TY - JOUR
T1 - Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion
AU - Lopez-Medina, M. A.
AU - Espinilla, M
AU - Paggetti, Cristiano
AU - Quero, Javier Medina
PY - 2019/8/11
Y1 - 2019/8/11
N2 - 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
AB - 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
KW - Sensor data fusion
KW - Fuzzy logic
KW - Activity recognition
KW - Smart objects
UR - https://www.mdpi.com/1424-8220/19/16/3512
UR - https://pure.ulster.ac.uk/en/publications/activity-recognition-for-iot-devices-using-fuzzy-spatio-temporal-
U2 - 10.3390/s19163512
DO - 10.3390/s19163512
M3 - Article
C2 - 31405220
SN - 1424-8220
VL - 19
SP - 1
EP - 20
JO - Sensors
JF - Sensors
IS - 16
M1 - 3512
ER -