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The Head Posture System Based on 3 Inertial Sensors and Machine Learning Models: Offline Analyze

机译:基于3个惯性传感器和机器学习模型的头部姿势系统:离线分析

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The current paper proposes and presents a new wearable system for head posture recognition, based on three inertial sensors used to prevent inadequate head posture during different office daily activities. During this experiment, 9 daily office activities were evaluated. The proposed model distinguished between bad or good posture with a high accuracy using the inertial time series's raw data. The performance of the proposed wearable system was evaluated offline with the help of machine learning algorithms. The advantage of the proposed approach is the possibility of transmitting data through the Wi-Fi connection, portability, low cost, and high performance. During this experiment, the best classification performances it was obtained with Decision Extra Trees Classifier, that was achieved an accuracy equal to 96.78%.
机译:目前的论文提出并提出了一种新的可穿戴系统,用于头部姿势识别,基于用于防止在不同办公日常活动期间的头部姿势不足的惯性传感器。在此实验中,评估了9个日常办公室活动。所提出的模型以高精度与惯性时间序列的原始数据的高精度区分开来。在机器学习算法的帮助下,拟议可穿戴系统的性能进行了脱机。所提出的方法的优点是通过Wi-Fi连接,可移植性,低成本和高性能传输数据的可能性。在此实验期间,使用决策额外的树木分类器获得的最佳分类性能,这实现了等于96.78%的精度。

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