of water flow volume on the isolation of bacteria from motion sensor faucets

Water outlets for washing hands and medical equipment are essential for preventing hospital infection. The present study clarified the effects of water flow volume on the identification and quantitative evaluation of bacteria found around spouts in the 17 hand-washing stations. Pseu-domonas aeruginosa was detected from 4 sta-tions before adjustment and 2 after adjustment. Although no significant difference was identified in the detection rate of P. aeruginosa (p = 0.368), when combining P. aeruginosa and glucose non-fermentative Gram-negative bacilli (NFB), the number of stations with P. aeruginosa and/or NFB decreased significantly from 15 before adjustment to 9 after adjustment (p = 0.023). Before adjust-ment, quantity of bacteria was “2+” for 3 stations and “1+” for 7 stations, but was “1+” for 3 stations and “2+” for 0 stations after adjustment. These results show that quantity of bacteria could be reduced from spouts by adjusting flow volume. These results were also supported by experiments for cleanliness using Adenosine 5’-triphosphate bioluminescence me- thod.

Enhanced performance of triboelectric mechanical motion sensor via continuous charge supplement and adaptive signal processing

The development of automation industry is inseparable from the progress of sensing technology.As a promising self-powered sensing technology,the durability and stability of triboelectric sensor(TES)have always been inevitable challenges.Herein,a continuous charge supplement(CCS)strategy and an adaptive signal processing(ASP)method are proposed to improve the lifetime and robustness of TES.The CCS uses low friction brushes to increase the surface charge density of the dielectric,ensuring the reliability of sensing.A triboelectric mechanical motion sensor(TMMS)with CCS is designed,and its electrical signal is hardly attenuated after 1.5 million cycles after reasonable parameter optimization,which is unprecedented in linear TESs.After that,the dynamic characteristics of the CCS-TMMS are analyzed with error rates of less than 1%and 2%for displacement and velocity,respectively,and a signal-to-noise ratio of more than 35 dB.Also,the ASP used a signal conditioning circuit for impedance matching and analog-to-digital conversion to achieve a stable output of digital signals,while the integrated design and manufacture of each hardware module is achieved.Finally,an intelligent logistics transmission system(ILTS)capable of wirelessly monitoring multiple motion parameters is developed.This work is expected to contribute to automation industries such as smart factories and unmanned warehousing.

An Approach to Star Map Simulation for Star Sensor Considering the Effect of Image Motion

To test high resolution and dynamic performance of star sensor, a method of consideration image motion on Modeling the motion blur of star sensor is proposed. Firstly, image motion geometric model based on the rotation of Starlight vector is studied. Secondly, with the help of the normal distribution of static star image energy model, introducing the star image motion speed, obtaining the energy distribution function of moving stars, implementing high dynamic simulation of star map. Finally, establishing the simulation environment, through adjusting input parameters such as integral time, rate of change of three attitude angle, the launch time, location, then, important simulation data of stars observed by star sensor in orbit can quickly be obtained, such as navigation stars information, value and direction of image motion, intensity distribution, signal to noise ratio. This work is very important to research and evaluate the star image motion compensation algorithm.

The Safe Navigation of Partial Motion Planning Based on “Cooperation” with Roadside Fixed Sensors in VANET

In recent years, many methods of safe vehicle navigation and partial motion planning (PMP) have been proposed in vehicular ad-hoc network (VANET) field. In order to improve the limitation of traditional PMP, this paper presents a novel effective way to plan motion with cooperation of roadside fixed sensors (RFSs). With their cooperation, the vehicles can get the surrounding information quickly and effectively, and give highly accurate projections about the near future conditions on road. After proposing our algorithm, the worst case is analyzed and methods are found to solve the problem. Finally we conduct one elemental contrast experiment, driver situation awareness, with or without the “cooperation” of RFSs in highway scenarios. The result shows that the vehicles can make a better PMP based on the forward conditions received from RFSs, and extend the warning distance obviously when emergency happens.

