A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost

Abstract

Recently, multi-floor indoor positioning has become increasingly interesting for researchers, in which accurate recognition of indoor activities is critical for the detection of floor changes and the improvement of positioning accuracy according to indoor landmarks. However, we have not found a comprehensive study for recognizing indoor activities related to multi-floor indoor positioning based on a robust machine learning algorithm. In this work, we propose a framework for recognizing five indoor activities, i.e., walking, stillness, stair climbing, escalator, or elevator taking. In this framework, we investigate the relevant sensors and features to improve the recognition accuracy of these activities, especially some specific features in the frequency domain and wavelet domain. We propose to utilize a promising tree-based ensemble learning classifier, XGBoost, to recognize these activities. Based on our dataset created by 40 volunteers, we provide a comprehensive analysis of the proposed framework for indoor activity recognition. Considering both accuracy and computational cost, the XGBoost-based indoor activity recognition algorithm outperforms the other ensemble learning classifiers and single classifiers, and the average recognition F-score of XGBoost reaches 84.41%. In addition, our introduced specific features in the frequency domain and wavelet domain can significantly improve the recognition accuracy. Moreover, we use a publicly available dataset to verify our proposed framework and XGBoost classifier reaches 84.19% that outperforms the other classifiers.

Exploratory Study of Some Machine Learning Techniques to Classify the Patient Treatment

“…Y. Mishina, R. Murata, Y. Yamauchi, T. Yamashita, and H. Fujiyoshi claim that RF is more robust than other famous models and have been utilized in many fields such as, computer visions and pattern recognition [49]. The common weakness in using RF is the processing needs more time when applied to large amounts of data because it has to build many tree models [50]. A large number of trees also require significant memory capacity [49].…”

Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

“…The paper concluded that their proposed XGBoost model outperformed the other classifiers. In a recent contribution, Zhang et al [33] have used barometer, gyroscope and accelerometer to record five movements of multi-floor indoor activities. The recorded movements were elevator taking, stair climbing, stillness, escalator taking and walking.…”

“…Although some studies in Table 1 are providing high accuracy, all these differences make their performance incomparable. For example, Guo et al [32] used the XGboost classifier and achieved an f-measure score of 99%, while Zhang et al [33] have achieved f-measure of only 84.14% using the same classifier. Moreover, the impact of feature selection methods on the performance of boosting classifiers is not studied systematically considering the domain of PAC, and very little is known about how these classifiers behave when the feature selection stage is incorporated before classification.…”

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

“…XGBoost algorithm is improved based on GBDT (Gradient Boosting Decision Tree) algorithm [26], which is a kind of supervision algorithm [27]. The idea is to establish a certain number of classification regression trees, so that the predicted number value of the tree group is as close to the real number value as possible and has the greatest generalization ability [28]. The advantage of XGBoost algorithm is that it is hard to over-fitting, it can specify the default direction of branch for missing number value or specified number value.…”