Abstract
Recognizing human activities is one of the main goals of human-centered intelligent systems. Smartphone sensors produce a continuous sequence of observations. These observations are noisy, unstructured and high dimensional. Therefore, efficient features have to be extracted in order to perform an accurate classification. This paper proposes a combination of Hierarchical and kernel Extreme Learning Machine (HK-ELM) methods to learn features and map them to specific classes in a short time. Moreover, a feature fusion approach is proposed to combine H-ELM based learned features with hand-crafted ones. Our proposed method was found to outperform state-of-the-art in terms of accuracy and training time. It gives an accuracy of 97.62% and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU).
Highlights
Recognizing human activities is one of the main goals of human-centered intelligent systems
This paper proposes a combination of H-Extreme learning machine (ELM) based learned features and hand-crafted features
It gives an accuracy of 97.62% and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU)
Summary
Recognizing human activities is one of the main goals of human-centered intelligent systems. Human Activity Recognition (HAR) is a type of system that automatically observes human activities and maps each activity to its corresponding class. It is connected to different applications such as machine computer interaction, entertainment devices and health monitoring. It plays an important role to permanently monitor children and elderly people by using home-based services. Different data acquisition devices such as smartphone sensors (Accelerometer and Gyro) [1, 2] were used to collect information about the activities. Different activities are classified and recognized by utilizing this data. Sensor based activity recognition is a difficult task because the sensory data is noisy, unstructured, and high dimensional. The process of building a classification model is not an easy task
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