基于Leap Motion和支持向量机的手势识别

为了使手势识别在更多的领域得到推广及应用,提出了基于Leap Motion体感设备实时跟踪技术获取手势三维空间坐标信息的方法,并从中分别提取角度信息和相对坐标信息,构建手势特征数据,建立手势识别模型。对特征数据进行归一化处理后,利用支持向量机(SVM)分类器进行训练、建模和分类,实现手势识别。实验结果表明:以角度数据和坐标数据作为手势特征的方法可行,平均识别率分别为96.6%和91.8%。通过对比可以得出:以角度数据作为特征值具有较高的准确性和鲁棒性,并避免了单纯依照一种特征值产生的局限性。

基于多Leap Motion传感器的机械手手势控制系统

为了实现对工业机械手的手势控制,并针对单个Leap Motion传感器在使用时因手指或者手掌遮挡而导致识别率降低的缺点,提出了基于多Leap Motion传感器,用于人手数据采集的新型控制系统。该系统采用基于主成分分析法的数据融合技术对该多传感器数据进行数据融合以得到人手姿态完整的信息,再将该融合数据送入SVM多分类器进行手势姿态识别。实验结果表明该系统能准确地检测人手完整信息,避免了使用单个Leap Motion传感器因视觉干扰或者不可避免的遮挡导致手势姿态数据不准确的问题,提高了手势识别率,可以准确、有效地实现对机械手进行控制。

Sensors-Based Ambient Assistant Living via E-Monitoring Technology

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted timely.Apart from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with robots.Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions.In this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human actions.These features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization method.Finally,the Masi entropy classifier is applied for action recognition and classification.Experiments have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and CMU-MMAC.During experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC dataset.Hence experimental results validated the proposed system.

基于Motion JPEG2000的分布式无线传感器网络视频压缩新算法

在保证传输图像质量的前提下最小化能量消耗,是基于无线传感器网络(WSN)的视频监视系统设计中的一个关键问题。本文针对WSN结构特点,提出了一种分布式视频压缩算法。该算法基于Motion JPEG2000的分片编码方式,定义了两个参数来判定视频序列中一帧图像区域的重要性大小,一个是片的复杂度,另一个是片的运动程度。从而以低复杂度算法实现了自适应的码率分配。文中基于此算法提出WSN构建方案。仿真实验结果表明,本文提出的分布式视频压缩方案,能有效减小WSN中数据传输能量消耗,同时得到较好的图像重建效果。

Identity Authentication Based on Sensors of Smartphone and Neural Networks

The smartphone has become an indispensable electric device for most people since it can assist us in finishing many tasks such as paying and reading. Therefore, the security of smartphones is the most crucial issue to illegal users who cannot access legal users’ privacy information. This paper studies identity authentication using user action. This scheme does not rely on the password or biometric identification. It checks user identity just by user action features. We utilize sensors installed in smartphones and collect their data when the user waves the phone. We collect these data, process them and feed them into neural networks to realize identity recognition. We invited 13 participants and collected about 350 samples for each person. The sampling frequency is set at 200 Hz, and DenseNet is chosen as the neural network to validate system performance. The result shows that the neural network can effectively recognize user identity and achieve an authentication accuracy of 96.69 percent.

基于Leap motion的钢琴弹奏手指运动曲线的测量与分析

针对建立手指触键运动自适应曲线的完整描述,提出了一种基于Leap motion空间3D体感传感器的新型测量方法。首先引入手指运动偏角的概念,建立手指触键运动模型并分布对齐坐标系;然后,根据新型测量方法,进行手指触键动作捕捉与运动特征点选取;最后运用不同的拟合模式在置信区间为95%的条件下处理手指触键动作数据,建立手指触键运动特征描述自适应函数。该函数针对不同长度的人手可为其提供准确的钢琴弹奏触键运动曲线。该研究为钢琴弹奏教育服务型机器人教学平台中的手指触键运动曲线自适应判别系统的建立提供理论支持。

