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This paper evaluates a set of computational algorithms for the automatic estimation of human postures and gait properties from signals provided by an inertial body sensor. The use of a single sensor device imposes limitations for the automatic estimation of relevant properties, like step length and gait velocity, as well as for the detection of standard postures like sitting or standing. Moreover, the exact location and orientation of the sensor are also a common restriction that is relaxed in this study.Based on accelerations provided by a sensor, known as the ‘9×2’, three approaches are presented extracting kinematic information from the user motion and posture. First, a two-phases procedure implementing feature extraction and support vector machine based classification for daily living activity monitoring is presented. Second, support vector regression is applied on heuristically extracted features for the automatic computation of spatiotemporal properties during gait. Finally, sensor information is interpreted as an observation of a particular trajectory of the human gait dynamical system, from which a reconstruction space is obtained, and then transformed using standard principal components analysis, finally support vector regression is used for prediction.Daily living activities are detected and spatiotemporal parameters of human gait are estimated using methods sharing a common structure based on feature extraction and kernel methods. The approaches presented are susceptible to be used for medical purposes.  相似文献
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人体的运动姿态检测和行为分析具有广泛应用价值.设计了一种基于足底压力的人体姿态检测和行为分析系统,系统由压力采集模块、数据处理模块、无线通信模块和行为分析软件组成.姿态检测系统获取足底压力数据,采用蓝牙方式进行数据通信,行为分析软件使用支持向量机多分类方法实现坐、站、走、跑和爬楼5个经典人体姿态的区分.实验证明该系统对人体姿态具有较好的识别精度和可靠性,可以用于人体姿态检测和行为分析.  相似文献
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