Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
IMU and Innovative Sensors for Healthcare
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

IMU and Innovative Sensors for Healthcare

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

Deadline for manuscript submissions: 25 October 2024 | Viewed by 2917

Special Issue Editors

Dr. Paolo Capodaglio

E-Mail Website
Guest Editor
Ospedale San Giuseppe, Istituto Auxologico Italiano, IRCCS, Strada Luigi Cadorna 90, 28824 Piancavallo, VB, Italy
Interests: IMU; physical and rehabilitation medicine; functional evaluation and instrumental assessment; ageing and pathological conditions; spinal cord injuries; musculoskeletal disorders; obesity and metabolic conditions; monitoring physical work load in health workers and other occupational activities
Special Issues, Collections and Topics in MDPI journals
Dr. Veronica Cimolin

E-Mail Website
Guest Editor
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Interests: bioengineering; movement analysis; biomechanics; rehabilitation; healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The proposed Special Issue IMU and Innovative Sensors for Healthcare is focused on new IMU and sensors, measurement techniques and their applications in healthcare. Recent technologies offer innovative solutions to modernize health care and meet demands at a low cost. They represent novel solutions to several relevant challenges in healthcare, such as an early detection of pathologies, a minimally invasive management and prevention of high-burden diseases, the improvement of people’s ability to self-manage their health and wellbeing, the ability to alert healthcare professionals to changes in their condition and to support adherence to prescribed intervention. A variety of compact wearable sensors that are widely available today have allowed researchers and clinicians to pursue applications whereby individuals are monitored not only in clinical settings, but also in home and community settings with different applications.

We invite original research papers and review articles aimed at proposing wearable technology for healthcare, methods for sensor signal processing and new approaches to analyzing biomedical signals.

Dr. Paolo Capodaglio
Dr. Veronica Cimolin
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

  • wearable technology
  • inertial sensors
  • wearable sensors
  • healthcare
  • biomedical signals

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 721 KiB  
Article
Using Computer Vision to Annotate Video-Recoded Direct Observation of Physical Behavior
by Sarah K. Keadle, Skylar Eglowski, Katie Ylarregui, Scott J. Strath, Julian Martinez, Alex Dekhtyar and Vadim Kagan
Sensors 2024, 24(7), 2359; https://doi.org/10.3390/s24072359 - 8 Apr 2024
Viewed by 470
Abstract
Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults [...] Read more.
Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18–59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images. Full article
(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)
►▼ Show Figures

Figure 1

18 pages, 969 KiB  
Article
Wrist-Based Fall Detection: Towards Generalization across Datasets
by Vanilson Fula and Plinio Moreno
Sensors 2024, 24(5), 1679; https://doi.org/10.3390/s24051679 - 5 Mar 2024
Viewed by 808
Abstract
Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence [...] Read more.
Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors. Previous studies related to the creation of fall detection systems models use only one dataset that usually has a small number of samples. Training and testing machine learning models in this small scope: (i) yield overoptimistic classification rates, (ii) do not generalize to real-life situations and (iii) have very high rate of false positives. Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples. Our new dataset includes a standard way of adding samples, which allow the future addition of other data sources. We evaluate our dataset by using classic cost-sensitive Machine Leaning methods that deal with class imbalance. For the training and validation of this model, a set of temporal and frequency features were extracted from the raw data of an accelerometer and a gyroscope using a sliding window of 2 s with an overlap of 50%. We study the generalization properties of each dataset, by testing on the other datasets and also the performance of our new dataset. The model showed a good ability to distinguish between activities of daily living and falls, achieving a recall of 90.57%, a specificity of 96.91% and an Area Under the Receiver Operating Characteristic curve (AUC-ROC) value of 98.85% against the combination of three datasets. Full article
(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)
►▼ Show Figures

Figure 1

18 pages, 6240 KiB  
Article
Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis
by Youngmin Oh
Sensors 2024, 24(5), 1618; https://doi.org/10.3390/s24051618 - 1 Mar 2024
Viewed by 600
Abstract
Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this study, we investigate data augmentation and model training strategies to [...] Read more.
Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this study, we investigate data augmentation and model training strategies to address this problem. Three transformations are tested with varying data volumes to analyze the changes in the classification performance of individual data. Moreover, the impact of transfer learning relative to a pre-trained one-dimensional convolutional neural network (Conv1D) and training with an advanced InceptionTime model are estimated with data augmentation. In Conv1D, the joint training data of non-disabled (ND) participants and double rotationally augmented data of stroke patients is observed to outperform the baseline in terms of F1-score (60.9% vs. 47.3%). Transfer learning pre-trained with ND data exhibits 60.3% accuracy, whereas joint training with InceptionTime exhibits 67.2% accuracy under the same conditions. Our results indicate that rotational augmentation is more effective for individual data with initially lower performance and subset data with smaller numbers of participants than other techniques, suggesting that joint training on rotationally augmented ND and stroke data enhances classification performance, particularly in cases with sparse data and lower initial performance. Full article
(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)
►▼ Show Figures

Figure 1

16 pages, 3360 KiB  
Article
Assessment of Foot Strike Angle and Forward Propulsion with Wearable Sensors in People with Stroke
by Carmen J. Ensink, Cheriel Hofstad, Theo Theunissen and Noël L. W. Keijsers
Sensors 2024, 24(2), 710; https://doi.org/10.3390/s24020710 - 22 Jan 2024
Viewed by 788
Abstract
Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an [...] Read more.
Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an inertial measurement unit (IMU) to measure the foot strike angle (FSA), and explores eight different kinematic parameters as potential indicators for forward propulsion. Twelve people with stroke performed walking trials while equipped with five IMUs and markers for optical motion analysis (the gold standard). The validity of the IMU-based FSA was assessed via Bland–Altman analysis, ICC, and the repeatability coefficient. Eight different kinematic parameters were compared to the forward propulsion via Pearson correlation. Analyses were performed on a stride-by-stride level and within-subject level. On a stride-by-stride level, the mean difference between the IMU-based FSA and OMCS-based FSA was 1.4 (95% confidence: −3.0; 5.9) degrees, with ICC = 0.97, and a repeatability coefficient of 5.3 degrees. The mean difference for the within-subject analysis was 1.5 (95% confidence: −1.0; 3.9) degrees, with a mean repeatability coefficient of 3.1 (SD: 2.0) degrees. Pearson’s r value for all the studied parameters with forward propulsion were below 0.75 for the within-subject analysis, while on a stride-by-stride level the foot angle upon terminal contact and maximum foot angular velocity could be indicative for the peak forward propulsion. In conclusion, the FSA can accurately be assessed with an IMU on the foot in people with stroke during regular walking. However, no suitable kinematic indicator for forward propulsion was identified based on foot and shank movement that could be used for feedback in people with stroke. Full article
(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop