Situation identification in smart wearable computing systems based on machine learning and Context Space Theory

• DOI: 10.1016/j.inffus.2023.102197
• Publication Date: 2023-12-16T16:27:37Z
• Authors: Giuseppe D’Aniello; Matteo Gaeta; Raffaele Gravina; Qimeng Li; Zia Ur Rehman; Giancarlo Fortino
• Keywords: Identification; Activity Recognition; Wearable Technology; Smart environment; Sensor Fusion

Wearable devices and smart sensors are increasingly adopted to monitor the behaviors of human artificial agents. Many applications rely on capability such recognize daily life activities performed by monitored users in order tailor their with respect occurring situations. Despite constant evolution sensing technologies numerous research this field, an accurate recognition in-the-wild situations still represents open challenge. This work proposes a novel approach for situation identification capable recognizing which they occur different environments behavioral contexts, processing data acquired wearable environmental sensors. An architecture situation-aware computing system is proposed, inspired Endsley's situation-awareness model, consisting two-step identification. The first identifies via learning-based technique. Simultaneously, context recognized using Context Space Theory. Finally, fusion between state allows identifying complex user acting. knowledge regarding forms basis smarter can be realized. has been evaluated ExtraSensory public dataset compared state-of-the-art techniques, achieving accuracy 96% significantly low computational time, demonstrating efficacy approach.