本研究將探討如何利用居家環境的Wi-Fi通道狀態資訊實現巴金森氏症患者日常生活中動作症狀之偵測。我們將設計實驗，透過受試者模擬手部靜止性顫抖、步態凝凍及組合動作等，對訊號進行分析並取得量化指標。
微小動作之呼吸偵測的最小值與最大值之平均絕對誤差為0.126和0.075秒，中位數與平均值為0.114和0.065秒；手部靜止性顫抖的最小值與最大值之平均絕對誤差為0.033和0.023秒，中位數與平均值為0.004和0.001秒，在評估顫抖持續時間之準確度為94.7%至97.9%；步調變化分為慢速步調與快速步調，慢速步調之平均絕對誤差為0.087至0.1秒，快速步調之平均絕對誤差為0.122至0.186秒；步態凝凍之平均絕對誤差為0.047至0.121秒，組合日常動作為結合手部靜止性顫抖和步態，在手部靜止性顫抖之最小值與最大值的平均絕對誤差為0.011和0.016秒；中位數與平均值為0.002和0.001秒，在評估顫抖持續時間之準確度為91.7%至99.3%，而行走步調之平均絕對誤差為0.03至0.055秒；透過靜態檢測以子載波曲線圖判別環境的改變與有無人存在；透過兩台路由器成功收取通道狀態資訊的資料，增加接收端設備的選擇，更接近可應用於居家物聯網環境的設定。
本研究透過制定多個不同情境對步態凝凍、步調變化和手部靜止性顫抖進行測試，驗證無線感測結果的精確度，並使用兩台路由器進行收取通道狀態資訊，增加接收端可使用的設備選項，使提出的系統方法具備可應用於巴金森氏症患者居家健康監測之潛在解決方案，可提供長時間、無隱私疑慮且客觀的參考數據，協助醫生診斷和即時精準調整病患的藥劑量。

This paper evaluated the use of WiFi channel state information (CSI) in implementing home monitoring of various motor symptoms for patients with movement disorders, such as Parkinson’s disease. The goal is to enable the physicians to see how the patient's symptoms change daily over a long period of time rather than a short observation during a clinical session.
We established the methods of retrieving Wi-Fi CSI and applied a low-pass filter and principal component analysis (PCA) to reduce the noise of data. Simulated breathing, resting tremor of hand, freezing of gait and mixed types of motion were performed in the experimental environment and quantified by our computer algorithm. We evaluated data collection of CSI via a PC and via a router.
From results of our Wi-Fi based sensing, the mean absolute error% was 2.1% for respiratory period, and the absolute error% for the total duration of respiration was 0.06% - 5.6%;for resting tremor of hand, the mean absolute error% for the period was 0.3%, and the absolute error for the total duration was 1.4% - 5.3%; in testing freezing of gait, the mean absolute error% for the cadence was 4.2% - 4.7% for slow walking, 10.7% - 14.7% for fast walking, and 3.4% - 9.1% for the freezing time. The proposed method could separate the time intervals of resting tremor of hand and walking from the data with mixed types of motion and achieve similar accuracy values of quantification. Besides, using a router as the receiver reached an accuracy of detection comparable to using a PC as the receiver.
This study demonstrated the feasibility of using WiFi CSI for home health monitoring by successfully sensing the different scales of human movements. The method can be further developed to provide objective and long-term health data to assist physicians make more precise medical decisions.

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