可穿戴型下肢助力机器人感知系统研究

在分析可穿戴型下肢助力机器人所需要的控制信息的基础上,设计了一套基于CAN总线的人体下肢运动信息感知系统.该系统主要包括可获取人体腿部和脚底状态的力传感器和获取髋关节和踝关节角度信息的编码器.实验结果表明,所设计的多传感器系统稳定,实时性好,满足可穿戴型下肢助力机器人系统控制的需要.     

可穿戴型下肢助力机器人感知系统研究

在分析可穿戴型下肢助力机器人所需要的控制信息的基础上，设计了一套基于CAN总线的人体下肢运动信息感知系统。该系统主要包括可获取人体腿部和脚底状态的力传感器和获取髋关节和踝关节角度信息的编码器。实验结果表明，所设计的多传感器系统稳定，实时性好，满足可穿戴型下肢助力机器人系统控制的需要。     

康复外骨骼机器人设计

康复外骨骼机器人旨在协助残障患者等恢复支撑骨骼。外骨骼下肢助力机器人采用不同的结构、驱动方式、传感器分布和控制策略,针对单自由度驱动下肢外骨骼机器人本体设计方案,创建了虚拟样机,分析了多姿态运动的运动学和动力学参数变化。采取被动驱动带主动驱动的思想,减少驱动数量,精简结构与控制系统。其可面向不同的应用领域,为穿戴者提供助力和帮助,具有一定的实用价值。     

基于激光距离传感器的路况识别系统的设计

针对动力型下肢设备存在的运动模式识别率低的问题,设计了一种可穿戴式路况识别系统。通过安装在腰部的便携式激光距离传感器和惯性测量传感器分别采集激光距离信息和地形高度信息,利用小波去噪算法对所采集的数据进行处理,提取特征值,最后选择训练简单、结构清晰的概率神经网络进行路况识别。实验结果表明,该便携式系统能有效识别平地、上楼、下楼、上坡和下坡五种路况并提高识别精度,证明了将可穿戴式路况识别系统应用于假肢或助行器等动力型下肢设备环境感知系统的有效性和可行性。     

基于Kinect的机器人控制系统

设计一款可以通过人体动作对机器人进行控制的机器人控制系统。该系统由主机和从机两部分组成,通过Kinect体感传感器采集人体动作信息,在主机中进行图像处理解析出相应的人体动作,然后通过无线传输单元向机器人发送相应的控制指令,控制机器人做出响应,完成相应的一套动作或对人体动作进行实时模仿。制作的机器人样机运行良好,能够根据人体左右手的动作和语音命令,做出正确的响应。     

基于仿生运动的康复机器人结构设计与优化

本文的主要研究内容就是对于康复机器人的运行状态以及运行效果进行了讨论,首先分析了人体下肢康复理论以及下肢相关骨骼。基于康复利润以及下肢骨骼,本文对于使用的康复机器人进行了自由度配置、关节选择、驱动方式选择、仿生关节设计五个部分的讨论和阐述。最终得出结论,康复机器人的设计需要给予康复理论和人体骨骼情况来选择驱动方式、配置方式,而康复机器人的优化方式则是尽量加强踝关节,重视行走的基础部分,只有这样才能够达到最佳的效果。     

基于多传感器的礼仪机器人测距系统的研发

为了提高礼仪机器人在复杂环境中测量数据的可靠性以及解决同一类型多传感器测距盲区无法补偿的问题,使得礼仪机器人可以测得300cm以内障碍物的距离信息,本设计采用红外传感器和超声波传感器组成礼仪机器人的测距系统,并应用自适应加权数据融合技术对传感器所测数据进行融合,实现了两种传感器在功能上的互补,提升了系统的实时性,减小了系统的测距误差。实验结果表明系统可在300cm以内精确测距,提高了整体测距精度,得到了被测距离更加准确的估计。     

管道蛇形清洁机器人控制系统设计

本文针对管道油污难清理难题以及管道清洁机器人对远程监控、运动控制、清洁控制与多信息的获取和处理的需求,设计了以STM32单片机为核心、多个传感器和模块融合的管道蛇形清洁机器人控制系统,实现了机器人远程实时控制以及对管道内环境信息采集和处理等功能。本文所设计的整个控制系统具有良好的可操作性和拓展性。本文通过模拟管道清洁场景实验和Adams软件分析,结果表明:蛇形机器人控制系统可实现远程无线获取多信息,并能有效的清洁管道内壁污垢。     

