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Reliability Improvement for Acquired Human Signals
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Reliability Improvement for Acquired Human Signals

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Actuators, Sensors and Devices".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 2609

Special Issue Editors

Prof. Dr. Wing-Kuen Ling

E-Mail Website
Guest Editor
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: biomedical signal denoising; machine learning with applications in biomedical signal classification and regression; nonlinear dynamics with applications in EEG and ECG modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Steve Ling

E-Mail Website
Guest Editor
Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 00099F, Australia
Interests: neural networks; medical imaging; BCI applications and non-invasive bioinstrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health monitoring outside hospitals is very important for patients with chronic diseases. To achieve this goal, human signals need to be acquired using consumer-level wearable devices. Nevertheless, the reliability of the human signals acquired by consumer-level sensors is currently very low. To address this issue, signal-processing-based methods and artificial-intelligence-based methods are employed to improve the reliability of the acquired human signals. This Special Issue mainly focuses on proposing new signal processing methods and artificial-intelligence-based methods to improve the reliability of the acquired human signals.

Prof. Dr. Wing-Kuen Ling
Dr. Steve Ling
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. Journal of Sensor and Actuator Networks 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 2000 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

  • acquired human signals
  • reliability
  • signal processing
  • artificial intelligence
  • consumer-level sensors

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Published Papers (1 paper)

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Research

27 pages, 8188 KiB  
Article
Sensorless Estimation of Human Joint Torque for Robust Tracking Control of Lower-Limb Exoskeleton Assistive Gait Rehabilitation
by Auwalu Muhammad Abdullahi and Ronnapee Chaichaowarat
J. Sens. Actuator Netw. 2023, 12(4), 53; https://doi.org/10.3390/jsan12040053 - 7 Jul 2023
Cited by 9 | Viewed by 2235
Abstract
Patients suffering from motor disorders or weakness resulting from either serious spinal cord injury or stroke often require rehabilitation therapy to regain their mobility. In the lower limbs, exoskeletons have two motors aligned with the patients’ hip and knee to assist in rehabilitation [...] Read more.
Patients suffering from motor disorders or weakness resulting from either serious spinal cord injury or stroke often require rehabilitation therapy to regain their mobility. In the lower limbs, exoskeletons have two motors aligned with the patients’ hip and knee to assist in rehabilitation exercises by supporting the patient’s body structure to increase the torques at the hip and knee joints. Assistive rehabilitation is, however, challenging, as the human torque is unknown and varies from patient to patient. This poses difficulties in determining the level of assistance required for a particular patient. In this paper, therefore, a modified extended state observer (ESO)-based integral sliding mode (ISM) controller (MESOISMC) for lower-limb exoskeleton assistive gait rehabilitation is proposed. The ESO is used to estimate the unknown human torque without application of a torque sensor while the ISMC is used to achieve robust tracking of preset hip and knee joint angles by considering the estimated human torque as a disturbance. The performance of the proposed MESOISMC was assessed using the mean absolute error (MAE). The obtained results show an 85.02% and 87.38% reduction in the MAE for the hip and joint angles, respectively, when the proposed MESOISMC is compared with ISMC with both controllers tuned via LMI optimization. The results also indicate that the proposed MESOISMC method is effective and efficient for user comfort and safety during gait rehabilitation training. Full article
(This article belongs to the Special Issue Reliability Improvement for Acquired Human Signals)
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