精確的恢復監測與建議對提升運動表現至關重要，而睡眠則是恢復過程中的核心要素。為解決當前運動員睡眠監測面臨的挑戰—臨床級設備不利於長期應用，而消費級裝置的演算法精準度不足且缺乏透明性—本研究致力於開發並驗證一套透明化、個人化，且基於腦波訊號 (EEG) 的睡眠恢復評估框架，期望為運動員提供每日睡後恢復與介入建議的依據。本研究針對一位 27 歲男性鐵人三項運動員進行為期 38 天的縱向個案研究。受試者每晚於居家環境中自主配戴整合腦波與呼吸監測功能的模組化系統，並由專業睡眠技師進行資料判讀。此外，受試者每日填寫主觀睡眠日誌，記錄主觀與客觀訓練強度，並回報當日生活中的重大事件，以作為綜合恢復評估的重要參考。我們進一步建構一套基於模糊推論系統 (fuzzy inference system, FIS) 的睡眠恢復評估模型，並設計多維度雷達圖以進行視覺化診斷與回饋。在38天的監測期間中，共成功取得30晚的有效客觀睡眠資料，資料取得率為78.9%，並篩選出20晚品質良好的資料進行深入分析。研究結果顯示，儘管該名運動員的多項傳統睡眠指標多數落在臨床正常範圍內，例如睡眠效率 (sleep efficiency, SE)、睡眠潛伏期 (sleep onset latency, SOL) 及入睡後醒來總時數 (wake after sleep onset, WASO)，其實際恢復狀態卻仍呈現明顯波動，反映出傳統指標在精細評估恢復狀態時敏感度有限，若搭配覺醒指數 (arousal index, AI) 等反映睡眠穩定度的指標一併評估，則能更具體揭示恢復狀態的變化。本研究所建立的 FIS 模型在恢復評估中展現出高度的預測準確性，整體一致性達 83.3%。多維度雷達圖能清晰揭示恢復不佳的潛在原因，例如睡眠時長不足、睡眠連續性差及穩定度下降等。綜合來說，本研究成功構建了一套可行、透明且具個人化特性的恢復評估框架，不僅驗證了居家長期睡眠監測的實用性，更提供超越傳統睡眠分數的資料導向評估工具，有助於運動員與教練更有效優化訓練與恢復策略。此框架未來有望推廣至更廣泛的運動員族群，透過個人化且精準的回饋，促進科學化訓練與表現優化。

Accurate recovery monitoring and guidance are crucial for enhancing athletic performance, with sleep serving as a core component of the recovery process. To address current challenges in athlete sleep monitoring—namely, the impracticality of clinical-grade devices for long-term use and the limited accuracy and transparency of consumer-grade device algorithms—this study aims to develop and validate a transparent, personalized sleep recovery estimation framework based on electroencephalography (EEG) signals, providing athletes with daily recovery and intervention recommendations. This study conducted a 38-day longitudinal case study involving a 27-year-old male triathlete. The participant wore a modular system integrating EEG and respiratory monitoring every night in his home environment, with data scored by professional sleep technicians. Additionally, he completed daily subjective sleep diaries, recorded both subjective and objective training intensities, and reported major daily life events, serving as important references for comprehensive recovery evaluation. We further developed a sleep recovery estimation model based on a fuzzy inference system (FIS) and designed a multidimensional radar chart for visual diagnosis and feedback. During the 38-day monitoring period, 30 nights of valid objective sleep data were successfully collected, yielding a data acquisition rate of 78.9%, from which 20 high-quality nights were selected for in-depth analysis. Results showed that although most conventional sleep metrics—such as sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO)—remained within clinical normal ranges, the athlete’s actual recovery status exhibited significant fluctuations. This indicates that conventional metrics have limited sensitivity in finely evaluating recovery status. Incorporating key indicators of sleep stability, particularly the arousal index (AI), provided a more specific insight into recovery changes. The FIS model established in this study demonstrated high predictive accuracy in recovery estimation, achieving an overall agreement of 83.3%. The multidimensional radar chart clearly highlighted potential causes of poor recovery, including insufficient sleep duration, poor sleep continuity, and decreased stability. In summary, this study successfully constructed a feasible, transparent, and personalized recovery estimation framework that not only validates the practicality of long-term home sleep monitoring but also offers a data-driven evaluation tool that surpasses traditional sleep scores, assisting athletes and coaches in optimizing training and recovery strategies more effectively. In the future, this framework can be extended to a broader population of athletes, providing personalized and precise feedback to support more scientific training interventions and enhance performance.

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