Human Activity Recognition for Ambient Assisted Living

The considerable rise in the average age of the population leads to the exceeding number of seniority people. This has made a strong impact in demanding community requirement such as rehabilitation, physical support, home assistance etc. For this reason, Ambient Assisted Living (AAL) plays an exceptional role in supervising the Activities of Daily Living (ADL) of the target users. AAL would be an encouraging technology for the current care models by acting as a companion. This becomes a thought-provoking research area in the fast-developing world, but the condition for examining various ADL and the self-classification becomes a big challenge. There are numerous ways to accumulate data from camera, microphone and other sensors. Datasets that are used in this paper have been collected from publicly available resources for Human Activity Recognition. In this work, we have discussed about different classification algorithm used for grouping of features into their corresponding activity and by then their performance is evaluated. However, many classifiers face the limitations of enormous number of datasets and immense training time of the feature vector. Hence to overcome these problems, we have used RF and K-NN classifiers. Both are tremendously helpful in dealing with larger datasets and provides an accuracy level of about 83.05%.