Abstract
Advances in wearable technologies have the ability to revolutionize and improve people's lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers' bodies and their immediate environment. Human activities recognition, which involves the use of various body sensors and modalities either separately or simultaneously, is one of the most important areas of wearable technology development. In real-life scenarios, the number of sensors deployed is dictated by practical and financial considerations. In the research for this article, we reviewed our earlier efforts and have accordingly reduced the number of required sensors, limiting ourselves to first-person vision data for activities recognition. Nonetheless, our results beat state of the art by more than 4% of F1 score.
Highlights
The demand for human activity recognition (HAR) has increased with the advent of ubiquitous mobile and sensor-rich devices
The first category is based on Machine-learning approaches, these approaches include k-nearest neighbor (K-NN), support vector machine (SVM), hidden Markov models (HMM), decision trees (DT), and some others
The second category of methods is based on neural networks, among these are the methods based on artificial neural networks (ANN), recurrent
Summary
The demand for human activity recognition (HAR) has increased with the advent of ubiquitous mobile and sensor-rich devices. Visual-sensing technology, such as video cameras, are used in vision-based human-activity detection techniques to monitor both an actor’s behavior, and changes in the surroundings. Others, they are utilized to represent activities of varying duration and complexity In the latter scenario, the term ‘‘activity’’ usually refers to a single person’s straightforward behavior that lasts for a brief length of time. Disparate from third-person camera, e.g., cameras used in surveillance systems, the first-person camera provides visual data about the subject wearing the camera It continuously captures the interactions between the subject and other surrounding environment as people and objects. This will directly reflect the preferences from personal and relational perspective of the subject Those interactions yield that first-person visual data is ideal for human activity recognition.
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