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Triaxial accelerometer based fall detection method using a self-constructing cascade-adaboost-svm classifier

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......
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Triaxial accelerometer based fall detection method using a self-constructing cascade-adaboost-svm classifier

  1. 1. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE BANGALORE / HYDRABAD CELL: 9894917187 | 875487 1111/2222/3333 | 8754872111 / 3111 / 4111 / 5111 / 6111 Visit: www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com TRIAXIAL ACCELEROMETER-BASED FALL DETECTION METHOD USING A SELF-CONSTRUCTING CASCADE-ADABOOST-SVM CLASSIFIER ABSTRACT In this paper, we propose a cascade-AdaBoost support vector machine (SVM) classifier to complete the triaxial accelerometer-based fall detection method. The method uses the acceleration signals of daily activities of volunteers from a database and calculates feature values. By taking the feature values of a sliding window as an input vector, the cascade- AdaBoost-SVM algorithm can self-construct based on training vectors, and the AdaBoost algorithm of each layer can automatically select several optimal weak classifiers to form a strong classifier, which accelerates effectively the processing speed in the testing phase, requiring only selected features rather than all features. In addition, the algorithm can automatically determine whether to replace the AdaBoost classifier by support vector machine. We used the UCI database for the experiment, in which the triaxial accelerometers are, respectively, worn around the left and right ankles, and on the chest as well as the waist. The results are compared to those of the neural network, support vector machine, and the cascade-AdaBoost classifier. The experimental results show that the triaxial accelerometers around the chest and waist produce optimal results, and our proposed method has the highest accuracy rate and detection rate as well as the lowest false alarm rate.
  2. 2. EXISTING SYSTEM Falls are one of the most common adverse events in hospitals and fall management remains a major challenge in the medical care quality. Falls in patients are associated with major health complications that can result in health decline and increased medical care cost. To deliver medical care in time, reliable location-aware fall detection is needed. PROPOSED SYSTEM In this paper, we propose a patient alert alarm for fall management. It is SMS location awareness fall detection system that provides immediate position information to the care givers as soon as the fall happened. Obviously, the integration of location awareness and fall detection technologies fulfills the requirements of delivering critical information to relative professions and improve the medical care quality. This project gives the location awareness such as in-time information about the location of their patients, doctors, and medical staffs when medical emergencies arise .For example, inpatients might fall somewhere in the hospital during the midnight, a patient alert alarm system for fall management provides location information to the night shift nurses.In this project, we use a location-aware fall detection system by using tri-axial accelerometers as fall detecting sensors. The proposed system can provide emergency alert and position information to caregivers through SMS as the patients fall happened.
  3. 3. BLOCK DIAGRAM APPLICATIONS 1. Fall detection of patients 2. Accident awareness HARDWARE REQUIREMENTS 1. 89C51 microcontroller 2. Accelerometer sensor 3. ADC 4. Power supply 5. GSM modem 6. Mobile SOFTWARE REQUIREMENTS 1. Keil IDE 2. Flash magic

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