Recognition of human activities using machine learning methods with wearable sensors

Body activity recognition using wearable sensor technology has drawn more and more attentions over the past few decades. The complexity and variety of body activities makes it difficult to fast, accurately and automatically recognize body activities. To solve this problem, this paper formulates body activity recognition problem as a classification problem using data collected by wearable sensors. And three different machine learning algorithms, support vector machine, hidden markov model and artificial neural network are presented to recognize different body activities. Various numerical experiments on a real-world wearable sensors dataset are designed to verify the effectiveness of these classification algorithms. Finally the results demonstrate that all the three algorithms achieve satisfactory activity recognition performance.