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15 pages, 2566 KiB

Open AccessArticle

A Low-Cost Inertial Measurement Unit Motion Capture System for Operation Posture Collection and Recognition

by Mingyue Yin, Jianguang Li and Tiancong Wang

Sensors 2024, 24(2), 686; https://doi.org/10.3390/s24020686 - 21 Jan 2024

Viewed by 1020

Abstract

In factories, human posture recognition facilitates human–machine collaboration, human risk management, and workflow improvement. Compared to optical sensors, inertial sensors have the advantages of portability and resistance to obstruction, making them suitable for factories. However, existing product-level inertial sensing solutions are generally expensive. [...] Read more.

In factories, human posture recognition facilitates human–machine collaboration, human risk management, and workflow improvement. Compared to optical sensors, inertial sensors have the advantages of portability and resistance to obstruction, making them suitable for factories. However, existing product-level inertial sensing solutions are generally expensive. This paper proposes a low-cost human motion capture system based on BMI 160, a type of six-axis inertial measurement unit (IMU). Based on WIFI communication, the collected data are processed to obtain the displacement of human joints’ rotation angles around XYZ directions and the displacement in XYZ directions, then the human skeleton hierarchical relationship was combined to calculate the real-time human posture. Furthermore, the digital human model was been established on Unity3D to synchronously visualize and present human movements. We simulated assembly operations in a virtual reality environment for human posture data collection and posture recognition experiments. Six inertial sensors were placed on the chest, waist, knee joints, and ankle joints of both legs. There were 16,067 labeled samples obtained for posture recognition model training, and the accumulated displacement and the rotation angle of six joints in the three directions were used as input features. The bi-directional long short-term memory (BiLSTM) model was used to identify seven common operation postures: standing, slightly bending, deep bending, half-squatting, squatting, sitting, and supine, with an average accuracy of 98.24%. According to the experiment result, the proposed method could be used to develop a low-cost and effective solution to human posture recognition for factory operation. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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15 pages, 2422 KiB

Open AccessArticle

Detection of SO₂F₂ Using a Photoacoustic Two-Chamber Approach

by Hassan Yassine, Christian Weber, Andre Eberhardt, Mahmoud El-Safoury, Jürgen Wöllenstein and Katrin Schmitt

Sensors 2024, 24(1), 191; https://doi.org/10.3390/s24010191 - 28 Dec 2023

Viewed by 670

Abstract

The wide use of sulfuryl difluoride (SO₂F₂) for termite control in buildings, warehouses and shipping containers requires the implementation of suitable sensors for reliable detection. SO₂F₂ is highly toxic to humans and the environment, and moreover, [...] Read more.

The wide use of sulfuryl difluoride (SO₂F₂) for termite control in buildings, warehouses and shipping containers requires the implementation of suitable sensors for reliable detection. SO₂F₂ is highly toxic to humans and the environment, and moreover, it is a potent greenhouse gas. We developed two photoacoustic two-chamber sensors with the aim to detect two different concentration ranges, 0–1 vol.-% SO₂F₂ and 0–100 ppm SO₂F₂, so that different applications can be targeted: the sensor for high concentrations for the effective treatment of buildings, containers, etc., and the sensor for low concentrations as personal safety device. Photoacoustic detectors were designed, fabricated, and then filled with either pure SO₂F₂ or pure substituent gas, the refrigerant R227ea, to detect SO₂F₂. Absorption cells with optical path lengths of 50 mm and 1.6 m were built for both concentration ranges. The sensitivity to SO₂F₂ as well as cross-sensitivities to CO₂ and H₂O were measured. The results show that concentrations below 1 ppm SO₂F₂ can be reliably detected, and possible cross-sensitivities can be effectively compensated. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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24 pages, 17507 KiB

Open AccessArticle

A Vibration Sensing Device Using a Six-Axis IMU and an Optimized Beam Structure for Activity Monitoring

by Pieter Try and Marion Gebhard

Sensors 2023, 23(19), 8045; https://doi.org/10.3390/s23198045 - 23 Sep 2023

Cited by 2 | Viewed by 1452

Abstract

Activity monitoring of living creatures based on the structural vibration of ambient objects is a promising method. For vibration measurement, multi-axial inertial measurement units (IMUs) offer a high sampling rate and a small size compared to geophones, but have higher intrinsic noise. This [...] Read more.

