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21 pages, 8240 KiB

Open AccessArticle

Optimizing Human–Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis

by Soroush Korivand, Gustavo Galvani, Arash Ajoudani, Jiaqi Gong and Nader Jalili

Sensors 2024, 24(9), 2817; https://doi.org/10.3390/s24092817 - 28 Apr 2024

Viewed by 260

Abstract

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, [...] Read more.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot’s speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration. Full article

(This article belongs to the Special Issue Sensors in 2024)

10 pages, 2452 KiB

Open AccessArticle

Investigation of the Efficacy of a Listeria monocytogenes Biosensor Using Chicken Broth Samples

by Or Zolti, Baviththira Suganthan, Sanket Naresh Nagdeve, Ryan Maynard, Jason Locklin and Ramaraja P. Ramasamy

Sensors 2024, 24(8), 2617; https://doi.org/10.3390/s24082617 - 19 Apr 2024

Viewed by 637

Abstract

Foodborne pathogens are microbes present in food that cause serious illness when the contaminated food is consumed. Among these pathogens, Listeria monocytogenes is one of the most serious bacterial pathogens, and causes severe illness. The techniques currently used for L. monocytogenes detection are [...] Read more.

Foodborne pathogens are microbes present in food that cause serious illness when the contaminated food is consumed. Among these pathogens, Listeria monocytogenes is one of the most serious bacterial pathogens, and causes severe illness. The techniques currently used for L. monocytogenes detection are based on common molecular biology tools that are not easy to implement for field use in food production and distribution facilities. This work focuses on the efficacy of an electrochemical biosensor in detecting L. monocytogenes in chicken broth. The sensor is based on a nanostructured electrode modified with a bacteriophage as a bioreceptor which selectively detects L. monocytogenes using electrochemical impedance spectroscopy. The biosensing platform was able to reach a limit of detection of 55 CFU/mL in 1× PBS buffer and 10 CFU/mL in 1% diluted chicken broth. The biosensor demonstrated 83–98% recovery rates in buffer and 87–96% in chicken broth. Full article

(This article belongs to the Special Issue Sensors in 2024)

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23 pages, 5234 KiB

Open AccessArticle

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

by Charbel Geryes Aoun, Noura Mansour, Fadi Dornaika and Loic Lagadec

Sensors 2024, 24(8), 2433; https://doi.org/10.3390/s24082433 - 10 Apr 2024

Viewed by 449

Abstract

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices’ hardware and software when combined with the Internet. Hence, USSNs are regarded as [...] Read more.

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices’ hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems’ development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs. Full article

(This article belongs to the Special Issue Sensors in 2024)

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17 pages, 6092 KiB

Open AccessArticle

Laser Triangulation Sensors Performance in Scanning Different Materials and Finishes

by Victor Meana, Pablo Zapico, Eduardo Cuesta, Sara Giganto and Susana Martinez-Pellitero

Sensors 2024, 24(8), 2410; https://doi.org/10.3390/s24082410 - 10 Apr 2024

Viewed by 369

Abstract

The variety of equipment implementing laser triangulation technology for 3D scanning makes it difficult to analyse their performance, comparability, and traceability. In this study, three laser triangulation sensors arranged in different configurations are analysed using high precision spheres made of different materials and [...] Read more.

The variety of equipment implementing laser triangulation technology for 3D scanning makes it difficult to analyse their performance, comparability, and traceability. In this study, three laser triangulation sensors arranged in different configurations are analysed using high precision spheres made of different materials and surface finishes. Three types of reference parameters were used: diameter, form error, and standard deviation of the point cloud. The experimentation was based on studying the quality of the point clouds generated by the three sensors, which enabled us to find and quantify an edge effect in the horizon of the scanned surface. A procedure to reach the optimal filtering conditions was proposed, and a chart of recommended usage of each sphere (material and finish) was created for the different types of sensors. This filter enables removal of both spurious points and those few points that spoil the form error, greatly improving the quality of the measurement. Full article

(This article belongs to the Special Issue Sensors in 2024)

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15 pages, 9297 KiB

Open AccessArticle

A High-Finesse Suspended Interferometric Sensor for Macroscopic Quantum Mechanics with Femtometre Sensitivity

by Jiri Smetana, Tianliang Yan, Vincent Boyer and Denis Martynov

Sensors 2024, 24(7), 2375; https://doi.org/10.3390/s24072375 - 08 Apr 2024

Viewed by 413

Abstract

We present an interferometric sensor for investigating macroscopic quantum mechanics on a table-top scale. The sensor consists of a pair of suspended optical cavities with finesse over 350,000 comprising 10 g fused silica mirrors. The interferometer is suspended by a four-stage, light, in-vacuum [...] Read more.