基于智能手机传感器数据的运动状态分类与特征预测

随着智能手机的广泛使用,其内置的传感器为监测和分析用户的运动状态提供了丰富的数据资源。这些数据不仅能帮助用户了解自己的活动模式,还能用于估算日常运动所消耗的热量。然而,使运动健康应用准确地将这些数据分类并预测用户的运动特征,仍是一项技术挑战。探讨了基于智能手机传感器数据的运动状态分类与特征预测方面存在的问题,提出了优化智能手机传感器数据处理的建议,以增强运动状态分类的准确性,从而为开发更智能、更准确的运动健康监测应用奠定基础。

基于Leap Motion的动态手势识别

动态手势识别作为人机交互的一个重要方向,在各个领域具有广泛的需求。相较于静态手势,动态手势的变化更为复杂,对其特征的充分提取与描述是准确识别动态手势的关键。为了解决对动态手势特征描述不充分的问题,利用高精度的Leap Motion传感器对手部三维坐标信息进行采集,提出了一种包含手指姿势和手掌位移的特征在内的、能够充分描述复杂动态手势的特征序列,并结合长短期记忆网络模型进行动态手势识别。实验结果表明,提出的方法在包含16种动态手势的数据集上的识别准确率为98.50%;与其他特征序列的对比实验表明,提出的特征序列,能更充分准确地描述动态手势特征。

基于G-sensor有运动情景动态心电监护系统

基于移动设备的心电监护设备越来越多,但大多数仅显示心电图,没有考虑到对应的用户运动情景。使用Android手机内置的G-sensor采集三轴加速度,设计一种有运动情景的动态心电监护系统。充分利用目前Android手机提供的功能,降低医疗成本,是面向家庭和社区医院的新型医疗系统,用户家属和医务人员可以通过客户端对用户进行实时监护,随时掌握其心电和运动状态信息,有利于医生做出准确的诊断,实现远程的、有运动情景的动态心电监护。

Spiral Steel Wire Based Fiber-Shaped Stretchable and Tailorable Triboelectric Nanogenerator for Wearable Power Source and Active Gesture Sensor

Continuous deforming always leads to the performance degradation of a flexible triboelectric nanogenerator due to the Young’s modulus mismatch of different functional layers.In this work,we fabricated a fiber-shaped stretchable and tailorable triboelectric nanogenerator(FST-TENG)based on the geometric construction of a steel wire as electrode and ingenious selection of silicone rubber as triboelectric layer.Owing to the great robustness and continuous conductivity,the FST-TENGs demonstrate high stability,stretchability,and even tailorability.For a single device with ~6 cm in length and ~3 mm in diameter,the open-circuit voltage of ~59.7 V,transferred charge of ~23.7 nC,short-circuit current of ~2.67 μA and average power of ~2.13 μW can be obtained at 2.5 Hz.By knitting several FST-TENGs to be a fabric or a bracelet,it enables to harvest human motion energy and then to drive a wearable electronic device.Finally,it can also be woven on dorsum of glove to monitor the movements of gesture,which can recognize every single finger,different bending angle,and numbers of bent finger by analyzing voltage signals.

基于Leap Motion的体感遥控小车设计

Leap Motion是一款用于识别手部细微动作的3D体感传感器,能够以超过290帧/s的速度追踪人体手部的动作。通过PC对Leap Motion进行实际开发,将采集到的手势数据转换成控制指令,并通过ATK-ESP8266构建无线网络环境,将控制指令传递给遥控小车。遥控小车以STM32F407ZGT6为主MCU,由3个直流伺服电机进行驱动,通过CAN总线建立MCU与3个伺服驱动器之间的通讯,并构建遥控小车的闭环控制算法,最终实现通过人体的简单动作手势对遥控小车进行准确控制。

Jogging and Walking Analysis Using Wearable Sensors

Gait analysis is a process of learning the motion of human and animal by using wearable sensor approach and vision approach. This analysis is mainly used in medical and sports field where the study of body parts is crucial. 3-space sensor is a sensor consists of accelerometer, gyroscope sensor and compass sensor, built in one device. In this study, 3-space sensor is used to collect data of walking and jogging motion, of a test subject running on a treadmill. Angular velocity of the test subject’s arm and the angle of subject’s leaping motion are the two main components under investigation. Data are analyzed and processed with Principal of Component Analysis (PCA) technique. This method aims to combine and reduce the number of variables of the raw data. The Quiver function is used in order to generate feature vectors for both motions. Furthermore, the output of the process was used to create a system that can recognize human motion on any given data. The system is highly able to differentiate both of the motions.

