Abstract
In this paper, we propose a new method for improving human activity recognition (HAR) datasets in order to increase their classification accuracy when trained with a certain classifier like a Neural Network. In this paper a novel training/testing process for building/testing a classification model for human activity recognition (HAR) is proposed. Traditionally, HAR is done by a classifier that learns what activities a person is doing by training with skeletal data obtained from a motion sensor such as Microsoft Kinect or accelerometer sensors. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. In addition to the spatial features that describe current positions in the skeletal data, new features called Shadow Features are used to improve the supervised learning efficiency and accuracy of Neural Network classifiers. Shadow Features are inferred from the dynamics of body movements, thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterizing activities in the classification process and thus significantly improving the accuracy. These Shadow Features are generated based on the existing features obtained from sensor datasets. In this paper we show that the accuracy of a neural network classifier can be significantly improved by the addition of Shadow Features and we also show that the generation of Shadow Features can be achieved with little time cost, on the fly, with the NVIDIA GPU technology and the CUDA programming model, this way we can improve the Neural Network accuracy at almost no time cost. GPUs are particularly suitable for generating Shadow Features, since they possess multiple cores which can be taken advantage of, in order to generate Shadow Features for multiple data columns in parallel, therefore reducing a lot of processing time, especially when dealing with huge datasets.
Highlights
Human activity recognition (HAR) [3, 15, 31, 32] is a branch of machine learning that trains a classification model using historical activity records to recognize unseen activities
A new concept of feature is presented, which is called Shadow Features, with the goal of improving existing human activity recognition (HAR) datasets, and we show that these features can be computed and appended to an existing dataset on the fly, with very small time cost by using NVIDIA GPUs and the CUDA programming model to generate such features
For each Q size, we provide a comparison on the maximum accuracy attained by a neural network training/testing the Kinect dataset with Shadow Features generated by different statistical operations which are Number of input Neurons
Summary
Human activity recognition (HAR) [3, 15, 31, 32] is a branch of machine learning that trains a classification model using historical activity records to recognize unseen activities. The sensor data arrives in the form of continuous sequential values, similar to a time series Time series such as those collected from an accelerometer are usually multivariate, comprising tri-axial information, known in its simplest form as three features (x, y, z). It is very common to use the GPU to accelerate data processing tasks due to its parallel computing capabilities, as was shown in [4] in which the authors take advantage of the GPU to reduce their model training time through the simultaneous analysis of multiple inputs. In our proposed work we take advantage of the parallel processing capabilities of the GPU in order to accelerate the shadow features generation process as well as the Neural Network training and prediction times
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