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Uses of Image and Speech Processing, Sensor Fusion, the Cloud...
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Uses of Image and Speech Processing, Sensor Fusion, the Cloud and Multimedia for Healthcare Applications

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

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 3054

Special Issue Editors

Dr. Farzan Majeed Noori

E-Mail Website
Guest Editor
Department of Informatics, University of Oslo, 0373 Oslo, Norway
Interests: sensor fusion; healthcare; multimodal; computer vision; multimedia
Dr. Azeem Hafeez

E-Mail Website
Guest Editor
Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Interests: automotive cyber-security
Prof. Dr. Hafiz Malik

E-Mail Website
Guest Editor
Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Interests: automotive cybersecurity; sensor security; information integrity verification; multimedia and digital forensics; information fusion; pattern recognition
Dr. Md Zia Uddin

E-Mail Website
Guest Editor
Sustainable Communication Technologies Department, SINTEF Digital, 0373 Oslo, Norway
Interests: sensors; healthcare; human-machine/computer/robot interaction; pattern recognition; deep learning; artificial intelligence; machine learning; big data; robotics; image processing; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing and imaging technologies are evolving, and their applications in healthcare are substantial. Regarding biomedical data, the extraction of features from various different formats and their homogeneous representation are areas of interest in medical applications. Additionally, the data fusion methods used in the Internet of Things enable "smart" healthcare systems, improving quality of life, health, and well-being.

This Special Issue invites submissions of original research and novel work on image and speech processing, sensor fusion, the cloud and multimedia for healthcare applications, covering a wide range of areas such as:

  • Imaging technology in healthcare;
  • Speech processing in healthcare;
  • Health rehabilitation;
  • Sensors fusion in healthcare;
  • Cloud and multimedia for digital health;
  • Health and safety risk assessments;
  • Wireless sensors networks for healthcare;
  • Smart health diagnostics;
  • Robotics in healthcare;
  • Internet of Things in healthcare;
  • Smart wearables healthcare;
  • Sensor fusion in biomedical imaging.

Dr. Farzan Majeed Noori
Dr. Azeem Hafeez
Prof. Dr. Hafiz Malik
Dr. Md Zia Uddin
Guest Editors

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

  • imaging technology in healthcare
  • speech processing in healthcare
  • health rehabilitation
  • sensors fusion in healthcare
  • cloud and multimedia for digital health
  • health and safety risk assessments
  • wireless sensors networks for healthcare
  • smart health diagnostics
  • robotics in healthcare
  • internet of Things in healthcare
  • smart wearables healthcare
  • sensor fusion in biomedical imaging

Published Papers (2 papers)

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Research

19 pages, 2453 KiB  
Article
TranStutter: A Convolution-Free Transformer-Based Deep Learning Method to Classify Stuttered Speech Using 2D Mel-Spectrogram Visualization and Attention-Based Feature Representation
by Krishna Basak, Nilamadhab Mishra and Hsien-Tsung Chang
Sensors 2023, 23(19), 8033; https://doi.org/10.3390/s23198033 - 22 Sep 2023
Viewed by 1125
Abstract
Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the accurate classification of stuttering types. The researchers introduce “TranStutter”, a pioneering Convolution-free Transformer-based DL model, designed to excel in [...] Read more.
Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the accurate classification of stuttering types. The researchers introduce “TranStutter”, a pioneering Convolution-free Transformer-based DL model, designed to excel in speech disfluency classification. Unlike conventional methods, TranStutter leverages Multi-Head Self-Attention and Positional Encoding to capture intricate temporal patterns, yielding superior accuracy. In this study, the researchers employed two benchmark datasets: the Stuttering Events in Podcasts Dataset (SEP-28k) and the FluencyBank Interview Subset. SEP-28k comprises 28,177 audio clips from podcasts, meticulously annotated into distinct dysfluent and non-dysfluent labels, including Block (BL), Prolongation (PR), Sound Repetition (SR), Word Repetition (WR), and Interjection (IJ). The FluencyBank subset encompasses 4144 audio clips from 32 People Who Stutter (PWS), providing a diverse set of speech samples. TranStutter’s performance was assessed rigorously. On SEP-28k, the model achieved an impressive accuracy of 88.1%. Furthermore, on the FluencyBank dataset, TranStutter demonstrated its efficacy with an accuracy of 80.6%. These results highlight TranStutter’s significant potential in revolutionizing the diagnosis and treatment of stuttering, thereby contributing to the evolving landscape of speech pathology and neurodevelopmental research. The innovative integration of Multi-Head Self-Attention and Positional Encoding distinguishes TranStutter, enabling it to discern nuanced disfluencies with unparalleled precision. This novel approach represents a substantial leap forward in the field of speech pathology, promising more accurate diagnostics and targeted interventions for individuals with stuttering disorders. Full article
(This article belongs to the Special Issue Uses of Image and Speech Processing, Sensor Fusion, the Cloud and Multimedia for Healthcare Applications)
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13 pages, 5475 KiB  
Article
Brain Connectivity Analysis in Distinct Footwear Conditions during Infinity Walk Using fNIRS
by Haroon Khan, Marco Antonio Pinto-Orellana and Peyman Mirtaheri
Sensors 2023, 23(9), 4422; https://doi.org/10.3390/s23094422 - 30 Apr 2023
Cited by 2 | Viewed by 1362
Abstract
Gait and balance are an intricate interplay between the brain, nervous system, sensory organs, and musculoskeletal system. They are greatly influenced by the type of footwear, walking patterns, and surface. This exploratory study examines the effects of the Infinity Walk, pronation, and footwear [...] Read more.
Gait and balance are an intricate interplay between the brain, nervous system, sensory organs, and musculoskeletal system. They are greatly influenced by the type of footwear, walking patterns, and surface. This exploratory study examines the effects of the Infinity Walk, pronation, and footwear conditions on brain effective connectivity patterns. A continuous-wave functional near-infrared spectroscopy device collected data from five healthy participants. A highly computationally efficient connectivity model based on the Grange causal relationship between the channels was applied to data to find the effective relationship between inter- and intra-hemispheric brain connectivity. Brain regions of interest (ROI) were less connected during the barefoot condition than during other complex walks. Conversely, the highest interconnectedness between ROI was observed while wearing flat insoles and medially wedged sandals, which is a relatively difficult type of footwear to walk in. No statistically significant (p-value <0.05) effect on connectivity patterns was observed during the corrected pronated posture. The regions designated as motoric, sensorimotor, and temporal became increasingly connected with difficult walking patterns and footwear conditions. The Infinity Walk causes effective bidirectional connections between ROI across all conditions and both hemispheres. Due to its repetitive pattern, the Infinity Walk is a good test method, particularly for neuro-rehabilitation and motoric learning experiments. Full article
(This article belongs to the Special Issue Uses of Image and Speech Processing, Sensor Fusion, the Cloud and Multimedia for Healthcare Applications)
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