根據世界衛生組織(WHO)調查顯示憂鬱症的人口比例逐年快速成長，憂鬱傾向已是不可輕忽的狀況。近年來已有許多研究嘗試利用智慧型裝置協助進行情緒辨識甚至追蹤精神患者的情緒狀態，但多數研究並未對使用者給予進一步的回饋。因此，本研究嘗試在智慧型裝置上建立一套適時適地的紓壓活動推薦系統，藉此提醒使用者情緒自覺並協助其緩解負面情緒。
本研究設計一套情境感知的紓壓活動推薦方法並且實做於智慧型裝置。為了考量紓壓活動的多樣性及適用性，本研究的推薦方法會依使用者所在的環境來參考相似者與其本身過去的紓壓活動經驗，依此建立個人化的紓壓活動清單。最後，本研究的推薦系統會透過使用者在手機上的操作模式辨識其負面情緒，當辨識到負面情緒時會即時的提供適時適地的紓壓活動清單以協助其紓緩負面情緒。
在實驗的部份分為兩階段。第一階段，蒐集正常使用者的資料以進行系統準確度及可用性評估，實驗結果得到0.38的Mean reciprocal rank (MRR)分數。而在第二階段的實驗，我們與臨床心理醫師合作並進行憂鬱症患者的收案以討論系統的可用性及有效性。實驗結果得到0.28的MRR分數而在於情緒改善率：心情、壓力、焦慮分別改善了15.25%、5.63%、10.25%。
本研究在智慧型裝置開發了一套個人化的紓壓活動推薦系統，經過初步的臨床實驗，驗證了本系統的可用性及有效性。最後，我們期許此系統可以提醒使用者自覺情緒、輔助使用者進行情緒管理並且協助心理醫師追蹤病患的心理情況。

According to the World Health Organization (WHO), there is currently rapid growth in the proportion of the population suffering from depression. Hence, depression might not be disregarded, making awareness of negative emotions a helpful treatment. In recent years, many studies have used smartphones to identify and track emotion states in both healthy participants and patients with mental disorders. However, most previous approaches did not provide additional feedback about appropriate activity that helping users to improve emotion to the user. As a result, this study was devoted to building a smartphone-based context-aware relaxation activity recommender system that increased users’ awareness of their emotions and helped them to alleviate negative emotions.
This study developed the context-aware activity recommender method and implemented it on smartphones. In order to consider the variety and applicability of activities in our system, our proposed method created recommendation lists by referring to users’ environmental situation and activity histories, and preferences of similar users. Finally, we implemented a personalized smartphone-based activity recommender system. Using application usage patterns, our system instantaneously made an appropriate recommendation upon identifying users’ negative emotions and then helped users to alleviate these emotions.
This experiment was divided into two parts. In the first, we collected healthy participants’ data to evaluate the accuracy and availability of the system. Here, the results achieved a mean reciprocal rank (MRR) of 0.38. In the second part, we collaborated with clinical psychiatrists to gather data on patients with depression, and discussed the usability and validity of system. In this case, we obtained a MRR of 0.28. Furthermore, we found that the improvement rates of depression, stress, and anxiety were 15.28%, 5.63%, and 10.25%, respectively.
In this study, we successfully developed a smartphone-based context-aware personalized activity recommender system. Through data collection over a period of 14 days and a preliminary clinical experiment, we verified the availability and validity of our system. Finally, we expect that this recommender system can increase users’ awareness of their emotions, assist with emotion management, and help clinical psychiatrists with tracking users’ mental states.

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