NDLI: Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors

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Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors

Content Provider	MDPI
Author	Moreira, Dinis Barandas, Marília Rocha, Tiago Alves, Pedro Santos, Ricardo Leonardo, Ricardo Vieira, Pedro Gamboa, Hugo
Copyright Year	2021
Description	With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.
Starting Page	6316
e-ISSN	14248220
DOI	10.3390/s21186316
Journal	Sensors
Issue Number	18
Volume Number	21
Language	English
Publisher	MDPI
Publisher Date	2021-09-21
Access Restriction	Open
Subject Keyword	Sensors Industrial Engineering Smartphone Inertial Sensors Deep Learning Human Activity Recognition Indoor Location
Content Type	Text
Resource Type	Article

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