面向人体运动检测的水凝胶基柔性应变传感器设计

在可穿戴电子设备迅猛发展的背景下,构建面向人体运动检测的可穿戴柔性应变传感器成为目前传感器研究的重点之一。基于聚丙烯酰胺(PAM)、纤维素纳米晶(CNCs)、单宁酸(TA)构建了一种导电水凝胶基柔性应变传感器。机械性能与电学性能测试结果表明,相较于其他柔性应变传感器,聚丙烯酰胺-纤维素纳米晶-单宁酸导电水凝胶基柔性应变传感器具有超长的拉伸范围(0%~2 900%)、较好的黏附性、快速的响应时间(280 ms)和良好的稳定性(超320次加载-卸载循环)。该传感器对人体关节弯曲的大应变与喉部活动的小应变等运动信号具备良好的检测功能,可用作人体运动检测的柔性可穿戴传感器。

用于运动状态监测的纸基柔性摩擦电传感器

为解决柔性摩擦电传感器制备流程复杂、成本高、不够环保等问题,设计了一种基于纸基导电银浆的低成本柔性摩擦电传感器。当外部压力由5 N增大至50 N时,其输出电压和输出电流逐渐增大,在频率为1 Hz、幅值为50 N的外部压力作用下,输出电压的峰值约为7.52 V。当外部压强在17~173 kPa的范围内变化时,输出电压峰值与压强呈分段线性关系。当外部压力的频率变化时,输出电压保持不变。结果表明,该传感器压力传感性能良好。当应用于运动传感中时,该传感器能够感知肘关节、腕关节的弯曲角度,并能有效区分走、快走、跑3种运动类型以及踮脚、深蹲、抱腹跳3种健身动作,计算人体行走频率与健身动作次数,在人体运动状态监测等领域具有良好的应用价值。

Optic Fiber-Based Dynamic Pressure Sensor

Weigh-in-Motion（WIM） technique is the process of measuring the dynamic tire forces of a moving vehicle and estimating the corresponding tire loads of the static vehicle. Compared with the static weigh station, WIM station is an efficient and cost effective choice that will minimize unneccessary stops and delay for truckers. The way to turn birefringence of single-mode fiber into a prime quality for a powerful and reliable sensor is shown. Preliminary results for the development of a weigh-in-motion （WIM） technique based on sagnac-loop sensor are presented. After a brief description of the sensor and its principle of operation, the theoretical model characterization made in is developed. Then, a full static conditions is presented.

运动加速度抑制的偏振光/MIMU组合导航SHKF姿态解算

针对无人机受大运动加速度影响时,组合导航中加速度计输出信息关于有效重力加速度的占比减少,偏振光传感器会产生抖动现象,对姿态角求解模型的准确建立进行研究。首先,引入偏振光传感器组合导航,介绍了偏振光导航的定向原理。然后,以双矢量高斯牛顿法和Sage-Husa Kalman (SHKF)算法构建级联式航姿解算算法,在多传感器的基础上观测无人机的姿态信息。最后,在分析运动加速度影响下加速度计测量结果准确性的基础上,提出信任因子解决运动加速度对姿态解算的影响,该算法可以抑制高速运动的无人机体产生的运动加速度产生的影响。为了验证该算法的可行性,基于偏振光/微型惯性测量单元(Miniature Inertial Measurement Unit,MIMU)组合导航平台进行实验,结果表明静态和动态环境中所提出算法相较于PI,EKF算法有30%左右的提升,运动加速度影响下抑制了由于非重力加速度的加入导致的姿态偏移。提高了无人机在运动加速度下的情况下的姿态解算精度,保证了无人机的正常飞行。