A context‐awareness model for activity recognition in robot‐assisted scenarios. Issue 2 (11th December 2019)

• Record Type: Journal Article
• Authors: Rodriguez Lera, Francisco J.; Martín Rico, Francisco; Guerrero Higueras, Angel Manuel; Olivera, Vicente Matellán
• Is Part Of: Expert systems. Volume 37:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-11
• Subjects: activity recognition -- Bayesian network -- context awareness -- knowledge representation; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12481
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Context awareness in ambient assisted living programmes for the elderly is a cornerstone in the current scenario of noncustomized service robots distributed around the world. This research proposes a context‐awareness system for a human–robot scene interpretation based on seven primary contexts and the American Occupational Therapy Association. The context‐awareness system defined here proposes an inference mechanism for the activity recognition supported on hierarchical Bayesian networks. However, when the information from sensors increases, the computational cost associated also increases. Thus, an evaluation of different Bayesian network models is necessary for decreasing its impact over the robot performance. Two topological models have been modelled and tested using OpenMarkov application: a two‐level approach of an input–observations layer and the activity recognition layer, and a three‐layer model setting apart a primary contexts layer, the input–observations layer, and the activity recognition layer. The qualitative and quantitative results presented here show better performance in terms of memory and memory in a three‐layer model. Besides, its effect on a hybrid architecture of a robotic platform is presented.