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This paper presents a method for human activity recognition and energy expenditure estimation with two tri-axial accelerometers. Recognizing the activity of a person and measuring his/her energy expenditure is important for the management of several diseases. In the CHIRON project we aim to monitor congestive heart failure patients using wearable sensors and a smartphone. Our method uses a classifier for activity recognition constructed with machine learning. Attention was paid to the complexity of the attributes for machine learning, resulting in the omission of the most complex attributes in order to prolong the battery life. The recognized activity serves as an input to a classifier for energy expenditure estimation, which was also constructed with machine learning. The best-performing classifier turned out to be a composite of two activity-specific classifiers and a general classifier. Its mean absolute error was 0.91 metabolic equivalents of task (MET).