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Design of wearable and portable physiological parameter monitoring system for attentiveness evaluation

机译:用于注意力评估的可穿戴和便携式生理学参数监测系统的设计

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With the rapid development of information technology, the "human-machine collaboration" smart education model is emerging increasingly. Aiming at addressing the problems of poor portability of the devices, single sort of physiological signals, and excessively subjective evaluation of attentiveness in current monitoring systems, this paper designed an attentiveness evaluation system based on multiple physiological information. First of all, in view of the large volume of traditional acquisition devices, we designed the miniaturized, wearable multi-physiological signal acquisition node. Based on a single-channel EEG signal analog acquisition front-end, 9-axis acceleration acquisition chip and blood oxygen (SpO2) acquisition module, we acquired the EEG, posture and SpO2 signals synchronously. Secondly, in the light of the bandwidth and power consumption of information transmission in the wireless body area network, we designed a data transmission networking based on wireless radio frequency Wi-Fi, achieving high-speed signal communication with high accuracy. The attentiveness induction experiment was designed, and an objective evaluation index of attentiveness based on reaction time and accuracy rate for regression analysis and fitting was put forward. After preprocessing the raw data, a variety of features were extracted, and the performance of the attentiveness evaluation was verified. Results show that the accuracy rate of the attentiveness is up to 77.1%, which realizes the effective evaluation of attentiveness.
机译:随着信息技术的快速发展,“人机合作”智能教育模式正在越来越涌现。旨在解决设备可移植性差,单一的生理信号,以及当前监测系统中的注意力的过度主观评估,本文设计了基于多种生理信息的关注评估系统。首先,鉴于大量的传统采集装置,我们设计了小型化,可穿戴多生理信号采集节点。基于单通道EEG信号模拟采集前端,9轴加速度采集芯片和血氧(SPO2)采集模块,我们同步地获取了EEG,姿势和SPO2信号。其次,鉴于无线体积网络中信息传输的带宽和功耗,我们设计了基于无线射频Wi-Fi的数据传输网络,实现了高精度的高速信号通信。提出了基于反应时间和回归分析和配件的反应时间和准确率的注意力诱导实验,并提出了基于反应时间和准确率的客观评价指标。在预处理原始数据后,提取了各种特征,并验证了关节评估的性能。结果表明,细心的准确率高达77.1%,实现了对细心的有效评估。

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