近年來智慧環境的概念逐漸廣為人知，當中最受到人們關注的領域便是智慧家庭，由於當代社會生活作息不規律導致生活品質下降，可預期人們將會使用智慧家庭中的相關應用來改進他們的生活品質。醫學專家可通過了解人們的日常活動，詳細了解人們的健康狀況，進而協助人們改善生活品質。過去的研究指出可利用非侵入式感測器以收集可用於辨識人們日常作息之個人生理指標，也就是心率變異性資料。此外，萃取自智慧環境的居家情境資訊也有望可用於辨識人們的生活作息。
在此研究中，首先收集了參與實驗人員在現實生活中產生之心率變異性和日常活動資料，透過資料前處理技術來增進資料之品質，讓機器學習演算法能夠更好地透過心率變異性資料分類出使用者的日常活動。並使用了幾種常見的監督式機器演算法，在實驗結果中得到了高精確度的分類結果。之後更進一步地利用了所謂單一使用者獨立驗證的概念，評估心率變異性資料是否存有跨使用者的共通隱藏規律。
緊接著，本研究參考了過去以居家情境資訊辨識人們日常活動的相關研究，根據先前收集到的現實生活資料和過去相關研究，建立了居家情境資訊的模擬資料。模擬結果顯示出日常活動的辨識成功率在加入居家情境資訊的模擬資料後有所提升。

Since the concept of Intelligent Environment has been widely spread in recent years. Smart Home as the most concerned branch of Intelligent Environment, it is expected that smart home applications will be incorporated into people's lives to improve their quality of life, avoiding the decline in quality of life caused by irregular lifestyles in contemporary society. By investigating people's daily routines, medical experts will have a better understanding of their health conditions to further assist to improve their life quality. Previous research has indicated that the non-intrusive sensor can be used to collect participants' biomarkers, such as Heart Rate Variability (HRV), which can be used to recognize people's activities. Moreover, the indoor context information extracted from the intelligent environment has the potential to be used for daily activities recognition. In this dissertation, participants' real-life data on HRV signals and daily routines are collected. Further, data preprocessing methods are used to improve the data quality, and machine learning techniques are used to produce classification models to recognize participants' activities from HRV data. Several supervised learning methods are evaluated, and the results indicate the classification models can detect daily activities with high accuracy. An additional method of Leave-Individual-Out is used to evaluate whether there are common HRV patterns in participants' activities across different individuals. After that, we refer to the related research on the activities recognition using indoor context information, to build simulated data of indoor context information based on participants' real-life data and the past research studies. The simulation results show that the recognition accuracy of daily activities has improved after adding the simulated indoor context information.

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