背景：帕金森氏症是一種不間斷的神經退化性疾病。除了導致無意的運動，即震顫，也會使患病者有運動上的困難，如僵硬、遲緩。甚至導致一些步態、平衡等協調性問題。正確的治療能明顯改善運動能力與生活品質，因此量化症況是至關重要的。這篇論文的重點在於統一帕金森氏評定表 (UPDRS) 的姿勢性顫抖測試中，對微小非自主動作進行基於影像的量化分析。目前已有研究應用姿勢估計技術進行運動分析。然而，在我們嘗試應用 OpenCV 標準方法的 OpenPose 中姿勢估計的結果於我們的案例當中產生不理想的結果。
目標：本論文的目標是將深度特徵編碼技術 (DFE) 納入姿勢估計，用於跟蹤人類手部運動，並嘗試提高追蹤精確度。
方法： DFE 是由 Chang 與 Nordling 等人於 2021 年提出的技術，他們在人臉資 料訓練一個自動編碼器。DFE 技術可以達到亞像素等級的皮膚特徵追蹤。此處，我們重現 DFE 技術並以手部資料進行訓練。並且我們考慮了膚色過濾與時間訊息兩項特徵以提高跟蹤精確度。姿勢估計是通過跟蹤手部的 21 個關鍵 點來完成。為了評估姿勢估計的精確度與影像品質的關係，我們將高解析度的影像添加高斯模糊或高斯噪音或將解析度減以破壞影像品質。
結果與結論: 在我們預先錄製好的 8 個驗證影片中的 40 個手動標籤中，重新訓 練的 DFE 的平均誤差 (MAE) 是 1.26 像素，而 OpenPose 是 12.47 像素。其中，我們發現在一關鍵點沒有明顯的特徵，使得它們比具有明顯特徵的關鍵點相比更難以有正確的追蹤。因此，比起挑選最突出的特徵相比，若要以如同姿勢估計中使用固定關鍵點的進行追蹤，將會有更高的追蹤誤差。

Background: Parkinson’s disease is a progressive neurodegenerative disorder that causes both unintentional movements, i.e. tremors, and difficulty to carry out movements, such as stiffness, slowness and gait, balance, and coordination problems. Correct treatment significantly improve the ability to move as intended and life quality, thus symptom quantification is essential. This thesis focus on camera based quantification of small involuntary movements, so called micromovements, during the postural tremor test in the Unified Parkinson’s Disease Rating Scale (UPDRS). Previously, pose estimation has been applied successfully to movement analysis in other applications, but OpenPose–the standard method for pose estimation in OpenCV–yielded unsatisfactory results in our case.
Aim: The goal of this thesis is to incorporate Deep Feature Encodings (DFE) in pose estimation for tracking of human hand movement and explore the precision. More accurate tremor tracking will allow us to better assist in the diagnosis of Parkinson’s disease in the future.
Method: DFE is an autoencoder trained on human faces to enable tracking of skin features with sub-pixel accuracy, introduced by Chang and Nordling (2021). Here it is retrained on human hands, and both skin filtering and temporal information is added to improve the tracking accuracy. The pose estimation is done by tracking 21 keypoints on the hand. To assess how the precision of the pose estimation depend on video quality, high quality videos are destructed by adding Gaussian blur or noise, or halving the resolution.
Results and conclusion: On a set of 40 manually labelled frames in 8 videos the mean absolute error (MAE) of the retrained DFE is 1.26 px, while it for OpenPose is 12.47 px. Some of the keypoints have no clear distinctive feature, which make them harder to track using DFE compared to keypoints with distinctive features. Thus the benefit of having fixed keypoints in pose estimation comes at the expense of increased MAE compared to picking the most salient features.

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