PDF(Pubmed)
Abstract：
Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.
摘要：
使用髋关节角度的步态监测为人员识别提供了一种有前途的方法，利用智能手机惯性测量单元(IMU)的功能。这项研究调查了使用智能手机IMU提取髋关节角度，以根据步态模式区分个体。数据来自10名健康受试者(8名男性，2只雌性）以4km/h的速度在跑步机上行走10分钟。一种传感器融合技术，结合了加速度计，陀螺仪,磁强计数据用于推导有意义的髋关节角度。我们在WEKA环境中采用了各种机器学习算法，根据髋关节模式对受试者进行分类，分类准确率达到88.9%。我们的研究结果表明，使用髋关节角度进行人员识别的可行性，为未来生物识别应用的步态分析研究提供基线。这项工作强调了基于智能手机的步态分析在个人识别系统中的潜力。
