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Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion

1
Council of Health for the Andalusian Health Service, Av. de la Constitución 18, 41071 Sevilla, Spain
2
Department of Computer Science, Campus Las Lagunillas, 23071 Jaén, Spain
3
I + Srl, Piazza G.Puccini, 26, 50144 Firenze, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(16), 3512; https://doi.org/10.3390/s19163512
Received: 2 June 2019 / Revised: 25 July 2019 / Accepted: 7 August 2019 / Published: 11 August 2019
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Abstract

The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology’s potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers. View Full-Text
Keywords: sensor data fusion; fuzzy logic; activity recognition; smart objects sensor data fusion; fuzzy logic; activity recognition; smart objects
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

López Medina, M.Á.; Espinilla, M.; Paggeti, C.; Medina Quero, J. Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion. Sensors 2019, 19, 3512.

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