基于Kinect体感传感器的老年人跌倒自动检测

跌倒是独居老人最主要的意外风险之一,为快速有效获取跌倒信息,使老年人得到及时救助,提出一种基于Kinect体感传感器的人体跌倒自动检测方法,利用Kinect深度图像技术获取人体深度图像前景图,建立前景图三维包围盒,通过实时计算的三维包围盒的长、宽、高数值以及该数值的变化速度,判断人体跌倒是否发生。利用遮挡融合算法,解决了人体躯干被障碍物部分遮挡时,跌倒事件的检测和判定。在室内居家环境下进行了26种测试场景实验,检测误报率为2.0%~6.0%,漏报率为0~4.0%。该方法可以较为准确地实现人体跌倒自动检测。     

基于新颖性检测的跌倒风险预测

跌倒是65岁及以上人群因伤害致死的第一位原因.结合受试者个体信息的个性化特征,提出一种基于Kinect三维骨架数据的步态特征提取方法,对老年人的跌倒风险进行评估和预测.将跌倒风险分为高跌倒风险和低跌倒风险两类,考虑数据采集的成本问题,采用新颖性检测模型在不平衡数据集下对特征数据进行训练和评估.实验结果表明,OC-SVM...     

基于特征自动提取的跌倒检测算法

跌倒是导致老年人受伤甚至死亡的主要原因。准确及时的跌倒检测系统可以帮助跌倒者获得紧急救援。 目前基于传感器的跌倒检测方法主要利用人工设计提取的信号特征来区分跌倒和非跌倒运动,但人工提取的特征往往会限制算法的精确度,增大算法时延。为提高跌倒检测的精确度和实时性,本文提出了一种基于深度学习的跌倒检测算法。该算法可以自动提取数据特征,实现从原始数据到检测结果的端到端的处理。算法模型主要由两层级联的长短期记忆（Long Short-Term Memory, LSTM）循环神经网络组成,通过神经网络提取加速度计和陀螺仪数据内部的特征,并判断是否有跌倒状况发生。我们使用两个公开数据集MobiAct和SisFall对算法性能进行评估。 实验结果显示,算法在两个数据集都达到了较高的精确度（99.58%以上）和较低的时延（2.2毫秒以内）。     

基于集员滤波的双Kinect人体关节点数据融合

以Kinect为代表的深度图像传感器在肢体康复系统中得到广泛应用.单一深度图像传感器采集人体关节点数据时由于肢体遮挡、传感器数据错误和丢失等原因降低系统可靠性.本文研究了利用两台Kinect深度图像传感器进行数据融合从而达到消除遮挡、数据错误和丢失的目的,提高康复系统中数据的稳定性和可靠性.首先,利用两台Kinect采集患者健康侧手臂运动数据;其次,对两组数据做时间对准、Bursa线性模型下的坐标变换和基于集员滤波的数据融合;再次,将融合后的健康侧手臂运动数据经过“镜像运动”作为患侧手臂运动指令;最后,将患侧运动指令下发给可穿戴式镜像康复外骨骼带动患者患侧手臂完成三维动画提示的康复动作,达到患者主动可控康复的目的.本文通过Kinect与VICON系统联合实验以及7自由度机械臂控制实验验证了数据融合方法的有效性,以及两台Kinect可有效解决上述问题.     

基于二次判断的无线多传感器跌倒监测系统

通过研究跌倒事件,设计了一种基于多传感器的穿戴式跌倒监测系统.将加速度传感器、磁传感器和压力传感器相结合,采集相互独立的实时数据,并利用阈值和表决算法进行二次判断来提高系统的跌倒识别率.系统实时监测跌倒事件,并根据报警设置提醒误判或者通知家属以得到及时救助.实验结果表明,该系统有较高的识别率和可靠性,适合应用于跌倒监测系统.     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

