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  • 1
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    Performance Evaluation of a Low-Cost Sensing Unit for Seismic Applications: Field Testing During Seismic Events of 2016-2017 in Central Italy (2019)
    Details
    Publication Date: 2019-03-26
    Description: We propose a low-cost and low-power consumption device for seismic monitoring consisting of three single-axis accelerometers connected to a data logger with acquisition, synchronization, and transmission functionalities. The device was designed to be densely and prolifically deployed in high seismic risk areas, thus strengthening the Italian seismic network and providing a more accurate estimation of shaking maps. Moreover, the availability of such low-cost and high-performance units can allow the widespread diffusion of smart systems for seismic and structural monitoring, finalized to collapses prevention in critical structures, such as schools and hospitals, as well as constitute the founding nucleus of early warning systems based on Internet of Things architectures. The realized station was submitted to a testing phase, placing it contiguously to a high-performance seismic station located in central Italy and responsible for national seismic monitoring. The test station, installed from September 2016 to March 2017, was able to record the significant and numerous earthquakes that devastated central Italy during this period. The simultaneous acquisition of these seismic events by the sensors of the national seismic network, including that co-sited with the device under test, has furnished sufficient data for the device validation and performance quantification. A comparative analysis was performed through waveforms correlations study, strong motion parameters estimation, and spectral analysis. The proposed device demonstrated performances very close to those of more sophisticated and expensive systems. Therefore, it can effectively replace them or be added in engineering and civil protection applications and, finally, be used in earthquake early warning systems.
    Description: Published
    Description: 6644-6659
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: JCR Journal
    Keywords: Early warning systems ; earthquake ; Internet of Things ; MEMS ; seismic sensors ; structural monitoring
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
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    Activity Monitoring Through Wireless Sensor Networks Embedded Into Smart Sport Equipments: The Nordic Walking Training Utility (2023)
    IEEE
    Details
    Publication Date: 2023-02-28
    Description: This paper presents the study of NordicWalking providing objective evaluations based on real time acquisition of kinematic parameters during the sport practice. It is possible to carefully monitor the athletic gesture through the integration of conventional poles with inertial sensors, composed of a triaxial accelerometer, a triaxial gyroscope, a pressure sensor positioned on the handle, and a load cell, which constitute aWireless Sensor Networkwhose nodes are appropriately synchronized. The integration of such sensors, whichmust be unobstructive and not change the functionality of the poles, is dictated by the ultimate goal of providing a real time biofeedback in two possible scenarios. The first is intended for Nordic Walking’s instructors, who have the opportunity to verify the proper practice execution by their trainees through the availability of real time objective data, in addition to their personalexperience.The second is devoted to amateur playerswho can practicealone, after the training sessionwith the instructor, and can independently correct any imperfections in real time using a software tool running on their smartphone. Using the Dynamic TimeWarping algorithm, the proposed system identifies themost frequent errors in performing athletic gesture, allowing adjustment in real time of the sporting exercise, through the detection, quantification and correction of errors. The obtained results show that the developed system is able to provide an accurate analysis of the athletic gesture and the proposed algorithm allows a quantitative monitoring of the progress achieved by each subject over time.
    Description: Published
    Description: 2744 - 2757
    Description: 7TM.Sviluppo e Trasferimento Tecnologico
    Description: JCR Journal
    Keywords: Activity monitoring ; data analytics ; Internet Of Things ; smart sport equipments ; wireless sensor network ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
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    Internet of Things for Earthquake Early Warning Systems: A Performance Comparison Between Communication Protocols (2024)
    IEEE
    Details
    Publication Date: 2024-02-06
    Description: Earthquake Early Warning Systems (EEWSs) characterize seismic events in real time and estimate the expected ground motion amplitude in specific areas to send alerts before the destructive waves arrive. Together with the reliability of the results, the rapidity with which an EEWS can detect an earthquake becomes a focal point for developing efficient seismic node networks. Internet of Things (IoT) architectures can be used in EEWSs to expand a seismic network and acquire data even from low-cost seismic nodes. However, the latency and the total alert time introduced by the adopted communication protocols should be carefully evaluated. This study proposes an IoT solution based on the message queue-telemetry transport protocol for the waveform transmission acquired by seismic nodes and presents a performance comparison between it and the most widely used standard in current EEWSs. The comparison was performed in evaluation tests where different seismic networks were simulated using a dataset of real earthquakes. This study analyzes the phases preceding the earthquake detection, showing how the proposed solution detects the same events of traditional EEWSs with a total alert time of approximately 1.6 seconds lower.
