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Soft Sensors for Motion Capture and Analysis
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Soft Sensors for Motion Capture and Analysis

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

Deadline for manuscript submissions: closed (19 December 2019) | Viewed by 8996

Special Issue Editors

Prof. Dr. Alessandro Tognetti

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Guest Editor
Information Engineering Department, University of Pisa and Research Center "E. Piaggio” Largo L. Lazzarino 1, 56122 Pisa, Italy
Interests: biomedical engineering; sensing technologies; soft sensors; motion capture; data fusion; biomechanics; rehabilitation; wearable sensors and technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Nicola Carbonaro

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Guest Editor
Information Engineering Department and Research Center "E. Piaggio”, University of Pisa, 56123 Pisa, Italy
Interests: hardware and software development for wearable sensing technology for physiological and behavioral human monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current sensing devices for human motion capture (e.g., inertial devices, goniometers, etc.) are based on solid-state components and are not suitable for adapting to the deformable nature of the human body. Conversely, soft sensors are unobtrusive, compliant, and lightweight devices that have the potential to enable innovative mobile health applications. Examples of such technologies are e-textiles and flexible/stretchable devices (capacitive, piezoresistive, and piezoelectric) that have been employed for on-body, unobtrusive, and ambulatory motion capture and analysis. These technologies have the potential to enable daily life monitoring systems and wearable devices for a wide range of applications.

The purpose of this Special Issue is to bring together researchers in the field of soft sensors for human motion capture and analysis, to share their ideas and conceptual approaches, and to examine the recent advances in this field in depth, addressing innovating solutions, paradigms, and emerging issues.

Dr. Alessandro Tognetti
Dr. Nicola Carbonaro
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

  • Flexible/stretchable sensors for human motion capture
  • E-textiles solutions for human motion capture
  • Soft solution for gait analysis
  • Flexible/stretchable EMG bioelectrodes
  • New materials and technologies
  • Ambulatory human motion/activity monitoring
  • Data processing and data fusion
  • Integration of soft and conventional technologies
  • Processing of soft sensor data

Published Papers (2 papers)

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25 pages, 17741 KiB  
Article
Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture
by Haitao Guo and Yunsick Sung
Sensors 2020, 20(6), 1801; https://doi.org/10.3390/s20061801 - 24 Mar 2020
Cited by 18 | Viewed by 3286
Abstract
The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, [...] Read more.
The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM. Full article
(This article belongs to the Special Issue Soft Sensors for Motion Capture and Analysis)
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25 pages, 3913 KiB  
Article
Application-Based Production and Testing of a Core–Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles
by Ahmad Rezaei, Tyler J. Cuthbert, Mohsen Gholami and Carlo Menon
Sensors 2019, 19(19), 4288; https://doi.org/10.3390/s19194288 - 03 Oct 2019
Cited by 23 | Viewed by 4962
Abstract
Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core–sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the [...] Read more.
Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core–sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the development of a smart sleeveless shirt for measuring the kinematic angles of the trunk relative to the pelvis in complicated three-dimensional movements. The sensor’s piezoresistive properties and characteristics were studied with respect to the type of core material used. Sensor performance was optimized by straining above the intended working region to increase the consistency and accuracy of the piezoresistive sensor. The accuracy of the sensor when tracking random movements was tested using a rigorous 4-h random wave pattern to mimic what would be required for satisfactory use in prototype devices. By processing the raw signal with a machine learning algorithm, we were able to track a strain of random wave patterns to a normalized root mean square error of 1.6%, highlighting the consistency and reproducible behavior of the relatively simple sensor. Then, we evaluated the performance of these sensors in a prototype motion capture shirt, in a study with 12 participants performing a set of eight different types of uniaxial and multiaxial movements. A machine learning random forest regressor model estimated the trunk flexion, lateral bending, and rotation angles with errors of 4.26°, 3.53°, and 3.44° respectively. These results demonstrate the feasibility of using smart textiles for capturing complicated movements and a solution for the real-time monitoring of daily activities. Full article
(This article belongs to the Special Issue Soft Sensors for Motion Capture and Analysis)
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