Analyzing Activity Recognition Uncertainties in Smart Home Environments

Abstract

In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space applications, state-of-the-art AR technology is not ready or adequate for real-world deployments due to its insufficient accuracy. The accuracy limitation is directly attributed to uncertainties stemming from multiple sources in the AR system. Hence, one of the major goals of AR research is to improve system accuracy by minimizing or managing the uncertainties encountered throughout the AR process. As we cannot manage uncertainties well without measuring them, we must first quantify their impact. Nevertheless, such a quantification process is very challenging given that uncertainties come from diverse and heterogeneous sources. In this article, we propose an approach, which can account for multiple uncertainty sources and assess their impact on AR systems. We introduce several metrics to quantify the various uncertainties and their impact. We then conduct a quantitative impact analysis of uncertainties utilizing data collected from actual smart spaces that we have instrumented. The analysis is intended to serve as groundwork for developing "diagnostic" accuracy measures of AR systems capable of pinpointing the sources of accuracy loss. This is to be contrasted with the currently used accuracy measures.

Subscribing to fuzzy temporal aggregation of heterogeneous sensor streams in real‐time distributed environments

“…Intelligent systems for smart environments require a proper uncertainty treatment to obtain a high performance and accurate results [39] in relevant topics, such as activity recognition [38,66], which provides a key successful solution to the aging population [67] helping elderly people to stay with the best quality of life as long as possible in their homes. So, the fuzzy linguistic processing of this work has been focused on describing daily activities from heterogeneous sensor of a complex scene to provide an interpretable, intuitive, and flexible representation that can be analyzed by humans and intelligent computer systems.…”

“…This innovative approach provides an intuitive linguistic representation of mobile and ambient sensors as well as implies a drastic reduction of the communication burden. The proposed data modeling is based on the fuzzy linguistic approach and fuzzy logic [28] because it has provided successful results in developing intelligent systems using the data provided by the sensors [11,[29][30][31][32][33] highlighting the development of renewable energy and energy-saving systems [34][35][36][37].The managing of uncertainty and vagueness is key in intelligent systems, such as activity recognition [38], to obtain high performance and results [39]. Finally, in order to illustrate the usefulness and effectiveness of our proposal, we present the results of the fuzzy temporal aggregation of sensor streams with alpha-cut subscriptions in a case study where an inhabitants performs an daily activities in an intelligent environment.…”

“…Among them, it is noteworthy ZeroC Ice [24], an object-oriented distributed computing tool with support for several languages and platforms, Global Sensor Network (GSN) that applies sliding window management [25] in changing data stream [26] and, finally, OpenIot, which increases the characteristics of GSN adding top-k subscriptions over sliding windows (top-k/w subscriptions) [27] and semantic open-linked data techniques.In the context of this state of the art, the aim of this paper is to provide a straightforward approach that processes heterogeneous sensor streams using linguistic terms in real-time intelligent environments. The proposed data modeling is based on the fuzzy linguistic approach and fuzzy logic [28] because it has provided successful results in developing intelligent systems using the data provided by the sensors [11,[29][30][31][32][33] highlighting the development of renewable energy and energy-saving systems [34][35][36][37].The managing of uncertainty and vagueness is key in intelligent systems, such as activity recognition [38], to obtain high performance and results [39]. In this way, sensor-based activity recognition is a relevant research topic that integrates pervasive and mobile computing [40,41], context-aware computing [42][43][44], and ambient assisted living [45,46].In order to illustrate the usefulness and effectiveness of our proposal, we present the results in terms of reduction of the communication burden of the fuzzy temporal aggregation of sensor streams with alpha-cut subscriptions in a case study where an inhabitants performs an daily activities in an intelligent environment.…”

Evaluation of Wellness Detection Techniques using Complex Activities Association for Smart Home Ambient

“…E. Kim., et al [4] have discussed the uncertainty parameters in the wellness determination. This study serves as groundwork to develop diagnostic system.…”

“…There are broadly three categories of activities. These categories are concurrent activities, interleaved and simple atomic activities as reported in [4]. However, most of the literature does not consider all type of activities for their activity recognition and wellness detection of elderly people in smart homes ambient.…”