Activity monitoring of living creatures based on the structural vibration of ambient objects is a promising method. For vibration measurement, multi-axial inertial measurement units (IMUs) offer a high sampling rate and a small size compared to geophones, but have higher intrinsic noise. This work proposes a sensing device that combines a single six-axis IMU with a beam structure to enable measurement of small vibrations. The beam structure is integrated into the PCB of the sensing device and connects the IMU to the ambient object. The beam is designed with finite element method (FEM) and optimized to maximize the vibration amplitude. Furthermore, the beam oscillation creates simultaneous translation and rotation of the IMU, which is measured with its accelerometers and gyroscopes. On this basis, a novel sensor fusion algorithm is presented that adaptively combines IMU data in the wavelet domain to reduce intrinsic sensor noise. In experimental evaluation, the proposed sensing device using a beam structure achieves a 6.2-times-higher vibration amplitude and an increase in signal energy of 480% when compared to a directly mounted IMU without a beam. The sensor fusion algorithm provides a noise reduction of 5.6% by fusing accelerometer and gyroscope data at 103 Hz. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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15 pages, 6281 KiB

Open AccessArticle

Precision Heart Rate Estimation Using a PPG Sensor Patch Equipped with New Algorithms of Pre-Quality Checking and Hankel Decomposition

by Smriti Thakur, Paul C.-P. Chao and Cheng-Han Tsai

Sensors 2023, 23(13), 6180; https://doi.org/10.3390/s23136180 - 05 Jul 2023

Cited by 1 | Viewed by 2944

Abstract

A new method for accurately estimating heart rates based on a single photoplethysmography (PPG) signal and accelerations is proposed in this study, considering motion artifacts due to subjects’ hand motions and walking. The method comprises two sub-algorithms: pre-quality checking and motion artifact removal [...] Read more.

A new method for accurately estimating heart rates based on a single photoplethysmography (PPG) signal and accelerations is proposed in this study, considering motion artifacts due to subjects’ hand motions and walking. The method comprises two sub-algorithms: pre-quality checking and motion artifact removal (MAR) via Hankel decomposition. PPGs and accelerations were collected using a wearable device equipped with a PPG sensor patch and a 3-axis accelerometer. The motion artifacts caused by hand movements and walking were effectively mitigated by the two aforementioned sub-algorithms. The first sub-algorithm utilized a new quality-assessment criterion to identify highly noise-contaminated PPG signals and exclude them from subsequent processing. The second sub-algorithm employed the Hankel matrix and singular value decomposition (SVD) to effectively identify, decompose, and remove motion artifacts. Experimental data collected during hand-moving and walking were considered for evaluation. The performance of the proposed algorithms was assessed using the datasets from the IEEE Signal Processing Cup 2015. The obtained results demonstrated an average error of merely 0.7345 ± 8.1129 beats per minute (bpm) and a mean absolute error of 1.86 bpm for walking, making it the second most accurate method to date that employs a single PPG and a 3-axis accelerometer. The proposed method also achieved the best accuracy of 3.78 bpm in mean absolute errors among all previously reported studies for hand-moving scenarios. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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18 pages, 3647 KiB

Open AccessArticle

An Optical Sensory System for Assessment of Residual Cancer Burden in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

by Shadi Momtahen, Maryam Momtahen, Ramani Ramaseshan and Farid Golnaraghi

Sensors 2023, 23(12), 5761; https://doi.org/10.3390/s23125761 - 20 Jun 2023

Cited by 3 | Viewed by 1255

Abstract

Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) is a prognostic tool widely used to estimate survival outcomes in breast cancer. In this study, we introduced a machine-learning-based optical biosensor called the [...] Read more.

Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) is a prognostic tool widely used to estimate survival outcomes in breast cancer. In this study, we introduced a machine-learning-based optical biosensor called the Opti-scan probe to assess residual cancer burden in breast cancer patients undergoing NAC. The Opti-scan probe data were acquired from 15 patients (mean age: 61.8 years) before and after each cycle of NAC. Using regression analysis with k-fold cross-validation, we calculated the optical properties of healthy and unhealthy breast tissues. The ML predictive model was trained on the optical parameter values and breast cancer imaging features obtained from the Opti-scan probe data to calculate RCB values. The results show that the ML model achieved a high accuracy of 0.98 in predicting RCB number/class based on the changes in optical properties measured by the Opti-scan probe. These findings suggest that our ML-based Opti-scan probe has considerable potential as a valuable tool for the assessment of breast cancer response after NAC and to guide treatment decisions. Therefore, it could be a promising, non-invasive, and accurate method for monitoring breast cancer patient’s response to NAC. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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21 pages, 3259 KiB

Open AccessArticle

An Evidence Theoretic Approach for Traffic Signal Intrusion Detection

by Abdullahi Chowdhury, Gour Karmakar, Joarder Kamruzzaman, Rajkumar Das and S. H. Shah Newaz

Sensors 2023, 23(10), 4646; https://doi.org/10.3390/s23104646 - 10 May 2023

Cited by 1 | Viewed by 1488

Abstract

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches [...] Read more.

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon’s entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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12 pages, 1124 KiB

Open AccessArticle

An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring

by David Arney, Yi Zhang, Lauren R. Kennedy-Metz, Roger D. Dias, Julian M. Goldman and Marco A. Zenati

Sensors 2023, 23(8), 3890; https://doi.org/10.3390/s23083890 - 11 Apr 2023

Cited by 3 | Viewed by 1869

Abstract

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness [...] Read more.

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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14 pages, 3537 KiB

Open AccessArticle

Near-Infrared Spectroscopy for the In Vivo Monitoring of Biodegradable Implants in Rats

by Hafiz Wajahat Hassan, Eduarda Mota-Silva, Valeria Grasso, Leon Riehakainen, Jithin Jose, Luca Menichetti and Peyman Mirtaheri

Sensors 2023, 23(4), 2297; https://doi.org/10.3390/s23042297 - 18 Feb 2023

Cited by 1 | Viewed by 1845

Abstract

Magnesium (Mg) alloys possess unique properties that make them ideal for use as biodegradable implants in clinical applications. However, reports on the in vivo assessment of these alloys are insufficient. Thus, monitoring the degradation of Mg and its alloys in vivo is challenging [...] Read more.

Magnesium (Mg) alloys possess unique properties that make them ideal for use as biodegradable implants in clinical applications. However, reports on the in vivo assessment of these alloys are insufficient. Thus, monitoring the degradation of Mg and its alloys in vivo is challenging due to the dynamic process of implant degradation and tissue regeneration. Most current works focus on structural remodeling, but functional assessment is crucial in providing information about physiological changes in tissues, which can be used as an early indicator of healing. Here, we report continuous wave near-infrared spectroscopy (CW NIRS), a non-invasive technique that is potentially helpful in assessing the implant–tissue dynamic interface in a rodent model. The purpose of this study was to investigate the effects on hemoglobin changes and tissue oxygen saturation (StO₂) after the implantation of Mg-alloy (WE43) and titanium (Ti) implants in rats’ femurs using a multiwavelength optical probe. Additionally, the effect of changes in the skin on these parameters was evaluated. Lastly, combining NIRS with photoacoustic (PA) imaging provides a more reliable assessment of tissue parameters, which is further correlated with principal component analysis. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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22 pages, 12618 KiB

Open AccessArticle

Enabling Remote Responder Bio-Signal Monitoring in a Cooperative Human–Robot Architecture for Search and Rescue

by Pablo Vera-Ortega, Ricardo Vázquez-Martín, J. J. Fernandez-Lozano, Alfonso García-Cerezo and Anthony Mandow

Sensors 2023, 23(1), 49; https://doi.org/10.3390/s23010049 - 21 Dec 2022

Cited by 3 | Viewed by 2167

Abstract

The roles of emergency responders are challenging and often physically demanding, so it is essential that their duties are performed safely and effectively. In this article, we address real-time bio-signal sensor monitoring for responders in disaster scenarios. In particular, we propose the integration [...] Read more.

