隨著人工智慧、雲端計算、物聯網和嵌入式系統的興起，智慧系統與應用大幅影響人們日常生活的層面，同時也為失智症患者創造了早期檢測與及時預防的機會。於現今老年化社會中，如何正確區分年長者認知狀態，往往是臨床醫師所面臨極大的挑戰。現今臨床多以紙本式的篩選測試並搭配認知系統測驗之腦波分析等方式來檢測失智症患者的狀態，然而，這些測試方法往往因為過長及繁複的測試內容，引起失智症患者的焦慮及恐懼的心理，進而降低了檢測的精準度。為了能提升失智檢測之精準度以及帶給失智症患者良好的檢測體驗，一套簡易的認知互動評估檢測工具是被需要的。
本論文分三個階段來呈現認知評估工具需具備的議題，並皆藉由量測結果及臨床實驗來展示其功能及可行性。首先，為了提升常見並用於年長者之電容式觸控面板的操作體驗，藉由卡爾曼濾波演算法，提出一基於指數函數之適應性演算法參數，在不額外增加硬體負擔且低運算資源之情況下，大幅降低電容式觸控面板因多點觸控或過快的移動操作所導致的回報率衰減與軌跡嚴重延遲的現象，實驗結果亦證明了所提出之適應性卡爾曼濾波演算法比起移動平均法及傳統卡爾曼濾波演算法之最小延遲軌跡均方根誤差皆小於0.5 mm且有最大75.75 % 之雜訊抑制表現，說明了該演算法於電容式觸控面板之良好效能與可行性。接著本論文亦提出一套基於OLED發光面板之認知互動系統，該系統透過修正式的克羅斯積木原理搭配柔和的OLED光源以按壓操作之遊戲式的方式給年長者進行測驗，此外該系統搭配雲端平台，能將每次年長者於測驗時之系統參數、執行時間與歷次表現等數據透過無線網路上傳，藉此提供醫師或心理師於遠端分析，實驗結果顯示於24名受測者中，所提出之系統在老年族群與年輕組群中具有顯著的空間工作記憶的差異F(1, 92) = 214, p<0.001，且年長的受測者在本系統的表現比起電腦版更提升了45.8%的空間記憶表現，更能提升認知評估的精準度，亦說明該認知系統之可行性。最後，本論文為探討克羅斯積木測驗之空間序列於臨床試驗中之標準與成效於檢測認知障礙，除了選取序列長度、序列路徑交點及路徑長度作為特徵外，更進一步擷取和量化本系統之序列路徑中每次的跳動距離、左右跳動及上下跳動次數作為分類的特徵輸入。階層式分群法、支持向量機與模糊邏輯系統分別被用來建構臨床克羅斯積木測驗之序列難易度。結果顯示，在300筆的資料中，在階層式分群法三個族群的分類下，可將難易度分為簡單、中等及困難，其三類對應受測者之表現分數具有顯著性的差異F(2, 297) = 29.06, p<0.001，同樣地，所採用的支持向量機在此三種分類類別的情況下，以受測者的相似表現分數(簡單:100~79分;中等78~63分;困難:62~0分)對特徵值建立難易度模型，具有87.3%之分類準確性，此外，為了更進一步量化序列難易度，模糊邏輯系統透過近似推論的方式把所擷取之語意變數特徵值轉化成量化之序列難易度值。結果亦表明，所輸出的難易度與受測者的表現分數中，皮爾森相關系數為中等負相關r = -0.407且有顯著的差異表現F(4, 295) = 13.01, p<0.001，驗證了序列難易度對記憶表現之影響。因此藉由本論文所提出之增進觸控回饋之演算法、互動式認知檢測系統及對空間認知檢測之難易度量化，不僅能大幅度改善現有平板式的認知測驗之體驗，更能夠減輕臨床醫師在檢測上的負擔及準確區分失智年長者認知狀態，為失智症患者帶來更完善之檢測及提早治療。

With the developed technology of Artificial Intelligence (AI), cloud computing, Internet of Things (IoT), and embedded systems, the applications in intelligent systems have greatly got involved in people's daily lives. By doing so, the early detection of cognition in the elderly can also be realized conveniently and accurately. However, there is a challenge for clinicians to distinguish the cognitive status of the elderly correctly. Most clinical detection methods are paper-based screening tests or electroencephalography analysis with a cognitive system that often leads the elderly with dementia to become impatient and unwilling to finish the test due to the long and complicated processing content. To improve the accuracy of dementia detection and provide a good detection experience to dementia patients, a convenient cognitive interactive assessment tool is needed. This dissertation presents three aspects of a firmware algorithm, an embedded system, and a machine learning classification algorithm to improve the experience and accuracy of the early cognitive assessment of the elderly.
