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Inertial Sensors for Patient Monitoring and Rehabilitation

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 2589

Special Issue Editor


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Guest Editor
Università degli Studi di Modena e Reggio Emilia, Modena, Italy
Interests: gait analysis; inertial sensors; motion capture; pedestrian dead-reckoning; cerebral palsy; Parkinson’s disease

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue of Sensors entitled “Inertial Sensors for Patient Monitoring and Rehabilitation”. The use of disruptive innovative devices in rehabilitation is revolutionizing the way we manage, monitor, diagnose, and treat patients with motor disorders. Inertial sensors, in particular, are making a breakthrough in medical practice by helping healthcare professionals to engage individuals as active members in self-care, shifting the focus from hospital-oriented to domestic-centered care. This digital transformation is leveraging on standalone systems and inertial data sampled from smartphone or wearable sensors, increasingly allowing remote monitoring of patients’ physical conditions and progress and running of rehabilitation sessions. The implementation of tutoring mobile apps in the form of virtual therapists or of biofeedback-based solutions will strongly promote and encourage adherence to rehabilitation programs, especially in complex social or environmental situations such as the COVID-19 pandemic, where the possibility of patients reaching therapeutic centers is severely limited.

We invite high-quality research papers as well as review articles that describe current and expected challenges, along with potential solutions for patient monitoring and rehabilitation using inertial sensors.

Potential topics include but are not limited to the following:

Health monitoring:

  • New concepts in health sensing technology (e.g., mobile, wearable, sensor fusion);
  • Biomedical signal processing methods to improve the reliability of motion data;
  • Technological solutions to quantify individual motor conditions;
  • Digital approaches for health and disease surveillance;
Tutoring and inertial sensors-based applications:
  • Technological approaches to treat and rehabilitate motor functions;
  • Interventions based on mobile apps including approaches with virtual therapists;
  • User experience—how patients or clinicians consume health data.
Dr. Alberto Ferrari
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

  • Health enhancement
  • Inertial sensors
  • Sensor fusion
  • Domestic-centered care
  • Motion capture
  • Movement analysis
  • Health monitoring
  • Biomedical signal processing and algorithms

Published Papers (1 paper)

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Research

18 pages, 2113 KiB  
Article
The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait
by Dylan den Hartog, Jaap Harlaar and Gerwin Smit
Sensors 2021, 21(19), 6636; https://doi.org/10.3390/s21196636 - 06 Oct 2021
Cited by 3 | Viewed by 2209
Abstract
Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would [...] Read more.
Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used to detect and classify stumbling events in a dataset comprising of a wide variety of daily movements. Ten healthy test subjects were deliberately tripped by an unexpected and unseen obstacle while walking on a treadmill. The subjects stumbled a total of 276 times, both using an elevating recovery strategy and a lowering recovery strategy. Subjects also performed multiple Activities of Daily Living. During data processing, an event-defined window segmentation technique was used to trace high peaks in acceleration that could potentially be stumbles. In the reduced dataset, time windows were labelled with the aid of video annotation. Subsequently, discriminative features were extracted and fed to train seven different types of machine learning algorithms. Trained machine learning algorithms were validated using leave-one-subject-out cross-validation. Support Vector Machine (SVM) algorithms were most successful, and could detect and classify stumbles with 100% sensitivity, 100% specificity, and 96.7% accuracy in the independent testing dataset. The SVM algorithms were implemented in a user-friendly, freely available, stumble detection app named Stumblemeter. This work shows that stumble detection and classification based on SVM is accurate and ready to apply in clinical practice. Full article
(This article belongs to the Special Issue Inertial Sensors for Patient Monitoring and Rehabilitation)
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