Abstract
A fast and accurate computational model of HMM (Hidden Markov Model) is proposed for Activity Recognition System using inbuilt sensors of Smart Mobile Phone. Twelve features are calculated from the captured data and the feature vectors are divided into two vectors which are used as inputs to HMM. All computational methods follow probability theories and for measuring differences of two probability based events we used K–L divergence of Kullback and Leibler (Ann Math Stat 22(1):79–86, 1951) known as KLD (Kullback & Leibler Divergence). For comparing of feature values of ground truth and that of experimental values, we have developed an algorithm D-HMM (Divisional-HMM, proposed algorithm). Results show better recognition than existing HF-SVM (Hardware Friendly Support Vector Machine) and also better than our previous work of CFT (Conditional Features using Threshold, a method developed for using different schemes of threshold values for selection and matching purposes of feature values).
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Acharjee, D., Maity, S.P. & Mukherjee, A. Hidden Markov model a tool for recognition of human contexts using sensors of smart mobile phone. Microsyst Technol 23, 571–582 (2017). https://doi.org/10.1007/s00542-016-2973-5
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DOI: https://doi.org/10.1007/s00542-016-2973-5