    Description: Published
    Description: 43183 - 43194
    Description: OST4 Descrizione in tempo reale del terremoto, del maremoto, loro predicibilità e impatto
    Description: JCR Journal
    Keywords: Earthquake early warning systems ; Internet of Things ; message queue telemetry transport protocol ; , SeedLink protocol ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 4
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    A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning (2024)
    MDPI
    Details
    Publication Date: 2024-02-07
    Description: An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.
    Description: Published
    Description: 8431
    Description: OST4 Descrizione in tempo reale del terremoto, del maremoto, loro predicibilità e impatto
    Description: JCR Journal
    Keywords: Internet of Things ; cloud computing ; early warning systems ; earthquake localization ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 5
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    Low-cost MEMS accelerometers for earthquake early warning systems: A dataset collected during seismic events in central Italy (2024)
    Elsevier
    Details
    Publication Date: 2024-03-13
    Description: This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25,001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources. The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.
    Description: Published
    Description: 110174
    Description: OST5 Verso un nuovo Monitoraggio
    Description: JCR Journal
    Keywords: Earthquake early warning; Internet of things; MEMS accelerometers; Structural health monitoring; Wireless sensor network ; 05.04. Instrumentation and techniques of general interest ; 05.02. Data dissemination ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 6
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    Data acquired by wearable sensors for the evaluation of the flexion-relaxation phenomenon (2020)
    Elsevier
    Details
    Publication Date: 2020-08-01
    Electronic ISSN: 2352-3409
    Topics: Biology
    Published by Elsevier
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  • 7
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    A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-06-13
    Description: The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term “complex environment”, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation.
    Electronic ISSN: 2313-433X
    Topics: Computer Science
    Published by Molecular Diversity Preservation International
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  • 8
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    A dataset for wearable sensors validation in gait analysis (2020)
    Elsevier
    Details
    Publication Date: 2020-08-01
    Electronic ISSN: 2352-3409
    Topics: Biology
    Published by Elsevier
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  • 9
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    A Wireless Body Sensor Network for Clinical Assessment of the Flexion-Relaxation Phenomenon (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-06-24
    Description: An accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon using a wireless body sensor network consisting of sEMG sensors in association with a wearable device that integrates accelerometer, gyroscope, and magnetometer. The raw data collected from the sensors during the flexion relaxation test are processed by an algorithm able to identify the phases of which the test is composed, provide an evaluation of the myoelectric activity and automatically detect the phenomenon presence/absence. The developed algorithm was used to process the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on back muscles of subjects with and without Low Back Pain. The results have shown that the proposed method is significant for myoelectric silence detection and for clinical assessment of electromyography activity patterns.
    Electronic ISSN: 2079-9292
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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  • 10
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    An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals (2019)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2019-07-24
    Description: Divers’ health state after underwater activity can be assessed after the immersion using precordial echo Doppler examination. An audio analysis of the acquired signals is performed by specialist doctors to detect circulating gas bubbles in the vascular system and to evaluate the decompression sickness risk. Since on-site medical assistance cannot always be guaranteed, we propose a system for automatic emboli detection using a custom portable device connected to the echo Doppler instrument. The empirical mode decomposition method is used to develop a real-time algorithm able to automatically detect embolic events and, consequently, assess the decompression sickness risk according to the Spencer’s scale. The proposed algorithm has been tested according to an experimental protocol approved by the Divers Alert Network. It involved 30 volunteer divers and produced 37 echo Doppler files useful for the algorithm’s performances evaluation. The results obtained by the proposed emboli detection algorithm (83% sensitivity and 76% specificity) make the system particularly suitable for real-time evaluation of the decompression sickness risk level. Furthermore, the system could also be used in continuous monitoring of hospitalized patients with embolic risks such as post surgery ones.
    Electronic ISSN: 2079-9292
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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