Indoor/Outdoor Detection of Smartphones

• Main Authors: Shih-Hsien Kuo, 郭士賢
• Other Authors: Min-Te Sun
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/4g996y

碩士 === 國立中央大學 === 資訊工程學系在職專班 === 105 === The indoor/outdoor detection for smartphones has many potential applications. In this thesis, the practice of indoor/outdoor detection is treated as the supervised learning problem. The data are collected from different time and places, which contain features from IMU sensors (i.e., accelerometers, gyroscope, and gravity and so on). A number of machine learning algorithms, including kNN, Naive Bayes, ANN, and SVM, are adopted to build the prediction model using the training dataset, and the performance of different models are verified using the test dataset. The parameters in some models are tuned, such as k value in kNN and the number of hidden layers in ANN, in order to obtain the best prediction performance. On the other hand, 10-fold cross validation and AUC are used to verify if any model overfits the training dataset. At the end, we have identified that SVM with linear kernel has the best and most stable performance for smartphone indoor/outdoor detection among all different learning algorithms.