Human-sitting-pose detection using data classification and dimensionality reduction

The research area of sitting-pose analysis allows for preventing a range of physical health problems mainly physical. Despite that different systems have been proposed for sitting-pose detection, some open issues are still to be dealt with, such as: Cost, computational load, accuracy, portability, and among others. In this work, we present an alternative approach based on a sensor network to acquire the position-related variables and machine learning techniques, namely dimensionality reduction (DR) and classification. Since the information acquired by sensors is high-dimensional and therefore it might not be saved into embedded system memory, a DR stage based on principal component analysis (PCA) is performed. Subsequently, the automatic posed detection is carried out by the k-nearest neighbors (KNN) classifier. As a result, regarding using the whole data set, the computational cost is decreased by 33 % as well as the data reading is reduced by 10 ms. Then, sitting-pose detection task takes 26 ms, and reaches 75% of accuracy in a 4-trial experiment.