Special Issue Information

Dear Colleagues,

Sensor fusion and statistical signal processing play an essential role in modern daily life, due to the widespread diffusion of devices and technologies applied to Internet of Things technologies.

The application field concerns the localization, healthcare, autonomous vehicle, intelligent transportation system, home automation, industry, robotics, automated guided vehicles in manufacturing lines, first responder navigation, vehicular navigation, asset navigation and tracking, industry automation, indoor unmanned vehicle, etc.

Usually, the research is addressed to design and implement data fusion methods using the already available technologies or realize low-cost sensors. Sensor fusion and statistical signal processing are key elements for further advances in the field and present exciting challenges for signal processing practitioners and researchers.

Due to the large variety of technologies and standards involved, a data fusion algorithm typically needs to account for several technologies, such as filtering, Kalman, Bayesian filtering, system identification, communication channel models, machine learning, etc.

This Special Issue, entitled ‘Sensor Fusion and Statistical Signal Processing’, we solicit paper submissions of original works addressing fundamentals, supporting technologies, and technical issues on the data fusion of heterogeneous technologies. The topics not only cover design and analysis, but concern the realization and implementation of sensor fusion.

This Special Issue of Signals aims to publish novel results on the most recent developments in sensor fusion and statistical signal processing, emphasizing the integration of various technologies for improved performance.

Dr. Guido De Angelis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Signals is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sensor fusion
Kalman filter
machine learning
sensors
signal processing algorithms
filtering
statistical signal processing

Published Papers (1 paper)

Research

16 pages, 751 KiB

Open AccessArticle

Sensor-Based Prediction of Mental Effort during Learning from Physiological Data: A Longitudinal Case Study

by Ankita Agarwal, Josephine Graft, Noah Schroeder and William Romine

Signals 2021, 2(4), 886-901; https://0-doi-org.brum.beds.ac.uk/10.3390/signals2040051 - 03 Dec 2021

Cited by 2 | Viewed by 2093

Abstract

Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant’s self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student’s physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device. Full article

(This article belongs to the Special Issue Sensor Fusion and Statistical Signal Processing)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sensor Fusion and Statistical Signal Processing

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (1 paper)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI