Interaction-Feature Enhanced Multiuser Model Learning for a Home Environment Using Ambient Sensors

Abstract

Activity recognition (AR) is a key enabler for a context-aware smart home since knowing what the residents' current activities helps a smart home provide more desirable services. This is why AR is often used in assistive technologies for cognitively impaired people to evaluate their abilities to undertake activities of daily living. In a real-life scenario, multiple-resident AR has been considered as a very challenging problem, primarily due to the complexity of data association. In addition, most prior research has not considered the potential interpersonal interactions among residents to simplify complexity, especially in an environment monitored by ambient sensors. In this study, we propose two types of multiuser activity models, both of which are derived from an interaction-feature enhanced multiuser model learning framework. These two models consider interpersonal interactions and data association for multiuser AR using ambient sensors. We then compare their performance with the other two baseline models with or without consideration of data association and interpersonal interactions. The experimental results show that the derived models outperform other baseline classifiers. Therefore, the proposed approach can increase the opportunities for providing context-aware services for a multiresident smart home. C 2014 Wiley Periodicals, Inc.

GARSAaaS: group activity recognition and situation analysis as a service

“…In terms of comparing with the state of the art, it is difficult to provide a fair quantitative comparison (such as classification accuracy or algorithm performance) given that there is no widely accepted benchmark in the area of sensor-based group activity recognition. Most of the studies simulated their experiments and executed their proposed algorithms offline, e.g., [7,15,27,35], in which, challenges such as the inaccuracy caused by communication delay has been disregarded. Also, having domaindependent parameters can influence results, such as window size variations.…”

Multi Resident Complex Activity Recognition in Smart Home: A Literature Review

“…E.g. : hanging clothes together [42], [43] Activities such as concurrent and interleave activity can occur in both single and multiresident situation. However, concurrent and parallel activities differ in term of different location sensor signal.…”

Activity-Aware Energy-Efficient Automation of Smart Buildings

“…On the other hand, discriminative approaches that model the boundary between different activity classes offer an effective alternative. These techniques include decision trees, meta classifiers based on boosting and bagging, support vector machines, and discriminative probabilistic graphical models such as conditional random fields [20,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67]. Other approaches combine these underlying learning algorithms, including boosting and other ensemble methods [68][69][70][71].…”