共查询到18条相似文献,搜索用时 468 毫秒
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针对传统人体跌倒检测方法准确度低,不能在人体疑似跌倒的第一时间及时检测的问题,提出基于智能视觉的人体跌倒检测方法。根据智能视觉分析技术解析人体跌倒行为,采用加速度传感器采集人体跌倒惯性特征数据并利用加速度传感器建立三轴加速坐标,对跌倒行为作出判断。在巨大的特征量集合中,运用K-L变换方法提取出准确的加速度峰值和倾角变化值,据此设置跌倒行为检测的约束条件,完成对跌倒行为的分类。采用PSO分类器优化人体跌倒检测的SVM参数,完成人体跌倒高精准度检测。实验结果表明,所提方法的检测准确度高于对比的3种文献方法,检测时间最短,能够及时检测目标个体跌倒情况,可广泛应用于现实生活中。 相似文献
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为减少跌倒对老年人造成的伤害,并对跌倒进行实时检测,提出了一种基于Android智能手机的人体跌倒检测系统,手机安置于腰上采集手机加速度传感器数据,利用了姿态识别和跌倒检测相结合的算法,区分出跌倒行为和人体日正常常活动。当检测到异常跌倒时,报警信息以及从手机中GPS获取的位置被发送。仿真及实验表明:系统能够有效地识别出跌倒和日常行为,算法具有较高实时性、具有较高灵敏度和特异度。 相似文献
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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。 相似文献
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随着全球人口老龄化加剧,独自外出的老年人和独居老人跌倒的发生率很高,老年人跌倒会带来很多的安全隐患,不及时得到救助会带来严重的后果甚至危及生命。所以我们需要实时监测老年人是否发生了跌倒,如果检测到跌倒,系统就要迅速发出报警信息向周围人和监护人求助。本文设计了一款具有人体跌倒监测功能的装置。系统以STC89C52单片机为核心控制器,利用ADXL345加速度传感器实现对人体姿态的实时监控,ADXL345加速度传感器采集人体静态时的倾角数据和人体动态时的重力加速度数据,将采集到的数据通过LCD1602液晶屏显示。当系统检测到人体的倾斜度大于45度,且三个正交方向上产生的合加速度大于20时,则判断老年人为跌倒状态。判断人体处于跌倒状态后,单片机会驱动蜂鸣器工作发出警报声从而提醒附近人员对其提供帮助,系统也会通过GSM通信模块向监护人发送警报信息以此提醒监护人。 相似文献
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《计算机应用与软件》2017,(1)
为减少跌倒对人体造成的伤害,采用一种基于支持向量机的人体跌倒检测方法。利用安置于腰上的手机采集人体运动行为加速度数据,提取对跌倒行为敏感的时域及频域特征,利用奇异值分解方法降维特征和重构跌倒特征,采用支持向量机分类器检测跌倒行为。仿真实验表明:该方法能够有效地识别跌倒和日常行为,具有较高灵敏度和特异度,并可同时提高识别正确率。 相似文献
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利用三轴加速度传感器进行人体行为识别一直是传感器数据处理、模式识别领域的研究热点。加速度数据往往存在着多种动作数据难以区分的情况,特别是走、上楼、下楼这3个动作数据非常相似,这给正确识别这3种人体动作带来了较大的难度。提出一种基于特征增强与决策融合的行为识别方法,通过对部分特征值进行增强处理和对多个分类结果进行决策融合来识别走、上楼、下楼这些难以区分的相似动作。实验验证,所提方法可克服由于加速度数据的相似性而导致的动作识别正确率低、识别误差大的情况,有效提高人体行为识别率,且可在实际应用中实时识别人体行为动作。 相似文献
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A hierarchical human activity recognition (HAR) system is proposed to recognize abnormal activities from the daily life activities of elderly people living alone. The system is structured to have two-levels of feature extraction and activity recognition. The first level consists of R-transform, kernel discriminant analysis (KDA), $k$ -means algorithm and HMM to recognize the video activity. The second level consists of KDA, $k$ -means algorithm and HMM, and is selectively applied to the recognized activities from the first level when it belongs to the specified group. The proposed hierarchical approach is useful in increasing the recognition rate for the highly similar activities. System performance is analyzed by selecting the optimized number of features, number of HMM states and the number of frames per second to achieve maximum recognition rate. The system is validated by a novel set of six abnormal activities; falling backward, falling forward, chest pain, headache, vomiting, and fainting and a normal activity walking. Experimental results show an average recognition rate of 97.1 % for all the activities by using the proposed hierarchical HAR system. 相似文献
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We propose a novel pervasive system to recognise human daily activities from a wearable device. The system is designed in a form of reading glasses, named ‘Smart Glasses’, integrating a 3-axis accelerometer and a first-person view camera. Our aim is to classify subject’s activities of daily living (ADLs) based on their vision and head motion data. This ego-activity recognition system not only allows caretakers to track on a specific person (such as disabled patient or elderly people), but also has the potential to remind/warn people with cognitive impairments of hazardous situations. We present the following contributions: a