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

Miguel Ángel López Medina,Cristiano Paggeti,Javier Medina Quero,Macarena Espinilla

doi:10.3390/s19163512

Miguel Ángel López Medina, Cristiano Paggeti + Show 2 more

Open Access

https://doi.org/10.3390/s19163512

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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.

Highlights

Activity Recognition (AR) defines models able to detect human actions and their goals in smart environments with the aim of providing assistance
Based on the results shown in the case study, we defend that the use of fuzzy logic to extract spatial-temporal features from heterogeneous sensors constitutes a suitable model for representation and learning purposes in AR
The aim of this work was to describe and fuse the information from heterogeneous sensors in an efficient and lightweight manner in order to enable Internet of Things (IoT) devices to compute spatial-temporal features in AR, which can be deployed in fog computing architectures

Summary

Introduction

Activity Recognition (AR) defines models able to detect human actions and their goals in smart environments with the aim of providing assistance Such methods have increasingly been adopted in smart homes [1] and healthcare applications [2] aiming both at improving the quality of care services and allowing people to stay independent in their own homes for as long as possible [3]. Wearable devices have been used to analyze activities and gestures in AR [7] Recent paradigms such as edge computing [8] or fog computing [9] place the the data and services within the devices where data are collected, providing virtualized resources and engaged location-based services, at the edge of the mobile networks [10]. Fog computing has had a great impact, between ambient devices [15] and wearable devices [16]

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