HMM Adaptation for Improving a Human Activity Recognition System

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.

[1]  Jin Wang,et al.  Recognizing Human Daily Activities From Accelerometer Signal , 2011 .

[2]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[3]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  I. Pigeot,et al.  Using Hidden Markov Models to Improve Quantifying Physical Activity in Accelerometer Data – A Simulation Study , 2014, PloS one.

[5]  Davide Anguita,et al.  A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.

[6]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[7]  Petia Radeva,et al.  Human Activity Recognition from Accelerometer Data Using a Wearable Device , 2011, IbPRIA.

[8]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[9]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..

[10]  Faicel Chamroukhi,et al.  An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression , 2013, IEEE Transactions on Automation Science and Engineering.

[11]  Paul Lukowicz,et al.  Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers , 2004, Pervasive.

[12]  Jun Yang,et al.  Toward physical activity diary: motion recognition using simple acceleration features with mobile phones , 2009, IMCE '09.

[13]  Rubén San-Segundo-Hernández,et al.  Segmenting human activities based on HMMs using smartphone inertial sensors , 2016, Pervasive Mob. Comput..

[14]  Young-Koo Lee,et al.  Semi-Markov conditional random fields for accelerometer-based activity recognition , 2010, Applied Intelligence.

[15]  Andrey Temko,et al.  Acoustic Event Detection and Classification , 2007, Computers in the Human Interaction Loop.

[16]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

[17]  Angelo M. Sabatini,et al.  Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers , 2010, Sensors.

[18]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[19]  Davide Anguita,et al.  Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic , 2013, J. Univers. Comput. Sci..

[20]  Fernando Fernández Martínez,et al.  Feature extraction from smartphone inertial signals for human activity segmentation , 2016, Signal Process..

[21]  Yasir Mohd Mustafah,et al.  Real-time Human Activity Recognition , 2017 .

[22]  Y.-L. Chow.  Maximum mutual information estimation of HMM parameters for continuous speech recognition using the N-best algorithm , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[23]  Tadahiro Kuroda,et al.  Haar-Like Filtering for Human Activity Recognition Using 3D Accelerometer , 2009, 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop.

[24]  Wanmin Wu,et al.  Classification Accuracies of Physical Activities Using Smartphone Motion Sensors , 2012, Journal of medical Internet research.

[25]  Matjaz Gams,et al.  Adapting activity recognition to a person with Multi-Classifier Adaptive Training , 2015, J. Ambient Intell. Smart Environ..

[26]  Wilhelm Stork,et al.  Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Y.-K. Lee,et al.  Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis , 2010, 2010 5th International Conference on Future Information Technology.

[28]  Sung-Bae Cho,et al.  Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.

[29]  V. Garovic,et al.  Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm , 2015, PLoS biology.

[30]  Mitja Luštrek,et al.  Semi-supervised Learning for Adaptation of Human Activity Recognition Classifier to the User , 2011 .

[31]  Juan-Luis Gorricho,et al.  Activity Recognition from Accelerometer Data on a Mobile Phone , 2009, IWANN.

[32]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.