Analysis of human behavior recognition algorithms based on acceleration data

The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.

[1]  Toni Giorgino,et al.  Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package , 2009 .

[2]  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.

[3]  Tatsuya Yamazaki,et al.  Assistive Technologies in Smart Homes , 2014 .

[4]  Daniel Olgu ´ õn,et al.  Human Activity Recognition: Accuracy across Common Locations for Wearable Sensors , 2006 .

[5]  Paul Lukowicz,et al.  Recognizing and Discovering Human Actions from On-Body Sensor Data , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[6]  Fulvio Mastrogiovanni,et al.  Activity recognition in smart homes: from specification to representation , 2010, J. Intell. Fuzzy Syst..

[7]  Thestaffofthebenjaminrosehos.  MULTIDISCIPLINARY studies of illness in aged persons. II. A new classification of functional status in activities of daily living. , 1959, Journal of chronic diseases.

[8]  Fulvio Mastrogiovanni,et al.  The S.H.E.L.L. project: A secure habitat for an enhanced long living: Design, implementation and guidelines for a new concept of people assistance , 2010, 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010).

[9]  D. Donoho,et al.  Does median filtering truly preserve edges better than linear filtering , 2006, math/0612422.

[10]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[11]  Tullio Vernazza,et al.  Human motion modelling and recognition: A computational approach , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[12]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

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

[14]  Matthias Struck,et al.  User-friendly system for recognition of activities with an accelerometer , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[15]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[16]  James A. Landay,et al.  The Mobile Sensing Platform: An Embedded Activity Recognition System , 2008, IEEE Pervasive Computing.

[17]  Aude Billard,et al.  A survey of Tactile Human-Robot Interactions , 2010, Robotics Auton. Syst..