An Automated Daily Sports Activities and Gender Recognition Method Based on Novel Multikernel Local Diamond Pattern Using Sensor Signals

Abstract

Sensor signals have been frequently used for activity recognition and gender classification in the literature. In this study, a new multikernel local diamond pattern (MK-LDP) is proposed as a novel local descriptor method for feature extraction. The proposed MK-LDP is aimed to generate distinctive features from a signal or image using vertical and horizontal diamond-like patterns and multikernel functions. These kernels are signum, ternary, and quaternary. MK-LDP is utilized as a feature extraction technique for human activity recognition (HAR). The proposed HAR method has four fundamental phases, namely, preprocessing, feature generation with MK-LDP, informative features selection using hybrid ReliefF and neighborhood component analysis (RFNCA), and classification using support vector machine (SVM). The proposed MK-LDP extracts 2560 features from the raw sensor signals, RFINCA selects 512 most meaningful ones from the extracted 2560 features, and then the selected 512 features are used as input of the SVM for HAR. Three cases are defined to test the proposed MK-LDP- and RFNCA-based HAR method. These cases are gender classification, activity classification, and both gender and activity classification, respectively. The achieved best accuracy rates are 99.47%, 99.71%, and 99.36% for gender, daily sports activities, and both gender and daily sports activities recognition, respectively. The proposed MK-LDP-based method is also compared to the state-of-the-art and deep learning techniques. The obtained results revealed that the proposed MK-LDP- and RFNCA-based framework is successful HAR using sensor signals.