利用基因演算法改善階層式活動辨識

Genetic Algorithm for Improving Hierarchical Activity Recognition

潘思評(Ssu-Ping Pan)

指導教授 ： 蔡志忠

國立中正大學/工學院/資訊工程學系/碩士(2015年)

摘要

由於普適運算的進步，得以發展一些用來蒐集活動資訊的感測器，再加上機器學習的演算法，使得很多的應用能夠被發展。其中一種應用，就是在智能環境中，用來監控和追縱個人完成基本日常生活的能力，和提供一些個人生活上的協助。這樣的應用建立在需要從不同的感測器所收集到的資料串流中，自動地辨識人們真正執行的活動。
在本研究中，我們嘗試把活動類別組織成階層式的架構來改善分類的效能，並採用基因演算法，自動地去搜尋一個在準確率上有最佳表現的階層式架構。此外，為了能夠在收到新的資料時即時辨識使用者所執行的活動，我們採用滑動窗模式來處理真實環境所收集到的資料串流。最後，從我們的實驗結果得知，在這樣的架構下準確率皆能獲得改善。

並列摘要

Due to the incorporation of pervasive computing and machine learning algorithm, a wide variety of applications have been developed. One of these applications in smart environment is to monitor and track the functional status of residents and provide personal support for residents. Those application rely on being able to automatically recognize the activity perform by resident based on the series of streaming sensor data which collected by different sensors. In this thesis, we attempt to use a structure of hierarchy to improve the performance of activity recognition. We adopt a genetic algorithm-based method to automatically search the hierarchy which has the best performance in terms of accuracy. Furthermore, instead of experimenting on scripted or pre-segmented sequence of sensor events related to activities, we adopt a sliding window based approach to perform activity recognition in an on line or streaming fashion. From our experiment, whether in the situation that only considering the predefined activities or coupling with the “other” activity, the accuracy all can slightly be improved.

並列關鍵字

Activity Recognition ； Genetic Algorithm

參考文獻

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[11] Genetic algorithm