First, the touch algorithm-based adaptive Kalman filter is adopted in the commonly used capacitive touch panels to improve the operating experience for the elderly. Without adding additional hardware burdens and low computing cost, the proposed algorithm utilized an exponential function to model the effects of variable movement speeds and reporting rates to provide an appropriate value for the adjustment of the Kalman filter gain promptly. Experimental results indicate that the various reporting rates decrease estimative ability of touched position, which is solved by using AKF method to achieve the accurate estimation of touched position. The results also demonstrate that the proposed method can accurately match the reference trajectory compared with the trajectories of the moving average filter (MAF) and the Kalman filter (KF). The results of root means square error (RMSE) of the proposed AKF method under the specifications of various movement speeds and reporting rates are less than 0.5 cm, and the ripple in the raw data can be effectively suppressed 75.75%. To further achieve the detection of patients with early dementia, an interactive cognitive system based on OLED modules is proposed in the second part. This system utilizes a modified Corsi block tapping task method with a soft OLED light source to construct a game-based cognitive assessment tool for the elderly. Moreover, a client identification circuit is designed to compute the number of OLED lighting devices on each place to identify the various pattern arrangements in the Corsi block tapping task. Each data collected from subjects will be transferred to cloud servers through a smartphone APP via wireless networks to provide information to psychologists about a subject’s visuospatial short-term working memory. Experimental results involving 24 participants show that the difference in the memory span between young and elderly groups is significant, F(1, 92) = 214, p<0.001. Moreover, the mean span performance using the proposed system is 45.8% better than that achieved using the computerized system for the elderly group, indicating the feasibility of the proposed system in spatial working memory detection. Furthermore, in the third part of the proposed dissertation, the machine learning mechanisms are imported to analyze data from subjects to further investigate the effect of visual path sequence in CBTT. The features of sequence length, the distance of path sequence, the intersection of path sequence, and the changes between inter-sequence are quantified as the input of the difficulty model. Three methods including hierarchical clustering, support vector machine, and fuzzy logic system were adapted to construct the sequence difficulty of the CBTT. The experimental results show that the classification effect of the three groups of hierarchical clustering is significant to the test score F(2, 297) = 29.06, p<0.001, indicating that the difficulty can be divided into easy, normal, and difficult parts. In the case of these three classification categories, the SVM method yielded an 87.3% accuracy rate in classifying similar performance scores (simple: 100 to 79 points; medium 78 to 63 points; difficulty: 62 to 0 points). Moreover, to further quantify the difficulty of the sequence, the fuzzy logic system uses features as linguistic variables for approximate reasoning. A significant effect was observed between difficulty and score, F(4, 295) = 13.01, p<0.001, and had a moderately negative Pearson correlation r = -0.407, verifying the effect of the difficulty of sequence on memory performance. Therefore, the algorithm for operational improvement on CTP, the interactive cognitive detection system, and the models of quantification for the difficulty in spatial cognitive detection proposed in this dissertation can improve the experience of the existing tablet-style cognitive task and reduce the burden of clinicians. Moreover, the accurate dementia detection methods can recognize the degree of dementia and provide patients with early treatment.

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