TY - JOUR

T1 - Analyzing activity recognition uncertainties in smart home environments

AU - Kim, E.

AU - Helal, Sumi

AU - Nugent, C.

AU - Beattie, M.

PY - 2015/8

Y1 - 2015/8

N2 - 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. © 2015 ACM 2157-6904/2015/08-ART52 $15.00.

AB - 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. © 2015 ACM 2157-6904/2015/08-ART52 $15.00.

KW - Human activity recognition and activity model

KW - Uncertainty analysis

KW - Automation

KW - Human computer interaction

KW - Intelligent buildings

KW - Pattern recognition

KW - Space applications

KW - Accuracy limitations

KW - Accuracy measures

KW - Activity modeling

KW - Activity recognition

KW - Heterogeneous sources

KW - Real world deployment

KW - Smart space applications

KW - Uncertainty sources

U2 - 10.1145/2651445

DO - 10.1145/2651445

M3 - Journal article

VL - 6

JO - ACM Transactions on Intelligent Systems and Technology

JF - ACM Transactions on Intelligent Systems and Technology

SN - 2157-6904

IS - 4

M1 - 52

ER -