Flowchart for deciding suitable shaping parameter.
Under the current trend, whenever it comes to recognition, one will intuitively think of neural networks. In the training state of neural networks has been considering the larger the amount of data, the better. However, when data size grows, if without a filtering process, it may contain redundant data and extract repetitive features. Therefore, our goal here is to find efficient input data, rather than simply input more data. By doing so, we obtained higher recognition accuracy. Considering efficient input attributes for activity recognition, we proposed a data preprocessing process, namely data shaping which contained 4 parameters, sampling frequency, sliding window size, sliding window overlap rate and derived attributes respectively. Raw data were converted into derived data through data shaping, thereby improving the representativeness of the data and improving the accuracy. We utilized DSADS, WARD, Opportunity, UCI-HAR and PAMP2 dataset for experiments, and used 3 types of convolutional neural networks for training and testing. Through our experimental results, we knew that applying our method to the raw data can actually increase the recognition accuracy.
Results of shpaing data in 4 datasets trained by 3 architectures.
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