High Performance Activity Recognition Framework for Ambient Assisted Living in The Home Network Environment
| Autor: | Mitsuru Ikeda, Azman Osman Lim, Konlakorn Wongpatikaseree, Yasuo Tan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science ontology-based activity recognition Machine learning computer.software_genre context sensor network Activity recognition human posture Artificial intelligence activity recognition Electrical and Electronic Engineering business computer range-based algorithm Software Assisted living activity's location in activity log |
| Zdroj: | IEICE Transactions on Communication. (9):1766-1778 |
| ISSN: | 0916-8516 |
| Popis: | Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL^2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surronding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL^2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an anverage accuracy of 96.60% |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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