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Sensors
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Sensor Analytics: Maximizing Sensor Data-Driven Insights with Protection and Privacy
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Sensor Analytics: Maximizing Sensor Data-Driven Insights with Protection and Privacy

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 1878

Special Issue Editor

Dr. Rasha Kashef

E-Mail Website
Guest Editor
Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: data science; machine learning; big data; IoT; smart systems; operation research; management science; healthcare; autonomous systems; distributed computing

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has revolutionized the way that we collect and analyze data, providing a wealth of information through the use of sensors and other devices that are connected to the internet. Sensor analytics is the process of collecting, processing, and analyzing data from sensors in order to gain insights and make informed decisions. Sensor analytics involves using advanced algorithms and machine learning techniques to analyze the data from these sensors in real time, and to identify patterns, trends, and anomalies. This can be used to optimize processes, improve efficiency, and make better-informed decisions in a variety of industries, including manufacturing, transportation, healthcare, and agriculture. Sensor analytics plays a crucial role in the IoT, as it allows us to extract insights and make informed decisions based on the data collected from these sensors. In this Special Issue, we will explore the latest developments in sensor analytics and IoT, examining the challenges and opportunities that this technology presents. We will delve into the use of machine learning and artificial intelligence to analyze sensor data in real time and discuss the potential applications of this technology across a range of industries. We will also consider the importance of cybersecurity and privacy in the IoT and examine best practices for protecting against data breaches and ensuring the responsible use of sensor data. This Special Issue will provide a comprehensive overview of the current state of sensor analytics and the IoT and will serve as a valuable resource for researchers and practitioners working in this field.

Dr. Rasha Kashef
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • sensors
  • analytics
  • machine learning
  • IoT
  • security
  • privacy

Published Papers (1 paper)

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Research

27 pages, 3992 KiB  
Article
Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets
by Najmeh Razfar, Rasha Kashef and Farah Mohammadi
Sensors 2023, 23(12), 5513; https://doi.org/10.3390/s23125513 - 12 Jun 2023
Cited by 2 | Viewed by 1429
Abstract
Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment [...] Read more.
Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets—the camera-based method (Vicon) and wearable sensor-based technology (Xsens)—were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors. Full article
(This article belongs to the Special Issue Sensor Analytics: Maximizing Sensor Data-Driven Insights with Protection and Privacy)
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