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Human Performance Sensing and Human-Structure Interactions
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Human Performance Sensing and Human-Structure Interactions

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

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 3645

Special Issue Editors

Prof. Dr. Kenneth Loh

E-Mail Website
Guest Editor
1. Department of Structural Engineering, University of California, San Diego, CA, USA
2. Active, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Jacobs School of Engineering, The University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
Interests: stimuli-responsive materials; nanocomposites; sensors and actuators; soft materials; tomography
Special Issues, Collections and Topics in MDPI journals
Dr. Haeyoung Noh

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
Interests: structures as sensors; smart building; vibration; structural health monitoring; data mining
Dr. Fernando Moreu

E-Mail Website
Guest Editor
Department Civil, Construction, and Environmental Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Interests: structural health monitoring; wireless smart sensor networks; infrastructure management and policies; performance-based monitoring; augmented reality; human–machine interfaces and human cognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The optimal performance of complex systems not only requires both the human operator and structure to function at peak performance, but it also depends on how the two can effectively work together. Therefore, monitoring the human operator and how they interact with and control artificial structures is crucial for optimizing system performance and functionality while ensuring safety. Failure to consider the human operator and structure as an integrated system—and the failure of any one of these—can result in mission failure or poor/sub-optimal performance. This Special Issue of Sensors is soliciting contributions focused on human performance sensing and health monitoring, as well as sensing the interactions/interfaces between humans and artificial structural systems. Examples of specific topics of interest include: (1) wearable Internet-of-Things (IoT) technologies and feedback mechanisms; (2) bio-marker, biochemical, and bio-molecular sensing; (3) understanding and modeling of structural responses induced by humans or animals; (4) monitoring human–structure interfaces that enhance system performance; (5) novel augmented/virtual reality and sensing data-visualization methods; (6) human-centric structural management methodologies; and (7) laboratory and field validation studies on human performance assessment and human–structure interactions.

Prof. Dr. Kenneth Loh
Dr. Haeyoung Noh
Dr. Fernando Moreu
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

  • actuators
  • behavior
  • data visualization
  • digital health
  • human in the loop
  • Internet-of-Things
  • prehabilitation
  • rehabilitation
  • sensors
  • structural performance
  • wearable sensors

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Published Papers (2 papers)

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Research

24 pages, 11552 KiB  
Article
Experimental Evaluation of Pedestrian-Induced Multiaxial Gait Loads on Footbridges: Effects of the Structure-to-Human Interaction by Lateral Vibrating Platforms
by Bryan Castillo, Johannio Marulanda and Peter Thomson
Sensors 2024, 24(8), 2517; https://doi.org/10.3390/s24082517 - 14 Apr 2024
Cited by 3 | Viewed by 1317
Abstract
The introduction of resistant and lightweight materials in the construction industry has led to civil structures being vulnerable to excessive vibrations, particularly in footbridges exposed to human-induced gait loads. This interaction, known as Human–Structure Interaction (HSI), involves a complex interplay between structural vibrations [...] Read more.
The introduction of resistant and lightweight materials in the construction industry has led to civil structures being vulnerable to excessive vibrations, particularly in footbridges exposed to human-induced gait loads. This interaction, known as Human–Structure Interaction (HSI), involves a complex interplay between structural vibrations and gait loads. Despite extensive research on HSI, the simultaneous effects of lateral structural vibrations with fundamental frequencies close to human gait frequency (around 1.0 Hz) and wide amplitudes (over 30.0 mm) remain inadequately understood, posing a contemporary structural challenge highlighted by incidents in iconic bridges like the Millennium Bridge in London, Solferino Bridge in Paris, and Premier Bridge in Cali, Colombia. This paper focuses on the experimental exploration of Structure-to-Human Interaction (S2HI) effects using the Human–Structure Interaction Multi-Axial Test Framework (HSI-MTF). The framework enables the simultaneous measurement of vertical and lateral loads induced by human gait on surfaces with diverse frequency ranges and wide-amplitude lateral harmonic motions. The study involved seven test subjects, evaluating gait loads on rigid and harmonic lateral surfaces with displacements ranging from 5.0 to 50.0 mm and frequency content from 0.70 to 1.30 Hz. A low-cost vision-based motion capture system with smartphones analyzed the support (Tsu) and swing (Tsw) periods of human gait. Results indicated substantial differences in Tsu and Tsw on lateral harmonic protocols, reaching up to 96.53% and 58.15%, respectively, compared to rigid surfaces. Normalized lateral loads (LL) relative to the subject’s weight (W0) exhibited a linear growth proportional to lateral excitation frequency, with increased proportionality constants linked to higher vibration amplitudes. Linear regressions yielded an average R2 of 0.815. Regarding normalized vertical load (LV) with respect to W0, a consistent behavior was observed for amplitudes up to 30.0 mm, beyond which a linear increase, directly proportional to frequency, resulted in a 28.3% increment compared to rigid surfaces. Correlation analyses using Pearson linear coefficients determined relationships between structural surface vibration and pedestrian lateral motion, providing valuable insights into Structure-to-Human Interaction dynamics. Full article
(This article belongs to the Special Issue Human Performance Sensing and Human-Structure Interactions)
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26 pages, 8912 KiB  
Article
Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations
by Yiwen Dong and Hae Young Noh
Sensors 2024, 24(8), 2496; https://doi.org/10.3390/s24082496 - 13 Apr 2024
Cited by 2 | Viewed by 1371
Abstract
Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health [...] Read more.
Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left–right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively. Full article
(This article belongs to the Special Issue Human Performance Sensing and Human-Structure Interactions)
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