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首页> 外文会议>International Conference on Distributed Computing in Sensor Systems >A Gesture Recognition approach to classifying Allergic Rhinitis gestures using Wrist-worn Devices : a multidisciplinary case study
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A Gesture Recognition approach to classifying Allergic Rhinitis gestures using Wrist-worn Devices : a multidisciplinary case study

机译：使用腕戴式设备对过敏性鼻炎手势进行分类的手势识别方法：多学科案例研究

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In this paper, we propose a multidisciplinary Gesture Recognition case study using a Machine Learning approach for the detection and classification of allergic rhinitis-related gestures. Allergic diseases and especially allergic rhinitis are among the most common diseases in the world, mostly underappreciated, causing considerable impairment of daily activities, including job, and school productivity. For this reason, close monitoring and early recognition of symptoms worsening are considered essential. We hypothesize that recognizing allergic rhinitis to patients by such an approach may be a useful tool for such purpose.In our study, for the first time, the most common allergic rhinitis gestures are identified, based on patients’ description and specialists’ experience. Our data is retrieved by a large pool of active allergic rhinitis patients attending three specialized outpatient clinics in Greece. Gestures are recorded with the help of a wristband Bluetooth device incorporating a 3-axis accelerometer and a 3-axis gyroscope. Feature engineering and several signal processing methods are then applied to the raw sensor data (which are treated as 6-dimensional signals), and valuable features are extracted related to the time and frequency domains.To improve the performance of the Machine Learning models, we utilize Principal Component Analysis (PCA), and we also use functions such as Grid Search and Randomized Search, in order to achieve higher recognition accuracy by hyperparameter optimization.With these features and steps of processing, we built a classifier that can uniquely identify 15 allergic rhinitis gestures with an accuracy of 93% in a challenging variety of moves in the patient’s head (nose, eye, ear). It is worth noting that allergic rhinitis gestures are more subtle, varied and spontaneous than other moves that have been considered in the literature so far. To the best of our knowledge, this is the first time that a Machine Learning approach is successfully applied in such a challenging field like respiratory diseases.
机译：在本文中，我们提出了使用机器学习方法进行多学科手势识别的案例研究，用于检测和分类与过敏性鼻炎相关的手势。过敏性疾病，尤其是过敏性鼻炎，是世界上最常见的疾病，大多未被充分认识，从而导致包括工作和学校生产力在内的日常活动受到相当大的损害。因此，必须密切监视并尽早发现症状恶化。我们假设通过这种方法识别患者的过敏性鼻炎可能是实现此目的的有用工具。在我们的研究中，首次根据患者的描述和专家的经验来识别出最常见的过敏性鼻炎手势。我们的数据是通过在希腊的三个专业门诊就诊的大量活跃的过敏性鼻炎患者来检索的。借助带3轴加速度计和3轴陀螺仪的腕带蓝牙设备，可以记录手势。然后将特征工程和几种信号处理方法应用于原始传感器数据（被视为6维信号），并提取与时域和频域相关的有价值的特征。利用主成分分析（PCA），我们还使用了Grid Search和Randomized Search之类的功能，以通过超参数优化获得更高的识别准确度。借助这些功能和处理步骤，我们构建了可以唯一识别15种过敏源的分类器。鼻炎手势在患者头部（鼻子，眼睛，耳朵）的各种挑战性动作中的准确度达到93％。值得注意的是，过敏性鼻炎的姿势比迄今为止在文献中考虑的其他动作更加微妙，多样和自发。据我们所知，这是第一次将机器学习方法成功应用于呼吸系统疾病这样具有挑战性的领域。

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