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IEEE Final Year Project Topic for IT

Base Paper Title

FallDroid: An Automated Smart Phone based Fall Detection System using Multiple Kernel Learning

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IEEE Project Abstract

Common fall occurrences in the elderly population pose dramatic challenges in public healthcare domain. Adoption of an efficient and yet highly reliable automatic fall detection system may not only mitigate the adverse effects of falls through immediate medical assistance, but also profoundly improve the functional ability and confidence level of elder people. This paper presents a pervasive fall detection system developed on smartphones (SPs) namely, FallDroid that exploits a two-step algorithm proposed to monitor and detect fall events using the accelerometer signals. Comprising of the Threshold Based Method and Multiple Kernel Learning Support Vector Machine, the proposed algorithm uses novel techniques to effectively identify fall-like events and reduce false alarms. In addition to user convenience and low power consumption, experimental results reveal that the system detects falls with high accuracy(97.8%), sensitivity(99.5%), and specificity(95.2%), while achieving lowest false alarm rate of 1 alarm per 59 hours of usage.Common fall occurrences in the elderly population pose dramatic challenges in public healthcare domain. Adoption of an efficient and yet highly reliable automatic fall detection system may not only mitigate the adverse effects of falls through immediate medical assistance, but also profoundly improve the functional ability and confidence level of elder people. This paper presents a pervasive fall detection system developed on smartphones (SPs) namely, FallDroid that exploits a two-step algorithm proposed to monitor and detect fall events using the accelerometer signals. Comprising of the Threshold Based Method and Multiple Kernel Learning Support Vector Machine, the proposed algorithm uses novel techniques to effectively identify fall-like events and reduce false alarms. In addition to user convenience and low power consumption, experimental results reveal that the system detects falls with high accuracy(97.8%), sensitivity(99.5%), and specificity(95.2%), while achieving lowest false alarm rate of 1 alarm per 59 hours of usage.

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