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Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare

CICESE (Centro de Investigacion Cientifica y de Investigacion Superior de Ensenada), Ensenada 22860, Mexico
IPN (Instituto Politecnico Nacional), Tijuana 22435, Mexico
CONACYT (Consejo Nacional de Ciencia y Tecnología), Ciudad de Mexico 03940, Mexico
School of Computing, Ulster University, Jordanstown BT37 0QB, UK
INIFAP (Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias), Aguascalientes 20660, Mexico
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3035;
Received: 31 May 2019 / Revised: 27 June 2019 / Accepted: 5 July 2019 / Published: 10 July 2019
Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision. View Full-Text
Keywords: data labeling; gesture recognition; environmental sound recognition; activity recognition; pervasive healthcare data labeling; gesture recognition; environmental sound recognition; activity recognition; pervasive healthcare
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MDPI and ACS Style

Cruz-Sandoval, D.; Beltran-Marquez, J.; Garcia-Constantino, M.; Gonzalez-Jasso, L.A.; Favela, J.; Lopez-Nava, I.H.; Cleland, I.; Ennis, A.; Hernandez-Cruz, N.; Rafferty, J.; Synnott, J.; Nugent, C. Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare. Sensors 2019, 19, 3035.

AMA Style

Cruz-Sandoval D, Beltran-Marquez J, Garcia-Constantino M, Gonzalez-Jasso LA, Favela J, Lopez-Nava IH, Cleland I, Ennis A, Hernandez-Cruz N, Rafferty J, Synnott J, Nugent C. Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare. Sensors. 2019; 19(14):3035.

Chicago/Turabian Style

Cruz-Sandoval, Dagoberto, Jessica Beltran-Marquez, Matias Garcia-Constantino, Luis A. Gonzalez-Jasso, Jesus Favela, Irvin H. Lopez-Nava, Ian Cleland, Andrew Ennis, Netzahualcoyotl Hernandez-Cruz, Joseph Rafferty, Jonathan Synnott, and Chris Nugent. 2019. "Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare" Sensors 19, no. 14: 3035.

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