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Algorithms 2016, 9(3), 60; doi:10.3390/a9030060

HMM Adaptation for Improving a Human Activity Recognition System

Speech Technology Group, E.T.S.I. Telecomunicación, Universidad Politecnica de Madrid, 28040 Madrid, Spain
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Academic Editor: Olga C. Santos
Received: 16 June 2016 / Revised: 22 July 2016 / Accepted: 29 August 2016 / Published: 2 September 2016
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Abstract

When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity), and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP) approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance. View Full-Text
Keywords: user adaptation; human activity segmentation; HMMs; smartphone inertial sensors user adaptation; human activity segmentation; HMMs; smartphone inertial sensors
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MDPI and ACS Style

San-Segundo, R.; Montero, J.M.; Moreno-Pimentel, J.; Pardo, J.M. HMM Adaptation for Improving a Human Activity Recognition System. Algorithms 2016, 9, 60.

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