A wearable device for physical and emotional health monitoring
A wearable device for physical and emotional health monitoring
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用于身体和情绪健康监测的可穿戴设备
DOI：
10.1109/cic.2015.7408601
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
David Atienza Alonso
中科院分区：
文献类型：
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作者：
S. Murali;F. Rincón;David Atienza Alonso
个人健康监测系统正在成为有前途的解决方案，这些解决方案可以开发超小型的便携式设备，这些设备可以连续监视和处理几个重要的身体参数。在这项工作中，我们提出了一种可穿戴设备，用于身体和情感健康监测。该设备获得了用户的关键生理信号：ECG，呼吸，阻抗心脏图（ICG），血压和皮肤电导，并导致用户的情绪状态。我们已经开发了嵌入的算法，这些算法可以实时处理生物信号，以检测ECG中的任何异常（心律不齐和形态变化），并从ICG中检测关键参数（例如前射精和流体状态水平） 。我们提出了一种新的方法，可以检测来自ECG和ICG信号的连续节拍血压，以及一种计算用户情绪水平的实时嵌入情感分类器。情绪根据其吸引力（积极价）或水平价维度的厌恶性（负价）进行分类。情绪引起的兴奋水平由价值空间的垂直唤醒维度高至低位置表示。通过使用胸带进行长期监测，通过触摸设备上的金属电极（用于护理点测试）或连续触摸金属电极（用于护理点测试），以间歇性测量信号。使用蓝牙低能协议将来自设备的处理后数据发送到手机。我们的结果表明，该设备可以连续监视信号，从而在单个电池充电中准确地检测运动状态72小时以上。
Personal health monitoring systems are emerging as promising solutions to develop ultra-small, portable devices that can continuously monitor and process several vital body parameters. In this work, we present a wearable device for physical and emotional health monitoring. The device obtains user's key physiological signals: ECG, respiration, Impedance Cardiogram (ICG), blood pressure and skin conductance and derives the user's emotion states as well. We have developed embedded algorithms that process the bio-signals in real-time to detect any abnormalities (cardiac arrhythmias and morphology changes) in the ECG and to detect key parameters (such as the Pre- Ejection Period and fluid status level) from the ICG. We present a novel method to detect continuous beat-by-beat blood pressure from the ECG and ICG signals, as well as a real-time embedded emotion classifier that computes the emotion levels of the user. Emotions are classified according to their attractiveness (positive valence) or their averseness (negative valence) in the horizontal valence dimension. The excitement level induced by the emotions is represented by high to low positions in the vertical arousal dimension of the valence-arousal space. The signals are measured either intermittently by touching the metal electrodes on the device (for point-of-care testing) or continuously, using a chest strap for long term monitoring. The processed data from device is sent to a mobile phone using a Bluetooth Low Energy protocol. Our results show that the device can monitor the signals continuously, providing accurate detection of the motion state, for over 72 hours on a single battery charge.