The roles of emergency responders are challenging and often physically demanding, so it is essential that their duties are performed safely and effectively. In this article, we address real-time bio-signal sensor monitoring for responders in disaster scenarios. In particular, we propose the integration of a set of health monitoring sensors suitable for detecting stress, anxiety and physical fatigue in an Internet of Cooperative Agents architecture for search and rescue (SAR) missions (SAR-IoCA), which allows remote control and communication between human and robotic agents and the mission control center. With this purpose, we performed proof-of-concept experiments with a bio-signal sensor suite worn by firefighters in two high-fidelity SAR exercises. Moreover, we conducted a survey, distributed to end-users through the Fire Brigade consortium of the Provincial Council of Málaga, in order to analyze the firefighters’ opinion about biological signals monitoring while on duty. As a result of this methodology, we propose a wearable sensor suite design with the aim of providing some easy-to-wear integrated-sensor garments, which are suitable for emergency worker activity. The article offers discussion of user acceptance, performance results and learned lessons. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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18 pages, 5592 KiB

Open AccessArticle

A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring

by Anastasios G. Samarentsis, Georgios Makris, Sofia Spinthaki, Georgios Christodoulakis, Manolis Tsiknakis and Alexandros K. Pantazis

Sensors 2022, 22(24), 9725; https://doi.org/10.3390/s22249725 - 12 Dec 2022

Cited by 8 | Viewed by 3821

Abstract

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing [...] Read more.

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of

1.19 MP a^{- 1}

, a wide working pressure range

(< 872.4 kPa)

, excellent stability and durability (at least

2.280

cycles), great linearity (

R^{2} = 0.993

), fast response/recovery time

(142 - 160 ms)

, low hysteresis

(D H < 10 %)

and the ability to support a broad spectrum of gait speeds

(30 - 70

steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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17 pages, 5478 KiB

Open AccessArticle

A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement

by Stephen Catsamas, Baiqian Shi, Boris Deletic, Miao Wang and David T. McCarthy

Sensors 2022, 22(19), 7451; https://doi.org/10.3390/s22197451 - 30 Sep 2022

Cited by 5 | Viewed by 4013

Abstract

Current commercial sensors to monitor water flow velocities are expensive, bulky, and require significant effort to install. Low-cost sensors open the possibility of monitoring storm and waste water systems at a much greater spatial and temporal resolution without prohibitive costs and resource investment. [...] Read more.

Current commercial sensors to monitor water flow velocities are expensive, bulky, and require significant effort to install. Low-cost sensors open the possibility of monitoring storm and waste water systems at a much greater spatial and temporal resolution without prohibitive costs and resource investment. To aid in this, this work developed a low-cost, low-power velocity sensor based on acoustic Doppler velocimetry. The sensor, costing less than 50 USD is open-source, open-hardware, compact, and easily interfaceable to a wide range of data-logging systems. A freely available sensor design at this price point does not currently exist, and its novelty is in enabling high-resolution real-time monitoring schemes. The design is capable of measuring water velocities up to 1200 mm/s. The sensor is characterised and then verified in an in-field long-term test. Finally, the data from this test are then used to evaluate the performance of the sensor in a real-world scenario. The analysis concludes that the sensor is capable of effectively measuring water velocity. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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10 pages, 3788 KiB

Open AccessCommunication

Real-Time Temperature Monitoring under Thermal Cycling Loading with Optical Fiber Sensor

by Shiuh-Chuan Her and Jr-Luen Tasi

Sensors 2022, 22(12), 4466; https://doi.org/10.3390/s22124466 - 13 Jun 2022

Cited by 1 | Viewed by 1484

Abstract

A fiber optic sensing system consisting of a fiber Bragg grating (FBG) sensor, optical circulator, optical band pass filter and photodetector is developed to monitor the real-time temperature response of a structure under a dynamic thermal loading. The FBG sensor is surface-bonded on [...] Read more.

A fiber optic sensing system consisting of a fiber Bragg grating (FBG) sensor, optical circulator, optical band pass filter and photodetector is developed to monitor the real-time temperature response of a structure under a dynamic thermal loading. The FBG sensor is surface-bonded on a test specimen and integrated with an optical band pass filter. As a broadband light source transmits into a FBG sensor, a specific wavelength is reflected and transmitted into an optical band pass filter. The reflected wavelength is significantly affected by the temperature, while the output light power from the optical band pass filter is dependent on the wavelength. By measuring the light power with a photodetector, the wavelength can be demodulated, resulting in the determination of the temperature. In this work, the proposed optical sensing system was utilized to monitor the dynamic temperature change of a steel beam under a thermal cycling loading. To verify the accuracy of the fiber optic sensor, a thermocouple was adopted as the reference. The experimental results illustrate a good agreement between the fiber optic sensor and thermocouple. Electronic packages composed of various components such as a solder joint, silicon die, mold compound, and solder mask are frequently subjected to a thermal cycling loading in real-life applications. Temperature variations’ incorporation with mismatches of coefficients of thermal expansion among the assembly components leads to crack growth, damage accumulation and final failure. It is important to monitor the temperature to prevent a thermal fatigue failure. A fast response and easy implementation of the fiber optic sensing system was proposed for the real-time temperature measurement under thermal cycling loading. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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20 pages, 4469 KiB

