An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Smart Sensor to Detect the Falls of the Elderly
IEEE Pervasive Computing
Activity Summarisation and Fall Detection in a Supportive Home Environment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Toward a sound analysis system for telemedicine
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
IEEE Transactions on Information Technology in Biomedicine
Information extraction from sound for medical telemonitoring
IEEE Transactions on Information Technology in Biomedicine
Assistive music browsing using self-organizing maps
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Video-surveillance and context aware system for activity recognition
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Context guided and personalized activity classification system
Proceedings of the 2nd Conference on Wireless Health
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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).