目前針對帕金森氏症患者的運動症狀量化研究多以穿戴式裝置與電腦視覺為主，然而受制於硬體裝置，使用者無法享有非接觸式且具隱私的感知體驗，而WiFi通道狀態資訊將感知融入日常生活中，然而迄今為止，以WiFi通道狀態資訊感測帕金森氏症運動症狀量化研究仍寥寥無幾。
本研究致力於以WiFi通道狀態資訊基於菲涅耳場區理論的感知方法量化帕金森氏症患者手部顫抖症狀，目標為偵測靜止性顫抖之發生頻率與持續時間，探討當接收端呈單點與多點角落排列時，靜止性顫抖於典型居家環境中之量化準確度。當接收端呈單點排列時，平均動作頻率量化準確率達90.20%，平均持續時間頻率量化準確率達95.20%；當接收端呈多點角落排列時，平均動作頻率量化準確率達91.38%，平均持續時間頻率量化準確率達95.72%。
根據文獻回顧，與靜止性顫抖相似的微小動作，僅能於菲涅耳場區中線被明顯偵測，本研究運用帶通濾波器，消除大部分的環境干擾，使得靜止性顫抖得以在整個實驗空間中被偵測。透過接收端呈多點角落排列偵測靜止性顫抖症狀的實驗，證明多個菲涅耳場具有對稱性，且於空間中擺放多組收發設備可有效提升平均量化準確率。
不論患者在診間與否，醫院都能即時掌握患者情況，不僅提供醫師更全面的診斷依據，亦能隨時依據患者身心狀況調整處方箋，增進帕金森氏症患者的生活品質與身心健康，打造安心宜人的智慧型居家健康監測環境。

To provide physicians an objective way to consistently assess the severity of Parkinson's disease (PD), this study developed an approach to quantify the motor symptom via Wi- Fi channel state information (CSI). Our computer algorithm computed the frequency and duration of simulated hand tremors from the CSI data with an average accuracy of 90.94% and standard deviation of 3.53% by the spot arrangement of receiver.

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