Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm, etc. Thus, a solution is presented under the guidance of right prior knowledge, using Back-Propagation neural networks (BP networks) and simple Convolutional Neural Networks (CNN). The results show that HAR can be achieved with 90%–100% accuracy. Further analysis shows that intelligent algorithms for pattern recognition and classification problems, typically represented by HAR, require correct prior knowledge to work effectively.