Enabling human status awareness in assistive environments based on advanced sound and motion data classification

The paper presents the concept and an initial implementation of a patient status awareness system that may be used for patient activity interpretation and emergency recognition in cases like elder falls and distress speech expressions. The awareness is performed through collecting, analyzing and classifying motion and sound data. The latter are collected through sensors equipped with accelerometers and microphones that are attached on the body of the patients and transmit patient movement and sound data wirelessly to the monitoring unit. Applying Short Time Fourier Transform (STFT) and spectrogram analysis on sounds detection of fall incidents is possible. The classification of the sound and movement data is performed using Support Vector Machines. Evaluation results indicate the high accuracy and the effectiveness of the proposed implementation. The system architecture is open and can be easily enhanced to include patient awareness based on additional context (e.g., physiological data).

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

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

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

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

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

[6]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[7]  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).

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

[9]  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).

[10]  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.

[11]  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..

[12]  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.

[13]  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.

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

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

[16]  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.

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

[18]  John G. Taylor,et al.  Vector Machines , 2002, Neural Networks and the Financial Markets.

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

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

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

[22]  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.

[23]  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).

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

[25]  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.

[26]  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).

[27]  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.

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

[29]  Stephen J. McKenna,et al.  Activity summarisation and fall detection in a supportive home environment , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..