一种用于机器人手爪的PVDF接触力传感器设计

利用PVDF压电薄膜的压电敏感特性,设计了一种用于机器人手爪的接触力传感器,包括一个电荷放大装置,并建立了数据采集和处理系统。所设计的传感器具有体积小,柔顺性好,信号处理简单的特点。实验分析表明传感器可很好地满足机器人手爪的要求,为机器人手爪提供接触力信号,以实现稳定可靠地抓取。     

可穿戴膝关节康复外骨骼机器人的设计与探究

随着我国的医疗设施的逐步完善,膝关节损伤的患者对于康复设施的性能和功能需求也越来越高。本项目设计的可穿戴膝关节康复外骨骼机器人通过转移内骨骼的力以减少关节摩擦来达到缓解疼痛的最终目的,创新性开发出通过钛合金材料做成的外骨骼,使得结构紧凑,解决了使用大型机械外骨骼才能达到的展平效果,更在深入探究人体的膝关节运动参数和康复训练各项要求的基础上,设计了一套结构简便、穿戴方便舒适、关节灵活的可穿戴膝关节康复外骨骼机器人。     

基于虚拟样机技术的外骨骼机器人机构设计

为了提高人类的负重能力,并降低人类在行进中的能量消耗,设计了一种液压驱动的可穿戴外骨骼机器人,进行了机构设计并确定了液压系统的关键参数,其中腿机构设计是关键部分。通过数学公式分析了外骨骼机器人腿部受力,并对外骨骼机器人简化模型进行虚拟仿真,确定各关节运动所需的力矩。在此基础上,完成了机器人机构和液压系统设计。最后通过虚拟样机技术,验证了机构设计参数选取的合理性及所确定的液压系统满足设计要求。     

Wear-free indoor fall detection based on RFID and deep residual networks

Since falls of the elderly can easily cause serious health problems in daily life, fall detection has received the attention of researchers. Traditionally, wearable sensors have been used to detect whether a person has fallen. However, wearable sensors may bring inconvenience to users' activities and affect user experience. In this paper, a fall detection approach based on RFID is proposed. In the proposed approach, non-contact passive tags are used to construct an array of tags. Fall detection will be performed without the user wearing the device. The RSSI and phase data are collected when the reader queries the tags. Furthermore, an action segmentation algorithm is designed to quickly extract human action information based on the short-term variance change of the phase signal. Subsequently, a deep residual network is built to classify fall movements and daily movements. Experiments show that the system can handle differences among users and locations and has an excellent performance in terms of recognition accuracy and efficiency, with an average accuracy rate of 96.77%.     

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。     

Measuring human–robot interaction on wearable robots: A distributed approach

This paper presents a novel, distributed approach to monitor physical interaction between a user and a wearable robot. We propose to apply a matrix of optoelectronic sensors embedded in a thin and compliant silicone bulk onto the user-robot contact surface. This distributed tactile sensor can measure the pressure distribution on the interaction area without affecting the comfort of the user, and does not require the robot to be specifically designed to house it. Besides the estimation of the interaction force/torque, the distributed approach allows to monitor the pressure on the user’s skin. This information is fundamental to assess the comfort and safety of the users which determine the final acceptability of the robot-mediated rehabilitation. The proposed method is preliminary evaluated on an elbow active orthosis during a repetitive rehabilitation task. Experimental results prove the relevance of this approach for the detection of the user motion intention through a measurement of the interaction force distribution.     

Gait Analysis Using a Shoe-Integrated Wireless Sensor System

We describe a wireless wearable system that was developed to provide quantitative gait analysis outside the confines of the traditional motion laboratory. The sensor suite includes three orthogonal accelerometers, three orthogonal gyroscopes, four force sensors, two bidirectional bend sensors, two dynamic pressure sensors, as well as electric field height sensors. The "GaitShoe" was built to be worn in any shoe, without interfering with gait and was designed to collect data unobtrusively, in any environment, and over long periods. The calibrated sensor outputs were analyzed and validated with results obtained simultaneously from the Massachusetts General Hospital, Biomotion Laboratory. The GaitShoe proved highly capable of detecting heel-strike and toe-off, as well as estimating foot orientation and position, inter alia.     

High Performance Bacterial Cellulose Organogel-Based Thermoelectrochemical Cells by Organic Solvent-Driven Crystallization for Body Heat Harvest and Self-Powered Wearable Strain Sensors

As a low-grade sustainable heat source, the human body provides a great driving force for converting heat into electric energy using thermoelectric materials, which can effectively power wearable electronics. However, the low thermoelectric conversion efficiency is not sufficient to achieve energy autonomy in the operation of wearable devices. Herein, wearable bacterial cellulose (BC) organogel-based thermoelectrochemical cells (TECs) are designed and prepared with K₄Fe(CN)₆/K₃Fe(CN)₆ as a redox couple. The addition of propylene glycol significantly improves the mechanical properties of the TECs and drives K₄Fe(CN)₆ to gradually crystallize, resulting in the concentration gradient of redox ions, which significantly enhanced the heat-to-electricity conversion efficiency (from 1.27 to 2.30 mV K⁻¹), proving that they are promising candidates for powering flexible and wearable devices in various application scenarios. The TECs are further assembled into self-powered strain sensors, which can detect the movement of the human body under various tensions and pressures in real time with high sensitivity. This indicates that the BC organogel-based TECs for self-powered strain sensors have great application potential in the wearable field.     

A wearable point-of-care system for home use that incorporates plug-and-play and wireless standards.

A point-of-care system for continuous health monitoring should be wearable, easy to use, and affordable to promote patient independence and facilitate acceptance of new home healthcare technology. Reconfigurability, interoperability, and scalability are important. Standardization supports these requirements, and encourages an open market where lower product prices result from vendor competition. This paper first discusses candidate standards for wireless communication, plug-and-play device interoperability, and medical information exchange in point-of-care systems. It then addresses the design and implementation of a wearable, plug-and-play system for home care which adopts the IEEE 1073 Medical Information Bus (MIB) standards, and uses Bluetooth as the wireless communication protocol. This standards-based system maximizes user mobility by incorporating a three-level architecture populated by base stations, wearable data loggers, and wearable sensors. Design issues include the implementation of the MIB standards on microcontroller-driven embedded devices, low power consumption, wireless data exchange, and data storage and transmission in a reconfigurable body-area network.     

Energy-efficient,fast-settling,modified nested-current-mirror,single-stage-amplifier for high-resolution LCDs in 90-nm CMOS

The Internet of Things (IoT) presents opportunities to address a variety of systemic, metabolic healthcare issues. Cardiovascular disease and diabetes are among the greatest contributors to premature death worldwide. Wireless wearable continuous monitoring systems such as ECG sensors connected to the IoT can greatly decrease the risk of death related to cardiac issues by providing valuable long-term information to physicians, as well as immediate contact with emergency services in the event of a heart attack or stroke. In this report we discuss the fabrication, characterization and validation of composite fabric ECG sensors made from Nylon^® coated with reduced graphene oxide (rGO_x) as part of a self-powered wearable IoT sensor. We utilize an electronic probing station to measure electrical properties, take live ECG data to measure signal reliability, and provide detailed surface characterization through scanning electron microscopy. Finally, bonding between the layers of the composite and between composite and the Nylon^® is analyzed by Fourier transform Infrared spectroscopy. Furthermore, a low power analog front end circuit designed in 65 nm CMOS process is presented to interface the sensor with a system on chip used in a wearable IoT healthcare device.     

A low power wireless node for contact and contactless heart monitoring

Ubiquitous vital signs sensing and processing are promising alternatives to conventional clinical and ambulatory healthcare. Novel sensors, low power solutions for processing and wireless connectivity are creating new opportunities for wearable devices which allow continuous and long term monitoring, while maintaining freedom of movement for the users. This paper presents a low-power embedded platform with novel high sensitivity electric potential dry surface sensors that can be used in either contact or non-contact mode to measure biomedical signals. The proposed low power system is optimized to compute the heart rate and respiratory rate close to the sensors. This approach reduces the amount of data that needs to be transmitted to a host device. It allows also the platform to be autonomous and wearable or even be used in cars for applications such as driver drowsiness detection. Experimental measurements show the acquisition and the processing of data from sensors and the low power consumption achieved with the node in different modes of operation.