We present an interferometric sensor for investigating macroscopic quantum mechanics on a table-top scale. The sensor consists of a pair of suspended optical cavities with finesse over 350,000 comprising 10 g fused silica mirrors. The interferometer is suspended by a four-stage, light, in-vacuum suspension with three common stages, which allows for us to suppress common-mode motion at low frequency. The seismic noise is further suppressed by an active isolation scheme, which reduces the input motion to the suspension point by up to an order of magnitude starting from 0.7 Hz. In the current room-temperature operation, we achieve a peak sensitivity of 0.5 fm/

\sqrt{Hz}

in the acoustic frequency band, limited by a combination of readout noise and suspension thermal noise. Additional improvements of the readout electronics and suspension parameters will enable us to reach the quantum radiation pressure noise. Such a sensor can eventually be utilized for demonstrating macroscopic entanglement and for testing semi-classical and quantum gravity models. Full article

(This article belongs to the Special Issue Sensors in 2024)

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15 pages, 5960 KiB

Open AccessArticle

A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series

by Juntao Wu, M. Hesham El Naggar and Kuihua Wang

Sensors 2024, 24(4), 1190; https://doi.org/10.3390/s24041190 - 11 Feb 2024

Viewed by 828

Abstract

Machine learning (ML) algorithms are increasingly applied to structure health monitoring (SHM) problems. However, their application to pile damage detection (PDD) is hindered by the complexity of the problem. A novel multi-sensor pile damage detection (MSPDD) method is proposed in this paper to [...] Read more.

Machine learning (ML) algorithms are increasingly applied to structure health monitoring (SHM) problems. However, their application to pile damage detection (PDD) is hindered by the complexity of the problem. A novel multi-sensor pile damage detection (MSPDD) method is proposed in this paper to extend the application of ML algorithms in the automatic identification of PDD. The time-series signals collected by multiple sensors during the pile integrity test are first processed by the traveling wave decomposition (TWD) theory and are then input into a hybrid one-dimensional (1D) convolutional and recurrent neural network. The hybrid neural network can achieve the automatic multi-task identification of pile damage detection based on the time series of MSPDD results. Finally, the analytical solution-based sample set is utilized to evaluate the performance of the proposed hybrid model. The outputs of the multi-task learning framework can provide a detailed description of the actual pile quality and provide strong support for the classification of pile quality as well. Full article

(This article belongs to the Special Issue Sensors in 2024)

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13 pages, 5053 KiB

Open AccessArticle

Projection-Angle-Sensor-Assisted X-ray Computed Tomography for Cylindrical Lithium-Ion Batteries

by Jiawei Dong, Lingling Ju, Quanyuan Jiang and Guangchao Geng

Sensors 2024, 24(4), 1102; https://doi.org/10.3390/s24041102 - 08 Feb 2024

Viewed by 843

Abstract

X-ray computed tomography (XCT) has become a powerful technique for studying lithium-ion batteries, allowing non-destructive 3D imaging across multiple spatial scales. Image quality is particularly important for observing the internal structure of lithium-ion batteries. During multiple rotations, the existence of cumulative errors and [...] Read more.

X-ray computed tomography (XCT) has become a powerful technique for studying lithium-ion batteries, allowing non-destructive 3D imaging across multiple spatial scales. Image quality is particularly important for observing the internal structure of lithium-ion batteries. During multiple rotations, the existence of cumulative errors and random errors in the rotary table leads to errors in the projection angle, affecting the imaging quality of XCT. The accuracy of the projection angle is an important factor that directly affects imaging. However, the impact of the projection angle on XCT reconstruction imaging is difficult to quantify. Therefore, the required precision of the projection angle sensor cannot be determined explicitly. In this research, we selected a common 18650 cylindrical lithium-ion battery for experiments. By setting up an XCT scanning platform and installing an angle sensor to calibrate the projection angle, we proceeded with image reconstruction after introducing various angle errors. When comparing the results, we found that projection angle errors lead to the appearance of noise and many stripe artifacts in the image. This is particularly noticeable in the form of many irregular artifacts in the image background. The overall variation and residual projection error in detection indicators can effectively reflect the trend in image quality. This research analyzed the impact of projection angle errors on imaging and improved the quality of XCT imaging by installing angle sensors on a rotary table. Full article

