In this work we analyze and compare machine learning methods for recognizing human activity in the context of smart home by using data obtained from an optical heartbeat sensor and an accelerometer embedded in a smart watch, and find a number of activity classes that can be predicted. The conclusion is made that for such type of problems the random forest method with about 10 classes shows the best results.