2018
DOI: 10.3390/ijgi7030081
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A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition

Abstract: The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an … Show more

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Cited by 5 publications
(2 citation statements)
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“…Du et al [ 27 ] have improved the use of RFID tags on objects with a 3-stage activity recognition framework allowing us to capitalize on the usage state of different objects to infer the activity currently being performed and to predict the next activity in line. Galvan-Tejada et al [ 28 ] have used sound data and a Random Forest for the development of an indoor location system relying on a human activity recognition approach: The activity performed by the user allows us to infer their location in the environment. Chapron et al [ 29 ] have focused on bathroom activity recognition using Infrared Proximity Sensors (IRPS) in a cost-efficient system and have achieved an accuracy of 92.8% and 97.3% for toilet use and showering activities.…”
Section: Offline Activity Recognitionmentioning
confidence: 99%
“…Du et al [ 27 ] have improved the use of RFID tags on objects with a 3-stage activity recognition framework allowing us to capitalize on the usage state of different objects to infer the activity currently being performed and to predict the next activity in line. Galvan-Tejada et al [ 28 ] have used sound data and a Random Forest for the development of an indoor location system relying on a human activity recognition approach: The activity performed by the user allows us to infer their location in the environment. Chapron et al [ 29 ] have focused on bathroom activity recognition using Infrared Proximity Sensors (IRPS) in a cost-efficient system and have achieved an accuracy of 92.8% and 97.3% for toilet use and showering activities.…”
Section: Offline Activity Recognitionmentioning
confidence: 99%
“…Additionally, Guo et al [ 7 ] developed an IPS that leverages HAR using inertial sensors and a barometer to detect specific activities, such as going up stairs or opening doors, which are then matched using previously annotated landmarks in the floor plan, aiding improvements to the positioning process. Using sound as a source for HAR, Galván-Tejada et al [ 18 ] developed a room-level IPS, leveraging a Random Forest model to identify the specific features of activities traditionally performed in the same location.…”
Section: Related Workmentioning
confidence: 99%