feature extraction method from accelerometer and video; a classification algorithm integrating both locomotive (body motions) and stationary activities (without or with small motions); a novel multi-scale dynamic graphical model for structured classification over time. In this paper, we collect, train and validate our system on two large datasets: 20 h of elder ADLs datasets and 40 h of patient ADLs datasets, containing 12 and 14 different activities separately. The results show that our method efficiently improves the system performance (F-Measure) over conventional classification approaches by an average of 20%–40% up to 84.45%, with an overall accuracy of 90.04% for elders. Furthermore, we also validate our method on 30 patients with different disabilities, achieving an overall accuracy up to 77.07%. 相似文献
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基于时序分析的人体摔倒预测方法 总被引:1,自引:0,他引:1
提出一种基于人体动作状态序列时序分析法的人体摔倒预测方法。融合特征部位加速度信息为时间序列,选取摔倒过程中人体与低势物体碰撞前的过程序列段作为样本训练隐马尔可夫模型(HMM),通过分析输入序列与HMM的匹配程度实时分析当前时刻人体摔倒的风险。实验证明该方法取得良好的预测效果,并且可有效区分摔倒过程与其它日常生活行为过程。 相似文献
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Daniel Rodriguez-Martin Albert Samà Carlos Perez-Lopez Andreu Català Joan Cabestany Alejandro Rodriguez-Molinero 《Expert systems with applications》2013,40(18):7203-7211
Analysis of human body movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson’s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications. 相似文献
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D. FuentesL. Gonzalez-Abril C. AnguloJ.A. Ortega 《Expert systems with applications》2012,39(3):2461-2465
This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy. 相似文献
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Roberto PaoliFrancisco J. Fernández-Luque Ginés DoménechFélix Martínez Juan Zapata Ramón Ruiz 《Expert systems with applications》2012,39(5):5566-5575
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. 相似文献
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In recent decades, decreasing physical activity has emerged as one of the major issues affecting human health since people increasingly engaged in sedentary behavior in their homes and workplaces. In physical activity research, using GPS trajectories and advanced GIS methods has a potential for greatly enhancing our understanding of the association between objectively measured moderate and vigorous physical activity and physical and social environments. Relying only on objectively measured physical activity intensity, however, ignores the role of different places and types of physical activity on people's health outcomes. The aim of this study is to propose an approach to classifying physical activity in free-living conditions for physical activity research using published smartphone accelerometer data. Random forest and gradient boosting are used to predict jogging, walking, sitting, and standing. Generated training models based on the two classifiers are tested on accelerometer data collected from the smartphones of two subjects in free-living conditions. GPS trajectories with predicted physical activity labels are visually explored on a map to offer new insight on the assessment of the predicted results of daily activities and the identification of any difference in the results between random forest and gradient boosting. The findings of this study indicate that random forest and gradient boosting enable accurate physical activity classification in free-living conditions. GPS trajectories linked with predicted labels on a map assist the visual exploration of the erroneous prediction in daily activities including in-vehicle activities. 相似文献