id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-027713-8ohchx8p	Abidine, M’hamed Bilal	Human Activities Recognition in Android Smartphone Using WSVM-HMM Classifier	2020-05-31		.txt	text/plain	2355	138	50	In this work, we perform the recognition of the human activity based on the combined Weighted SVM and HMM by taking advantage of the relative strengths of these two classification paradigms. The basic procedure for mobile activity recognition involves i) collection of labelled data, i.e., associated with a specific class or activity from users that perform sample activities to be recognized ii) classification model generation by using collected data to train and test classification algorithms iii) a model deployment stage where the learnt model is transferred to the mobile device for identifying new contiguous portions of sensor data streams that cover various activities of interest. In this work, we adopted a new method for physical activity recognition using mobile phones that uses labels outputting WSVM in HMM. One also notices for HAR dataset that the multi-class WSVM method improves the classification results over MC-SVM, MC-HF-SVM and HMM classifiers used alone.	./cache/cord-027713-8ohchx8p.txt	./txt/cord-027713-8ohchx8p.txt