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Activity recognition technique is critical for the elderly people. To avoid the need of full time care giving service, the actual trend is to encourage elderly to stay living autonomously in their homes as long as possible. Reliable activity recognition techniques can improve life safety of the elderly and boost their confidence by immediately alerting fall cases to caregivers. Machine learning techniques are extensively used to predict the activity recognition of elder people. This paper has proposed the integration of the feature selection technique and genetic algorithms to improve the accuracy rate for detection of activity recognition of elder people as well as substantial features of activity recognition of elder people. The experimental results brings about the proposed technique that clearly shown the fact that proposed technique outperforms over the existing methods.

An Efficient Feature Selection Technique using Genetic Algorithm for Activity Recognition of Elder People