Human Activity Recognition and Biomedical Signal Processing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3736

Special Issue Editors


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Guest Editor
Department of Computer Science, Frankfurt University of Applied Sciences, Fb2, 60318 Frankfurt, Germany
Interests: machine learning; signal processing (antenna array signal processing CSI); human activity recognition (HAR); algorithms; probability theory; IoT; ubiquitous computing and software engineering

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Guest Editor
Department of Computer Science, Frankfurt University of Applied Sciences, Fb2, 60318 Frankfurt, Germany
Interests: computational science; machine learning; smart sensors; IoT; human activity recognition (HAR); safety critical systems; condensed matter physics

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Guest Editor
The Intelligent Computer Vision (iCV) Research Lab in the Institute of Technology, University of Tartu, 50411 Tartu, Estonia
Interests: machine learning; computer vision; human–computer interaction; emotion recognition; deep learning; human behaviour analysis
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Special Issue Information

Dear Colleagues,

In the past decade, human activity recognition (HAR) has evolved into a very active research topic with applications in many fields such as security and surveillance and smart environments, including smart homes and healthcare. Biomedical signal processing (BSP), on the other hand, has a long history and tradition in the medical field as biomedical signals provide useful information for clinicians to help them make decisions for clinical diagnoses, patient monitoring, and treatment management. Recently, however, due to the ubiquity of sensors and the availability of new classes of algorithms, entirely new applications are becoming possible. Three trends are fueling this development. First of all, ubiquitous (non-invasive) sensors provided by the Internet of things (IoT), smart phones, smart watches, fitness trackers, etc., generate an ever-increasing amount of data suitable for analysis. Secondly, new classes of algorithms, in particular in the field of machine learning, such as deep learning convolution networks, RNNs, and transformers, enable a better automated understanding of the data. Third, computation power not available in previous decades is now a reality even on edge devices.

In general, there is a significant signal processing challenge in extracting reliable state parameters from the often noisy and imperfect data to characterize the physiological state or HAR. Whilst for applications in the medical field, the potential to help clinicians, medical doctors, and patients themselves in making better real-time decisions is high, the data quality requirements are challenging, too.

The aim of this Special Issue is to bring together original research and review articles researching the potential and challenges of extracting physiological parameters characteristic but not limited to HAR and using traditional and modern techniques of interpreting BSP in both the medical and non-medical fields.

Therefore, this Special Issue encourages the submission of state-of-the-art research in HAR and biomedical signal processing applications, as well as fundamental research relevant to the subject. Topics of interest include (but are not limited to) the following subject categories:

  • New algorithms for HAR and/or BSP, including statistical analysis, machine learning, and deep learning;
  • Data preparation and cleansing techniques for time series in HAR and/or BSP;
  • Architecture of HAR and/or BSP applications in medical and non-medical fields;
  • Sensor fusion in HAR and/or BSP applications in medical and non-medical fields;
  • Wearable HAR and/or BSP applications in medical and non-medical fields.

Prof. Dr. Jörg Schäfer
Prof. Dr. Matthias Wagner
Prof. Dr. Gholamreza Anbarjafari
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • human activity recognition (HAR)
  • bio medical signal processing (BSP)
  • biomedical informatics
  • neural networks
  • signal processing
  • wearables
  • signal processing of data collected using noninvasive sensors
  • electrocardiogram (ECG)
  • electroencephalogram (EEG)
  • electromyogram (EMG)
  • radio signals (RSSI)
  • channel state information (CSI)
  • Internet of things (IoT)

Published Papers (2 papers)

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Research

15 pages, 3577 KiB  
Article
An Efficient EEG Signal Analysis for Emotion Recognition Using FPGA
by M. R. Ezilarasan and Man-Fai Leung
Information 2024, 15(6), 301; https://doi.org/10.3390/info15060301 - 24 May 2024
Viewed by 302
Abstract
Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA) [...] Read more.
Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA) was employed to eliminate artifacts from the raw brain signals before applying signal extraction to a convolutional neural network (CNN) for emotion identification. These features were then learned by the proposed CNN-LSTM (long short-term memory) algorithm, which includes a ResNet-152 classifier. The CNN-LSTM with ResNet-152 algorithm was used for the accurate detection and analysis of human emotional data. The SEED V dataset was employed for data collection in this study, and the implementation was carried out using an Altera DE2 FPGA development board, demonstrating improved performance in terms of FPGA speed and area optimization. Full article
(This article belongs to the Special Issue Human Activity Recognition and Biomedical Signal Processing)
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18 pages, 5731 KiB  
Article
An Improved Method of Heart Rate Extraction Algorithm Based on Photoplethysmography for Sports Bracelet
by Binbin Ren, Zhaoyuxuan Wang, Kainan Ma, Yiheng Zhou and Ming Liu
Information 2023, 14(5), 297; https://doi.org/10.3390/info14050297 - 19 May 2023
Cited by 1 | Viewed by 2424
Abstract
Heart rate measurement employing photoplethysmography (PPG) is a prevalent technique for wearable devices. However, the acquired PPG signal is often contaminated with motion artifacts, which need to be accurately removed. In cases where the PPG and accelerometer (ACC) spectra overlap at the actual [...] Read more.
Heart rate measurement employing photoplethysmography (PPG) is a prevalent technique for wearable devices. However, the acquired PPG signal is often contaminated with motion artifacts, which need to be accurately removed. In cases where the PPG and accelerometer (ACC) spectra overlap at the actual heart rate, traditional discrete Fourier transform (DFT) algorithms fail to compute the heart rate accurately. This study proposed an enhanced heart rate extraction algorithm based on PPG to address the issue of PPG and ACC spectral overlap. The spectral overlap is assessed according to the morphological characteristics of both the PPG and ACC spectra. Upon detecting an overlap, the singular spectrum analysis (SSA) algorithm is employed to calculate the heart rate at the given time. The SSA algorithm effectively resolves the issue of spectral overlap by removing motion artifacts through the elimination of ACC-related time series in the PPG signal. Experimental results reveal that the accuracy of the proposed algorithm surpasses that of the traditional DFT method by 19.01%. The proposed method makes up for the deficiency posed by artifact and heart rate signal overlap in conventional algorithms and significantly improves heart rate extraction accuracy. Full article
(This article belongs to the Special Issue Human Activity Recognition and Biomedical Signal Processing)
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