(This article belongs to the Special Issue Sensors in 2024)

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11 pages, 2284 KiB

Open AccessArticle

Real-Time Measurement of CH₄ in Human Breath Using a Compact CH₄/CO₂ Sensor

by Yueyu Lin, Dexter Manalili, Amir Khodabakhsh and Simona M. Cristescu

Sensors 2024, 24(4), 1077; https://doi.org/10.3390/s24041077 - 07 Feb 2024

Viewed by 750

Abstract

The presence of an elevated amount of methane (CH₄) in exhaled breath can be used as a non-invasive tool to monitor certain health conditions. A compact, inexpensive and transportable CH₄ sensor is thus very interesting for this purpose. In addition, [...] Read more.

The presence of an elevated amount of methane (CH₄) in exhaled breath can be used as a non-invasive tool to monitor certain health conditions. A compact, inexpensive and transportable CH₄ sensor is thus very interesting for this purpose. In addition, if the sensor is also able to simultaneously measure carbon dioxide (CO₂), one can extract the end-tidal concentration of exhaled CH₄. Here, we report on such a sensor based on a commercial detection module using tunable diode laser absorption spectroscopy. It was found that the measured CH₄/CO₂ values exhibit a strong interference with water vapor. Therefore, correction functions were experimentally identified and validated for both CO₂ and CH₄. A custom-built breath sampler was developed and tested with the sensor for real-time measurements of CH₄ and CO₂ in exhaled breath. As a result, the breath sensor demonstrated the capability of accurately measuring the exhaled CH₄ and CO₂ profiles in real-time. We obtained minimum detection limits of ~80 ppbv for CH₄ and ~700 ppmv for CO₂ in 1.5 s measurement time. Full article

(This article belongs to the Special Issue Sensors in 2024)

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13 pages, 4783 KiB

Open AccessArticle

Relationship between Car-Sickness Susceptibility and Postural Activity: Could the Re-Weighting Strategy between Signals from Different Body Sensors Be an Underlying Factor?

by Merrick Dida, Michel Guerraz, Pierre-Alain Barraud and Corinne Cian

Sensors 2024, 24(4), 1046; https://doi.org/10.3390/s24041046 - 06 Feb 2024

Viewed by 799

Abstract

Postural control characteristics have been proposed as a predictor of Motion Sickness (MS). However, postural adaptation to sensory environment changes may also be critical for MS susceptibility. In order to address this issue, a postural paradigm was used where accurate orientation information from [...] Read more.

Postural control characteristics have been proposed as a predictor of Motion Sickness (MS). However, postural adaptation to sensory environment changes may also be critical for MS susceptibility. In order to address this issue, a postural paradigm was used where accurate orientation information from body sensors could be lost and restored, allowing us to infer sensory re-weighting dynamics from postural oscillation spectra in relation to car-sickness susceptibility. Seventy-one participants were standing on a platform (eyes closed) alternating from static phases (proprioceptive and vestibular sensors providing reliable orientation cues) to sway referenced to the ankle-angle phases (proprioceptive sensors providing unreliable orientation cues). The power spectrum density (PSD) on a 10 s sliding window was computed from the antero-posterior displacement of the center of pressure. Energy ratios (ERs) between the high (0.7–1.3 Hz) and low (0.1–0.7 Hz) frequency bands of these PSDs were computed on key time windows. Results showed no difference between MS and non-MS participants following loss of relevant ankle proprioception. However, the reintroduction of reliable ankle signals led, for the non-MS participants, to an increase of the ER originating from a previously up-weighted vestibular information during the sway-referenced situation. This suggests inter-individual differences in re-weighting dynamics in relation to car-sickness susceptibility. Full article

(This article belongs to the Special Issue Sensors in 2024)

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