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Special Issue "Wearable Sensors for Movement Analysis"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Jeffrey M. Hausdorff
Website
Guest Editor
Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 62431, Israel
Interests: aging; gait; falls; freezing of gait; wearables; Parkinson’s disease; fractal physiology
Prof. Dr. Jaap van Dieën
Website
Guest Editor
Vrije Universiteit Amsterdam, van der Boechorststraat 9, 1081 BT Amsterdam, Netherlands
Interests: neuromechanics; posture; gait; movement disorders; musculoskeletal disorders
Dr. Martina Mancini
Website
Guest Editor
Department of Neurology, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098, USA
Interests: balance; turning; home monitoring; Parkinson's disease; closed-loop systems for gait rehabilitation; mobile imaging

Special Issue Information

Dear Colleagues,

In 1964, H Gage reported on the use of an accelerometer to quantify the “smoothness” of walking. Thirteen years later, GL Smidt and colleagues took that work a step further and linked a 3-D accelerometer and footswitches to a PDP-12 to quantitatively characterize gait over a 7-meter pathway in patients with tibial-femoral knee implants. We have come a long way since those early studies. The PDP-12 is now “ancient history”. Today, with smartphones and cloud computing, wearables are used to evaluate movement, gait, balance, and mobility, in conventional research settings, as well as in our streets, in our homes, and even on ski slopes over extended periods of time, changing the landscape of wearables and opening up new opportunities for research and clinical applications. Other types of sensors, such as thermal and electrophysiological sensors, have been miniaturized and made wearable to integrate additional information on human movement. Whether they are used to provide new insights into physiology and patho-physiology; to assess the impact of a new drug, therapy or training; or to generate real-time feedback and interventions, wearables are becoming an increasingly ubiquitous tool for the study and treatment of movement and mobility.

As the journal Sensors celebrates its 20th year, and as we mark what may be the sixth decade of wearables for the study of movement, we invite you to take part in this celebration by submitting manuscripts for a Special Issue devoted to ‘’Wearables for Movement Analysis’’. Papers from a wide variety of perspectives that will help to advance the field are invited. For more information or questions about the fit of a potential manuscript, please e-mail us.

Prof. Jeffrey M. Hausdorff
Prof. Dr. Jaap van Dieen
Dr. Martina Mancini
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 papers will be 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 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

  • accelerometers
  • inertial measurement units
  • gait
  • balance
  • mobility
  • postural transitions
  • aging
  • falls
  • validation
  • EMG
  • ECG
  • fNIRS
  • EEG

Published Papers (3 papers)

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Research

Open AccessArticle
Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test
Sensors 2020, 20(16), 4474; https://doi.org/10.3390/s20164474 - 10 Aug 2020
Abstract
Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson’s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop [...] Read more.
Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson’s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the “ground-truth” for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)
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Open AccessArticle
Validation of an IMU Suit for Military-Based Tasks
Sensors 2020, 20(15), 4280; https://doi.org/10.3390/s20154280 - 31 Jul 2020
Abstract
Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using [...] Read more.
Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)
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Open AccessArticle
A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors
Sensors 2020, 20(9), 2646; https://doi.org/10.3390/s20092646 - 06 May 2020
Abstract
An objective method to detect muscle fatigue-related kinematic changes may reduce workplace injuries. However, heterogeneous responses to muscle fatigue suggest that subject-specific analyses are necessary. The objectives of this study were to: (1) determine if wearable inertial measurement units (IMUs) could be used [...] Read more.
An objective method to detect muscle fatigue-related kinematic changes may reduce workplace injuries. However, heterogeneous responses to muscle fatigue suggest that subject-specific analyses are necessary. The objectives of this study were to: (1) determine if wearable inertial measurement units (IMUs) could be used in conjunction with a spine motion composite index (SMCI) to quantify subject-specific changes in spine kinematics during a repetitive spine flexion-extension (FE) task; and (2) determine if the SMCI was correlated with measures of global trunk muscle fatigue. Spine kinematics were measured using wearable IMUs in 10 healthy adults during a baseline set followed by 10 sets of 50 spine FE repetitions. After each set, two fatigue measures were collected: perceived level of fatigue using a visual analogue scale (VAS), and maximal lift strength. SMCIs incorporating 10 kinematic variables from 2 IMUs (pelvis and T8 vertebrae) were calculated and used to quantify subject-specific changes in movement. A main effect of set was observed (F (1.7, 15.32) = 10.42, p = 0.002), where the SMCI became significantly greater than set 1 starting at set 4. Significant correlations were observed between the SMCI and both fatigue VAS and maximal lift strength at the individual and study level. These findings support the use of wearable IMUs to detect subject-specific changes in spine motion associated with muscle fatigue. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

AuthorSilvia Del-Din; Rana Rehman and Lynn Rochester

Potential Title and Abstract

“Validation and comparison of novel lightweight template-based and machine learning algorithms for free-living gait detection.”

Background and Objectives:

Wearable-based free-living walking assessment methodologies require a valid and robust means of gait classification (GC) before further detailed gait quantification can take place [1]. A suitable GC algorithm is one that can perform in as wide a variety of contexts as possible and accurately capture valid gait. Such versatility is vital in free-living conditions. This study aimed at validating a template-based method for free-living gait detection.

Methods:

10 healthy adult subjects wore an accelerometer (AX3, Axivity) on the lower back and a synchronised GoPro camera on the trunk for two separate one-hour epochs [2]. Videos were annotated and represented the gold standard for validation and performance quantification (sensitivity and specificity) of a lightweight versatile gait detection algorithm in various contexts. The algorithm uses the convolution of the vertical signal and a sinusoidal template with a frequency within the typical ambulation range (~2.5Hz). Applying a threshold to the resultant signal produced a binary signal for GC, Fig 1a. GC Performance indicators, ROC curves and associated AUC values were calculated as well as Spearman’s correlation (rho) for number of bouts.

Results:

The novel algorithm had good performance (accuracy 87%, sensitivity 72%, specificity 84%, AUC=0.78) and rho=0.8 (p<0.01) with slight positive bias. We contextualised GC performance: short indoors bouts were associated with lower sensitivity whereas shuffling, turning and other activities e.g. cycling were more often associated with lower specificity.

Conclusions:

We validated a novel lightweight algorithm for free-living gait detection, the algorithm showed to perform very well in many contexts. Algorithm’s areas of improvement are related to activities in which periodicity is exhibited (e.g. cycling).


References:

[1]        Taborri.J et al. Gait partitioning methods: A systematic review. Sensors.2016:16(1):66

[2]        Hickey.A et al. Detecting free-living steps and walking bouts: validating an algorithm for             macro gait analysis. Physiological measurement. 2016:12;38(1)

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