基于ROS与Kinect的移动机器人同时定位与地图构建

同时定位与地图构建(SLAM)技术一直以来都是移动机器人实现自主导航和避障的核心问题,移动机器人需要借助传感器来探测周围的物体同时构建出相应区域的地图。由于传统的1D和2D传感器,如超声波传感器、声呐和激光测距仪等在建图过程中无法检测出Z轴(垂直方向)上的信息,易增加机器人发生碰撞的概率,同时影响建图结果的精确度。本文利用Kinect作为机器人SLAM的传感器,将其采集到的三维信息转化成二维的激光数据进行地图构建,同时借助机器人操作系统(robot operating system,ROS)进行仿真分析和实际测试。结果表明Kinect可以弥补1D和2D传感器采集信息的不足,同时能够较好的保持建图的完整性和可靠性,适用于室内的移动机器人SLAM实现。     

A system for ubiquitous fall monitoring at home via a wireless sensor network and a wearable mote

Accidental falls of our elderly, and physical injuries resulting, represent a major health and economic problem. Falls are the most common cause of serious injuries and are a major health threat in the stratum of older population. Early detection of a fall is a key factor when trying to provide adequate care to elderly person who has suffered an accident at home. Therefore, the detection of falls in the elderly remains a major challenge in the field of public health. Specific actions aimed at the fall detection can provide urgent care which allows, on the other hand, drastically reduce the cost of medical care, and improve primary care service. In this paper, we present a support system for detecting falls of an elder person by the combination of a wearable wireless sensor node based on an accelerometer and a static wireless non-intrusive sensory infrastructure based on heterogeneous sensor nodes. This previous infrastructure called DIA (Dispositivo Inteligente de Alarma, in Spanish) is an AAL (Ambient Assisted Living) system that allows to infer a potential fall. This inference is reinforced for prompt attention by a specific sensorisation at portable node sensor in order to help distinguish between falls and daily activities of assisted person. The wearable node will not determine a falling situation, it will advice the reasoner layer about specific acceleration patterns that could, eventually, imply a falling. Is at the higher layer where the falling is determined from the whole context produced by mesh of fixed nodes. Experimental results have shown that the proposed system obtains high reliability and sensitivity in the detection of the fall.     

基于视频和人体姿态估计的老年人摔倒监测研究

人口老龄化所带来的养老服务问题是现代社会面临的严重问题。例如在很多国家跌倒是造成老年人因伤致死的最大原因,因此如何对老年人进行自动摔倒监测就成为养老服务亟待解决的问题。目前,在室内摔倒监测领域中,基于可穿戴设备和基于环境传感器等主流摔倒监测方法面临着设备复杂、成本较高等问题。鉴于此,将人体姿态估计引入摔倒监测领域,提出了一种基于2D视频的摔倒监测算法。首先利用OpenPose数据集提取原始数据中人体关节的位置;其次利用这些具有增强特征的数据构建静态分类模型和动态分类模型;最后,在3个公共摔倒数据集上进行模型训练和摔倒监测的测试,取得了较好的效果,可以为摔倒监测相关研究提供一定的参考。     

Fusing multiple image transformations and a thermal sensor with kinect to improve person detection ability

This paper proposes a novel approach to combine data from multiple low-cost sensors to detect people in a mobile robot. Robust detection of people is a key capability required for robots working in environments with people. Several works have shown the benefits of fusing data from complementary sensors. The Kinect sensor provides a rich data set at a significantly low cost, however, it has some limitations for its use on a mobile platform, mainly that people detection algorithms rely on images captured by a static camera. To cope with these limitations, this work is based on the fusion of Kinect and a thermical sensor (thermopile) mounted on top of a mobile platform. We propose the implementation of an evolutionary selection of sequences of image transformation to detect people through supervised classifiers. Experimental results carried out with a mobile platform in a manufacturing shop floor show that the percentage of wrong classified using only Kinect is drastically reduced with the classification algorithms and with the combination of the three information sources. Extra experiments are presented as well to show the benefits of the image transformation sequence idea here presented.     

Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair

In this study, design and implementation of a multi sensor based brain computer interface for disabled and/or elderly people is proposed. Developed system consists of a wheelchair, a high-power motor controller card, a Kinect camera, electromyogram (EMG) and electroencephalogram (EEG) sensors and a computer. The Kinect sensor is installed on the system to provide safe navigation for the system. Depth frames, captured by the Kinect’s infra-red (IR) camera, are processed with a custom image processing algorithm in order to detect obstacles around the wheelchair. A Consumer grade EMG device (Thalmic Labs) was used to obtain eight channels of EMG data. Four different hand movements: Fist, release, waving hand left and right are used for EMG based control of the robotic wheelchair. EMG data is first classified using artificial neural network (ANN), support vector machines and random forest schemes. The class is then decided by a rule-based scheme constructed on the individual outputs of the three classifiers. EEG based control is adopted as an alternative controller for the developed robotic wheelchair. A wireless 14-channels EEG sensor (Emotiv Epoch) is used to acquire real time EEG data. Three different cognitive tasks: Relaxing, math problem solving, text reading are defined for the EEG based control of the system. Subjects were asked to accomplish the relative cognitive task in order to control the wheelchair. During experiments, all subjects were able to control the robotic wheelchair by hand movements and track a pre-determined route with a reasonable accuracy. The results for the EEG based control of the robotic wheelchair are promising though vary depending on user experience.     

基于穿戴式平台的老人摔倒监测系统设计

针对老年人跌倒伤害预防问题,基于人体躯域网络可穿戴检测平台,设计了一种人体摔倒生理状态检测系统.系统主要包括摔倒状态检测模块,人体生理状态检测模块,GPS定位模块以及远程监护模块等.当老年人摔倒发生时,摔倒状态检测模块通过三轴加速度传感器检测,确认摔倒后立刻与远程监护平台通信,告知监护人,并通过穿戴式生理状态检测模块实时监测其心率信息,利用GPS定位,通过无线通信的方式将摔倒位置以及生理信息实时反馈给监护人.实验结果表明:该系统可以有效监测老人摔倒状态和生理状态,对及时救助有很大的帮助,具有良好的社会意义.     

基于SE-CNN的人体摔倒检测方法

人口老龄化是当今社会发展不可忽视的问题,目前有很大一部分老年人在无人照顾的境况下独自生活,摔倒后无法及时得到救助成为威胁老人生命安全的重要原因之一。现有的人体摔倒检测方法存在适应性差、高入侵性、易误判、成本昂贵等问题,且无法快速、实时检测老人摔倒。提出一种基于机器学习和无线传感器网络的摔倒检测方法,使用多个物联网传感节点组建无线传感器网络采集RSS数据,对采集到的RSS数据进行预处理后,通过XGBoost模型对时域特征分量和小波域特征分量进行处理,并以排列组合方式得到具有强鲁棒性的联合特征分量。利用深度学习网络获得数据潜在规律的特点构建人体摔倒识别模型,采用卷积神经网络作为主干网络,并在相邻网络层之间引入通道注意力模块,通过构建SE-CNN模型实现人体摔倒检测。实验结果表明,联合特征的加入能够提高RSS数据的可区分性,且SE-CNN模型的识别准确率高于CNN模型,可以实现高准确率的人体摔倒检测。     

Integrating the enriched feature with machine learning algorithms for human movement and fall detection

Movement detection is gaining more and more attention among various pattern recognition problems. Recognizing human movement activity types is extremely useful for fall detection for elderly people. Wireless sensor network technology enables human motion data from wearable wireless sensor devices be transmitted for remote processing. This paper studies methods to process the human motion data received from wearable wireless sensor devices for detecting different types of human movement activities such as sitting, standing, lying, fall, running, and walking. Machine learning methods K Nearest Neighbor algorithm (KNN) and the Back Propagation Neural Network (BPNN) algorithm are used to classify the activities from the data acquired from sensors based on sample data. As there are a large amount of real-time raw data received from sensors and there are noises associated with these data, feature construction and reduction are used to preprocess these raw sensor data obtained from accelerometers embedded in wireless sensing motes for learning and processing. The singular value decomposition (SVD) technique is used for constructing the enriched features. The enriched features are then integrated with machine learning algorithms for movement detection. The testing data are collected from five adults. Experimental results show that our methods can achieve promising performance on human movement recognition and fall detection.     

