高齡者在表達身體不適、情緒困擾與社會孤立時，經常使用簡短、間接或同時涉及多個面向的語句，使傳統關鍵字式對話系統難以選擇適當的知識來源與回應策略。此外，語音語調與臉部表情雖可提供額外的情感脈絡，但此類模型輸出具有不確定性，不應被直接視為臨床診斷或風險判定依據。為處理上述問題，本論文設計並實作一套用於高齡者生理與心理意圖理解之風險感知雙軌檢索增強生成框架，目標是在非臨床情境下提供具證據條件、可檢查且較為審慎的對話支援。 所提出的原型整合網頁式互動、語音轉文字、音訊情緒辨識、音訊與影像之憂鬱相關情感偵測、確定性意圖路由、密集向量檢索、大型語言模型回應生成，以及執行軌跡紀錄。系統依目前文字訊息將操作性意圖分類為生理、心理、混合或未知，並據此搜尋生理知識庫、心理知識庫或兩者。多模態情緒輸出僅作為調整同理程度與語氣的輔助脈絡，不參與知識庫選擇或確定性自我傷害規則。一般訊息會將目前訊息、篩選後的使用者對話歷史、情緒狀態與檢索證據共同提供給語言模型，並透過提示限制避免診斷、藥物指示與臨床決策。當文字符合預先定義的明確自我傷害片語時，系統則略過一般生成流程，搜尋輔助安全知識庫以供追溯，並回傳固定的求助建議。 本研究以控制式工程評估檢驗原型，共執行五十六個單輪測試案例與六組、共十二輪的多輪對話。所有單輪請求均成功完成。意圖路由準確率為百分之六十二點五，巨集平均 F1 為零點六四零。生理與心理檢索的 Hit@5 分別為百分之七十五點零與百分之八十六點七，混合訊息同時取得兩軌相關證據的比例為百分之八十七點五。針對目前規則所涵蓋的自我傷害片語，召回率為百分之八十七點五；然而，其精確率僅為百分之五十點零，困難負例的特異度為百分之十二點五，且對未包含既定片語的語意高風險案例之觸發率為百分之零。人工結構化評分顯示，回應相關性為百分之六十點七，證據基礎性為百分之三十七點五，正確回應行動為百分之四十六點四；另一方面，所有受評回應皆避免明確診斷與直接治療指示。平均端到端延遲為九百三十五點六毫秒，第九十五百分位延遲為一千七百三十八點一毫秒。多輪測試中，第二輪回應正確聚焦目前訊息與避免前一主題延續的比例皆為百分之五十點零。 結果顯示，本研究成功建立一套可執行、模組化且具執行層級可追溯性的高齡者對話支援原型，雙軌密集檢索亦能在多數可回答案例中取得相關證據。然而，確定性路由對間接或模糊表達的處理能力有限，語言模型未能穩定使用已檢索證據，片語式安全規則容易受到否定、引用、第三人稱與語意改寫影響，多輪主題隔離亦仍不足。因此，本系統僅應被視為技術研究原型，不可取代醫師、心理專業人員、照護者或緊急支援服務。未來研究應優先改善語意與多語言路由、獨立檢索評估、證據重排序與生成後查核、情境感知安全分類、多輪歷史選擇，以及由專家與真實高齡使用者參與的倫理審查研究。;Older adults may express physical discomfort， emotional distress， and social isolation through brief， indirect， or overlapping statements， making it difficult for conventional keyword-based conversational systems to select appropriate knowledge and response strategies. Vocal and facial behavior may provide additional affective context， but such model outputs are uncertain and should not be treated as clinical diagnoses or independent risk decisions. To address these challenges， this thesis designs and implements a risk-aware Dual-Track Retrieval-Augmented Generation framework for elderly physiological and psychological intent understanding. The framework is intended to provide evidence-conditioned， inspectable， and cautious conversational support in a non-clinical setting. The prototype integrates web-based interaction， speech-to-text processing， audio emotion recognition， audio-video depression-related affect detection， deterministic intent routing， dense vector retrieval， Large Language Model response generation， and execution tracing. The current textual message is operationally classified as physiological， psychological， mixed， or unknown， and this result determines whether the physiological repository， psychological repository， or both are searched. Multimodal emotion outputs are used only as auxiliary context for empathy and tone; they do not select a repository or activate the deterministic self-harm rule. For normal messages， the prompt combines the current message， filtered user-side history， emotional-state context， and retrieved evidence while prohibiting diagnosis， medication instructions， and clinical decisions. When the text matches a predefined explicit self-harm phrase， the system bypasses normal generation， searches an auxiliary safety repository for traceability， and returns a fixed recommendation to seek immediate human or professional support. The prototype was examined through a controlled engineering evaluation containing 56 single-turn cases and six multi-turn conversations with 12 total turns. All single-turn requests completed successfully. Operational intent routing achieved 62.5\% accuracy and a macro-F1 score of 0.640. Physiological and psychological retrieval achieved Hit@5 values of 75.0\% and 86.7\%， respectively， while 87.5\% of mixed cases retrieved relevant evidence from both tracks. For self-harm phrases covered by the implemented rule， recall was 87.5\%; however， precision was 50.0\%， hard-negative specificity was 12.5\%， and none of the semantic high-risk challenge cases without the predefined wording activated the high-risk branch. Structured response assessment produced pass rates of 60.7\% for relevance， 37.5\% for evidence grounding， and 46.4\% for correct response action. In contrast， all assessed responses avoided explicit diagnosis and direct treatment instructions. Mean end-to-end latency was 935.6~ms， with a 95th-percentile latency of 1，738.1~ms. In the multi-turn evaluation， only 50.0\% of second-turn responses remained focused on the current message and avoided carryover from the previous topic. These findings demonstrate the technical feasibility of an executable， modular， and execution-traceable elderly-support prototype， and show that Dual-Track dense retrieval can return relevant evidence for most answerable cases. Nevertheless， deterministic routing remained weak for indirect or ambiguous language， the language model did not consistently use retrieved evidence， phrase-based safety control was vulnerable to negation， quotation， third-person references， and semantic paraphrases， and multi-turn topic isolation was insufficient. The system must therefore be regarded only as a research prototype and not as a replacement for physicians， mental-health professionals， caregivers， or emergency-support services. Future work should prioritize semantic and multilingual routing， independent retrieval evaluation， reranking and evidence thresholds， post-generation grounding checks， context-aware safety classification， improved history selection， and ethically reviewed evaluation with experts and older adult users.
