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Special Issue "Smart Sensors: Applications and Advances in Human Motion Analysis"

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

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Cristina P. Santos
Website
Guest Editor
Center for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, Portugal
Interests: human motion; human locomotion; human–robot interactions and collaboration; medical devices; neuro-rehabilitation of patients suffering from motor problems by means of bio-inspired robotics and neuroscience technologies
Special Issues and Collections in MDPI journals
Dr. Joana Figueiredo
Website
Guest Editor
Postdoctoral researcher, Center for MicroElectroMechanical Systems (CMEMS), University of Minho, Portugal
Interests: gait rehabilitation robotics; wearable motion sensors; gait analysis; human motion recognition

Special Issue Information

Dear Colleagues,

New directions in human motion cover motion recognition and prediction, and human-robot interaction perception using sensor-based technologies driven by recent technological advances (wearable sensors, advanced sensors, artificial intelligence and machine learning, electronic and smart sensing textiles, and so on). Advanced applications in both scenarios rely on the combination of smart sensors with the algorithmic advances in artificial intelligence.  

The adequate provision of human motion requires the consideration of various aspects, as follows. The use of unobtrusive, low-cost wearable sensors that are capable of tracking relevant motion in free-living conditions; machine learning-based strategies for reasoning sensor data; intuitive and collaborative sensor-based technologies for timely assisting and interacting with users in various environments. The accurate decision making of human motion may bring new achievements in diverse robotic applications. The inclusion of motion prediction strategies in robotic assistive devices is necessary to provide patients with personalized motor assistance and to prevent risk situations. Moreover, the collaborative robots, used in Industry 4.0 programs and social robots, will benefit if the robot is being continuously kept informed of the human motor performance and safety.  

This Special Issue covers new strategies to recognize and predict the human motion or the human-robot interaction, both in the clinical and in the industry fields, thanks to the application of smart sensors or the innovative use of the standard wearable sensors. Biofeedback strategies-related sensors to augment human collaboration with robotic systems are also encouraged.

Contributions may include, but are not limited to:

  • Smart sensors for human motion analysis;
  • Sensors for decision making and smart-based applications;
  • Wearable sensor-based strategies for motion intention recognition;
  • Machine learning algorithms for human motion recognition and prediction;
  • Machine learning -based sensor measurements for human motion estimation;
  • Sensors applications on collaborative and assistive robots;
  • Advanced strategies for improving human-robot interaction;
  • Sensing for physical human-robot interaction;
  • Applications of sensors for robotics

Dr. Cristina P. Santos
Dr. Joana Figueiredo
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.

Published Papers (4 papers)

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Research

Open AccessArticle
Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach
Sensors 2020, 20(10), 2939; https://doi.org/10.3390/s20102939 - 22 May 2020
Abstract
Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed [...] Read more.
Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes. Full article
(This article belongs to the Special Issue Smart Sensors: Applications and Advances in Human Motion Analysis)
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Open AccessArticle
Gait Characteristics under Imposed Challenge Speed Conditions in Patients with Parkinson’s Disease During Overground Walking
Sensors 2020, 20(7), 2132; https://doi.org/10.3390/s20072132 - 10 Apr 2020
Cited by 1
Abstract
Evaluating gait stability at slower or faster speeds and self-preferred speeds based on continuous steps may assist in determining the severity of motor symptoms in Parkinson’s disease (PD) patients. This study aimed to investigate the gait ability at imposed speed conditions in PD [...] Read more.
Evaluating gait stability at slower or faster speeds and self-preferred speeds based on continuous steps may assist in determining the severity of motor symptoms in Parkinson’s disease (PD) patients. This study aimed to investigate the gait ability at imposed speed conditions in PD patients during overground walking. Overall, 74 PD patients and 52 age-matched healthy controls were recruited. Levodopa was administered to patients in the PD group, and all participants completed imposed slower, preferred, and faster speed walking tests along a straight 15-m walkway wearing shoe-type inertial measurement units. Reliability of the slower and faster conditions between the estimated and measured speeds indicated excellent agreement for PD patients and controls. PD patients demonstrated higher gait asymmetry (GA) and coefficient of variance (CV) for stride length and stance phase than the controls at slower speeds and higher CVs for phases for single support, double support, and stance. CV of the double support phase could distinguish between PD patients and controls at faster speeds. The GA and CVs of stride length and phase-related variables were associated with motor symptoms in PD patients. Speed conditions should be considered during gait analysis. Gait variability could evaluate the severity of motor symptoms in PD patients. Full article
(This article belongs to the Special Issue Smart Sensors: Applications and Advances in Human Motion Analysis)
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Open AccessArticle
Gait Characteristics Based on Shoe-Type Inertial Measurement Units in Healthy Young Adults during Treadmill Walking
Sensors 2020, 20(7), 2095; https://doi.org/10.3390/s20072095 - 08 Apr 2020
Abstract
This study investigated the gait characteristics of healthy young adults using shoe-type inertial measurement units (IMU) during treadmill walking. A total of 1478 participants were tested. Principal component analyses (PCA) were conducted to determine which principal components (PCs) best defined the [...] Read more.
This study investigated the gait characteristics of healthy young adults using shoe-type inertial measurement units (IMU) during treadmill walking. A total of 1478 participants were tested. Principal component analyses (PCA) were conducted to determine which principal components (PCs) best defined the characteristics of healthy young adults. A non-hierarchical cluster analysis was conducted to evaluate the essential gait ability, according to the results of the PC1 score. One-way repeated analysis of variance with the Bonferroni correction was used to compare gait performances in the cluster groups. PCA outcomes indicated 76.9% variance for PC1–PC6, where PC1 (gait variability (GV): 18.5%), PC2 (pace: 17.8%), PC3 (rhythm and phase: 13.9%), and PC4 (bilateral coordination: 11.2%) were the gait-related factors. All of the pace, rhythm, GV, and variables for bilateral coordination classified the gait ability in the cluster groups. We suggest that the treadmill walking task may be reliable to evaluate the gait performances, which may provide insight into understanding the decline of gait ability. The presented results are considered meaningful for understanding the gait patterns of healthy adults and may prove useful as reference outcomes for future gait analyses. Full article
(This article belongs to the Special Issue Smart Sensors: Applications and Advances in Human Motion Analysis)
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Open AccessArticle
Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
Sensors 2020, 20(6), 1776; https://doi.org/10.3390/s20061776 - 23 Mar 2020
Abstract
Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of [...] Read more.
Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles. Full article
(This article belongs to the Special Issue Smart Sensors: Applications and Advances in Human Motion Analysis)
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