老年人运动状态监测和跌倒报警系统

体育锻炼是促进老年人健康长寿的有效手段之一。为了对老年人的运动状态进行实时监测,掌握运动状态参数,并能够对老年人不慎意外踏空或者某种疾病突发导致的跌倒及时报警,设计一种能够实时监测老年人跌倒动作发生并发送定位及报警信息给远程接收端的便携式监测系统。系统采用腰部三轴加速度传感器实时采集人体运动姿态数据;使用嵌入式处理器和无线网络实现数据处理、无线传输和远程报警;通过三级阈值的人体跌倒检测算法,实现人体跌倒姿态变化的加速度特征提取,对人体运动状态进行分级,预测严重的跌倒行为。实验结果表明：该系统具有性能稳定、正确率高和轻巧方便等特点,非常适合老年人穿戴使用,可保障老年人运动安全,应用前景广阔。     

基于物联网的养老院管理系统设计与实现

介绍基于物联网的养老院管理系统的各项功能,重点叙述RFID人员定位原理、摔倒检测原理和健康监测等传感器原理和实现。通过在RFID标签中录入老人的身份编号等信息,当老人携带标签路过某一布置了读卡器的路段时,系统便能确定该老人位置。运用重力加速传感器,采集加速度变化波形作为摔倒检测的依据。     

Capturing human motion using body‐fixed sensors: outdoor measurement and clinical applications

Motion capture is mainly based on standard systems using optic, magnetic or sonic technologies. In this paper, the possibility to detect useful human motion based on new techniques using different types of body‐fixed sensors is shown. In particular, a combination of accelerometers and angular rate sensors (gyroscopes) showed a promising design for a hybrid kinematic sensor measuring the 2D kinematics of a body segment. These sensors together with a portable datalogger, and using simple biomechanical models, allow capture of outdoor and long‐term movements and overcome some limitations of the standard motion capture systems. Significant parameters of body motion, such as nature of motion (postural transitions, trunk rotation, sitting, standing, lying, walking, jumping) and its spatio‐temporal features (velocity, displacement, angular rotation, cadence and duration) have been evaluated and compared to the camera‐based system. Based on these parameters, the paper outlines the possibility to monitor physical activity and to perform gait analysis in the daily environment, and reviews several clinical investigations related to fall risk in the elderly, quality of life, orthopaedic outcome and sport performance. Taking advantage of all the potential of these body‐fixed sensors should be promising for motion capture and particularly in environments not suitable for standard technology such as in any field activity. Copyright © 2004 John Wiley & Sons, Ltd.     

基于惯性传感技术的穿戴式老年人跌倒报警系统

现今老年人跌倒报警系统跌倒参照倾角方向单一,导致最终报警系统的误报率数值较大。针对这一不足,设计一种基于惯性传感技术的穿戴式老年人跌倒报警系统。硬件部分选择惯性传感器,设定传感器引脚功能,采用粘性电极安置在运动背心内部,设计穿戴式装置。软件部分选用SVM分类算法检测老年人跌倒的特征值,检测老年人跌倒状态,采用联合报警模式,构建一个三方向参照坐标转换过程,实现跌倒报警,完成对穿戴式老年人跌倒报警系统的设计。搭建实验环境,选取十位年轻人模拟老年人跌倒过程,分别使用两种传统老年人跌倒报警系统与设计跌倒报警系统进行实验,结果表明:设计的老年人跌倒报警系统误报率数值在1%左右,误报率数值最小。     

基于递归神经网络的跌倒检测系统

针对现有跌倒检测方法存在适应性差和功能较单一等问题，引入递归神经网络，通过发掘位置传感器数据之间的内在联系提高检测跌倒行为的效果。首先，设计了传感器、训练与检测输入数据的序列化表示方法，为发掘其中与跌倒和接近跌倒行为相关的内在关联提供了基础；接着，给出了用于跌倒检测的RNN训练算法以及基于RNN的跌倒检测算法，将跌倒检测转换为输入序列的分类问题；最后，在前期实现的基于分布式神经元大规模RNN系统的基础上，在Spark平台上实现了基于RNN的跌倒检测系统，使用Fall_adl_data数据集进行了测试与分析，验证了其能有效提高跌倒检测的准确率和召回率，F值相比现有跌倒检测系统提高12%和7%，同时能有效检测出接近跌倒的行为，有助于及时采取保护措施减少伤害。     

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-C$\mathbb{C}$WT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into “fall” and “ordinary activity” classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.