论文标题
通过利用智能手机传感器信号来识别体育活动
Physical Activity Recognition by Utilising Smartphone Sensor Signals
论文作者
论文摘要
人体运动活动识别在各个领域都有许多潜在的应用，例如医学诊断，军事传感，体育分析和人类计算机安全互动。随着智能手机和可穿戴技术的最新进展，这种设备具有嵌入运动传感器甚至能够感知小体动运动的嵌入式运动传感器已变得很普遍。这项研究在两个不同的日子里收集了来自60名参与者的人类活动数据，共有陀螺仪和加速度计传感器在现代智能手机中记录的六个活动。该论文通过使用大多数算法投票等方法来利用机器学习算法来确定不同的活动。还提供了更多分析，以揭示哪些时间和频域的特征最能识别个人运动活动类型。总体而言，拟议的方法在确定四个不同的活动时达到了98％的分类精度：在楼上行走，在楼下散步，坐在楼下，而受试者镇定并进行典型的基于桌子的活动。
Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain based features were best able to identify individuals motion activity types. Overall, the proposed approach achieved a classification accuracy of 98 percent in identifying four different activities: walking, walking upstairs, walking downstairs, and sitting while the subject is calm and doing a typical desk-based activity.
