Human Distress Sound Analysis and Characterization Using Advanced Classification Techniques

The analysis of sounds generated in close proximity of a subject can often indicate emergency events like falls, pain and other distress situations. This paper presents a system for collecting and analyzing sounds and speech expressions utilizing on-body sensors and advanced classification techniques for emergency events detection. A variety of popular classification and meta-classification algorithms have been evaluated and the corresponding results are presented.

[1]  Michel Vacher,et al.  Sound detection and classification through transient models usingwavelet coefficient trees , 2004, 2004 12th European Signal Processing Conference.

[2]  Bernhard Schölkopf,et al.  Statistical Learning and Kernel Methods , 2001, Data Fusion and Perception.

[3]  N. Noury A smart sensor for the remote follow up of activity and fall detection of the elderly , 2002, 2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.02EX578).

[4]  K. Fukaya Fall detection sensor for fall protection airbag , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[5]  Yong Wang,et al.  Using Model Trees for Classification , 1998, Machine Learning.

[6]  E. Ambikairajah,et al.  An Adapted Gaussian Mixture Model Approach to Accelerometry-Based Movement Classification Using Time-Domain Features , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  M. Alwan,et al.  A Smart and Passive Floor-Vibration Based Fall Detector for Elderly , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[9]  C. Rougier,et al.  Monocular 3D Head Tracking to Detect Falls of Elderly People , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Eric Castelli,et al.  Toward a Sound Analysis System for Telemedicine , 2005, FSKD.

[11]  Ian H. Witten,et al.  Stacking Bagged and Dagged Models , 1997, ICML.

[12]  Shuangquan Wang,et al.  Human activity recognition with user-free accelerometers in the sensor networks , 2005, 2005 International Conference on Neural Networks and Brain.

[13]  Michel Vacher,et al.  Information extraction from sound for medical telemonitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[14]  Hong Qiao,et al.  An Evolutionary Support Vector Machines Classifier for Pedestrian Detection , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Neil Johnson,et al.  A smart sensor to detect the falls of the elderly , 2004, IEEE Pervasive Computing.

[16]  M. Prado,et al.  Distributed intelligent architecture for falling detection and physical activity analysis in the elderly , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[17]  H.C. Kim,et al.  Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[19]  Ian Witten,et al.  Data Mining , 2000 .

[20]  N. Noury,et al.  Monitoring behavior in home using a smart fall sensor and position sensors , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[21]  J.F. Serignat,et al.  Generic Implementation of a Distress Sound Extraction System for Elder Care , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Bart Jansen,et al.  Context aware inactivity recognition for visual fall detection , 2006, 2006 Pervasive Health Conference and Workshops.

[23]  Jon Leachtenauer,et al.  Impact of monitoring technology in assisted living: outcome pilot , 2006, IEEE Transactions on Information Technology in Biomedicine.

[24]  Ron Kohavi,et al.  Wrappers for performance enhancement and oblivious decision graphs , 1995 .

[25]  Luís Torgo,et al.  Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings , 2005, PKDD.

[26]  Jean Carletta,et al.  Assessing Agreement on Classification Tasks: The Kappa Statistic , 1996, CL.

[27]  Nigel Steele,et al.  Neural-network compensation methods for capacitive micromachined accelerometers for use in telecare medicine , 2001, IEEE Transactions on Information Technology in Biomedicine.

[28]  S. Miaou,et al.  A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information , 2006, 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2..

[29]  J. Sasiadek,et al.  Sensor fusion based on fuzzy Kalman filter , 2001, Proceedings of the Second International Workshop on Robot Motion and Control. RoMoCo'01 (IEEE Cat. No.01EX535).

[30]  E. Ambikairajah,et al.  Automated Sound Analysis System for Home Telemonitoring using Shifted Delta Cepstral Features , 2007, 2007 15th International Conference on Digital Signal Processing.

[31]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[32]  Stefan Kramer,et al.  Ensembles of Balanced Nested Dichotomies for Multi-class Problems , 2005, PKDD.

[33]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[34]  Michel Vacher,et al.  First steps in data fusion between a multichannel audio acquisition and an information system for home healthcare , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[35]  Suhuai Luo,et al.  A dynamic motion pattern analysis approach to fall detection , 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

[36]  Stephen J. McKenna,et al.  Activity summarisation and fall detection in a supportive home environment , 2004, ICPR 2004.

[37]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.