Correction to: Neural Computing and Applications https://doi.org/10.1007/s00521-022-07911-0


The URL given in the end of the abstract with hyper image format has been inadvertently missed to be process during PRE stage and the same has been missed to check in the further stages also.

In this article, the source code provided for GitHub link in the abstract beginning was missing in the article.

The corrected details are given below:


Abstract


Capturing time and frequency relationships of time-series signals offers an inherent barrier for automatic human activity recognition (HAR) from wearable sensor data. Extracting spatiotemporal context from the feature space of the sensor reading sequence is challenging for the current recurrent, convolutional, or hybrid activity recognition models. The overall classification accuracy also gets affected by large size feature maps that these models generate. To this end, in this work, we have put forth a hybrid architecture for wearable sensor data-based HAR. We initially use continuous wavelet transform to encode the time series of sensor data as multi-channel images. Then, we utilize a spatial attention-aided convolutional neural network (CNN) to extract higher-dimensional features. To find the most essential features for recognizing human activities, we develop a novel feature selection (FS) method. In order to identify the fitness of the features for the FS, we first employ three filter-based methods: mutual information (MI), Relief-F, and minimum redundancy maximum relevance (mRMR). The best set of features is then chosen by removing the lower-ranked features using a modified version of the genetic algorithm (GA). The K-nearest neighbors (KNN) classifier is then used to categorize human activities. We conduct comprehensive experiments on five well-known, publicly accessible HAR datasets, namely UCI-HAR, WISDM, MHEALTH, PAMAP2, and HHAR. Our model significantly outperforms the state-of-the-art models in terms of classification performance. We also observe an improvement in overall recognition accuracy with the use of GA-based FS technique with a lower number of features. The source code of the paper is publicly available here https://github.com/apusarkar2195/HAR_WaveletTransform_SpatialAttention_FeatureSelection.