Open AccessArticle

Non-Contact Activity Monitoring Using a Multi-Axial Inertial Measurement Unit in Animal Husbandry

by Pieter Try and Marion Gebhard

Sensors 2022, 22(12), 4367; https://doi.org/10.3390/s22124367 - 09 Jun 2022

Cited by 1 | Viewed by 1994

Abstract

In this work, a novel method is presented for non-contact non-invasive physical activity monitoring, which utilizes a multi-axial inertial measurement unit (IMU) to measure activity-induced structural vibrations in multiple axes. The method is demonstrated in monitoring the activity of a mouse in a [...] Read more.

In this work, a novel method is presented for non-contact non-invasive physical activity monitoring, which utilizes a multi-axial inertial measurement unit (IMU) to measure activity-induced structural vibrations in multiple axes. The method is demonstrated in monitoring the activity of a mouse in a husbandry cage, where activity is classified as resting, stationary activity and locomotion. In this setup, the IMU is mounted in the center of the underside of the cage floor where vibrations are measured as accelerations and angular rates in the X-, Y- and Z-axis. The ground truth of activity is provided by a camera mounted in the cage lid. This setup is used to record 27.67 h of IMU data and ground truth activity labels. A classification model is trained with 16.17 h of data which amounts to 3880 data points. Each data point contains eleven features, calculated from the X-, Y- and Z-axis accelerometer data. The method achieves over 90% accuracy in classifying activity versus non-activity. Activity is monitored continuously over more than a day and clearly depicts the nocturnal behavior of the inhabitant. The impact of this work is a powerful method to assess activity which enables automatic health evaluation and optimization of workflows for improved animal wellbeing. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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12 pages, 3373 KiB

Open AccessArticle

Drone-Mountable Gas Sensing Platform Using Graphene Chemiresistors for Remote In-Field Monitoring

by Jaewoo Park, Franklyn Jumu, Justin Power, Maxime Richard, Yomna Elsahli, Mohamad Ali Jarkas, Andy Ruan, Adina Luican-Mayer and Jean-Michel Ménard

Sensors 2022, 22(6), 2383; https://doi.org/10.3390/s22062383 - 19 Mar 2022

Cited by 5 | Viewed by 3724

Abstract

We present the design, fabrication, and testing of a drone-mountable gas sensing platform for environmental monitoring applications. An array of graphene-based field-effect transistors in combination with commercial humidity and temperature sensors are used to relay information by wireless communication about the presence of [...] Read more.

We present the design, fabrication, and testing of a drone-mountable gas sensing platform for environmental monitoring applications. An array of graphene-based field-effect transistors in combination with commercial humidity and temperature sensors are used to relay information by wireless communication about the presence of airborne chemicals. We show that the design, based on an ESP32 microcontroller combined with a 32-bit analog-to-digital converter, can be used to achieve an electronic response similar, within a factor of two, to state-of-the-art laboratory monitoring equipment. The sensing platform is then mounted on a drone to conduct field tests, on the ground and in flight. During these tests, we demonstrate a one order of magnitude reduction in environmental noise by reducing contributions from humidity and temperature fluctuations, which are monitored in real-time with a commercial sensor integrated to the sensing platform. The sensing device is controlled by a mobile application and uses LoRaWAN, a low-power, wide-area networking protocol, for real-time data transmission to the cloud, compatible with Internet of Things (IoT) applications. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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Review

Jump to: Research

22 pages, 570 KiB

Open AccessReview

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

by Arrigo Palumbo, Patrizia Vizza, Barbara Calabrese and Nicola Ielpo

Sensors 2021, 21(21), 7172; https://doi.org/10.3390/s21217172 - 28 Oct 2021

Cited by 36 | Viewed by 5774

Abstract

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded [...] Read more.

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications. Full article

(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)

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