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Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location

机译:不论设备方向和位置如何,使用手机进行的准确活动识别

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This paper investigates two major issues in using a tri-axial accelerometer-embedded mobile phone for continuous activity monitoring, i.e. the difference in orientations and locations of the device. Two experiments with a total of ten test subjects performed six daily activities were conducted in this study: one with a device fixed on the waist in sixteen different orientations and another with three different device locations (i.e., shirt-pocket, trouser-pocket and waist) in two different device orientations. For handling with varying device orientations, a projection-based method for device coordinate system estimation has been proposed. Based on the dataset with sixteen different device orientations, the experimental results have illustrated that the proposed method is efficient for rectifying the acceleration signals into the same coordinate system, yielding significantly improved activity recognition accuracy. After signal transformation, the recognition results of signals acquired from different device locations are compared. The experimental results show that when the sensor is placed on different rigid body, different models are required for certain activities.
机译:本文研究了使用嵌入式三轴加速度计的手机进行连续活动监控的两个主要问题,即设备方向和位置的差异。在这项研究中,进行了总共十名受试者的两项实验,每天进行六项日常活动:一项以十六种不同方向将设备固定在腰上,另一项以三种不同的设备位置(即衬衫口袋,裤子口袋和腰部)进行)以两种不同的设备方向定位。为了以不同的设备方向进行处理,已经提出了一种基于投影的设备坐标系估计方法。基于具有十六种不同设备方向的数据集,实验结果表明,该方法可有效地将加速度信号校正为同一坐标系,从而显着提高了活动识别的准确性。信号转换后,比较从不同设备位置获取的信号的识别结果。实验结果表明,当传感器放置在不同的刚体上时,某些活动需要使用不同的模型。

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