In recent years, there has been an increasing interest in the use of highly nonlinear solitary waves (HNSWs) for nondestructive evaluation and structural health monitoring applications. HNSWs are mechanical waves that can form and travel in highly nonlinear systems, such as granular particles in Hertzian contact. The easiest setup consists of a built-in transducer in drypoint contact with the structure or material to be inspected/monitored. The transducer is made of a monoperiodic array of spherical particles that enables the excitation and detection of the solitary waves. The transducer is wired to a data acquisition system that controls the functionality of the transducer and stores the time series for post-processing. In this paper, the design and testing of a wireless unit that enables the remote control of a transducer without the need to connect it to sophisticated test equipment are presented. Comparative tests and analyses between the measurements obtained with the newly designed wireless unit and the conventional wired configuration are provided. The results are corroborated by an analytical model that predicts the dynamic interaction between solitary waves and materials with different modulus. The advantages and limitations of the proposed wireless platform are given along with some suggestions for future developments.
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Remote wireless monitoring of localized corrosion using compact solitary waves
This article presents a method to monitor corrosion remotely, based on highly nonlinear solitary waves, which are compact and nondispersive. In the study presented in this article, two types of solitary wave transducers were used to monitor accelerated localized corrosion on a steel plate. The first type consists of a chain of spherical particles surmounted by a commercial solenoid wired to, and controlled by, a data acquisition system used to lift and release the first particle of the chain and induce the mechanical impacts and stress waves in the chain. The chain included a piezoelectric wafer disk, also wired to the same data acquisition system, to sense, digitize, and store the propagating waves for post-processing. The second type of transducer was identical to the first one but the data acquisition system was replaced by a wireless node that communicated with a mobile device using a Bluetooth connection. Eight transducers were used to monitor the plate for over a week to detect the onset and progression of localized corrosion. Corrosion detection was performed by extracting a few features from the time waveforms and feeding these features to an outlier analysis algorithm based on the Mahalanobis distance. The results of the experiment showed the effectiveness of the proposed monitoring approach at detecting defects close to the transducers and confirm previous numerical predictions by the authors. The experiments also provided evidence that the performance of the wireless transducers is nearly identical to the performance of their wired counterparts, paving the way to a new paradigm for the structural health monitoring of remote structural components in harsh environments.
more » « less- Award ID(s):
- 1809932
- NSF-PAR ID:
- 10369372
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Structural Health Monitoring
- Volume:
- 22
- Issue:
- 3
- ISSN:
- 1475-9217
- Page Range / eLocation ID:
- p. 1876-1892
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract A nondestructive evaluation (NDE) technique based on highly nonlinear solitary waves (HNSWs) has been developed recently by a few groups worldwide. The technique is based on the propagation and detection of these waves along a one-dimensional monoperiodic array of spherical particles in which one end of the array is in contact with the material/structure to be inspected, and the particle at the opposite end induces the waves by means of a mechanical impact. Several studies have demonstrated that the dynamic interaction between the waves and the element to be evaluated is dependent on the geometric and mechanical properties of the structure, and such dependency can be monitored by sensing the waves reflected at the interface between the array and the structure. This NDE technique is typically performed by using the so-called HNSW transducer. The term transducer indicates a portable device that consists of a monoperiodic array of particles, a device to trigger the waves, and a sensing element to detect the waves. In the study presented in this article, the long-term performance of three transducers was investigated by placing them above a test object whose mechanical and geometric properties were left constant for a week while the transducers triggered and detected thousands of waves. Any variability of the waves was quantified by extracting simple features such as amplitude, time of flight, and cross-correlation. To investigate the cause of variabilities, 16 measurements were captured with short videos at ∼1000 fps. The results of the study demonstrate that the traveling time of the solitary waves is the most reliable parameter for long-term monitoring with the lowest variability and the least susceptibility to physical changes within the array. In addition, the findings of this study allow the framing of a valid strategy to improve the design of the transducers in order to make the HNSW-based technique suitable for long-term monitoring.more » « less
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Obeid, Iyad Selesnick (Ed.)Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.more » « less
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Precise form-fitting of prosthetic sockets is important for the comfort and well-being of persons with limb amputations. Capabilities for continuous monitoring of pressure and temperature at the skin-prosthesis interface can be valuable in the fitting process and in monitoring for the development of dangerous regions of increased pressure and temperature as limb volume changes during daily activities. Conventional pressure transducers and temperature sensors cannot provide comfortable, irritation-free measurements because of their relatively rigid construction and requirements for wired interfaces to external data acquisition hardware. Here, we introduce a millimeter-scale pressure sensor that adopts a soft, three-dimensional design that integrates into a thin, flexible battery-free, wireless platform with a built-in temperature sensor to allow operation in a noninvasive, imperceptible fashion directly at the skin-prosthesis interface. The sensor system mounts on the surface of the skin of the residual limb, in single or multiple locations of interest. A wireless reader module attached to the outside of the prosthetic socket wirelessly provides power to the sensor and wirelessly receives data from it, for continuous long-range transmission to a standard consumer electronic device such as a smartphone or tablet computer. Characterization of both the sensor and the system, together with theoretical analysis of the key responses, illustrates linear, accurate responses and the ability to address the entire range of relevant pressures and to capture skin temperature accurately, both in a continuous mode. Clinical application in two prosthesis users demonstrates the functionality and feasibility of this soft, wireless system.
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Abstract This article describes the design, construction, and field‐testing of a standalone networked animal‐borne monitoring system conceived to study community ecology remotely. The system consists of an assemblage of identical battery‐powered sensing devices with wireless communication capabilities that are each collar‐mounted on a study animal and together form a mobile ad hoc network. The sensing modalities of each device include high‐definition video, inertial accelerometry, and location resolved via a global positioning system module. Our system is conceived to use information exchange across the network to enable the devices to jointly decide without supervision when and how to use each sensing modality. The ultimate goal is to extend battery life while making sure that important events are appropriately documented. This requires judicious use of highly informative but power‐hungry sensing modalities, such as video, because battery capacity is constrained by stringent weight and dimension restrictions. We have proposed algorithms to regulate sensing rates, data transmission among devices, and triggering for video recording based on location and animal group movements and configuration. We have also developed the hardware and firmware of our devices to reliably execute these algorithms in the exacting conditions of real‐life deployments. We describe validation of the performance and reliability of our system using deployment results for a mission in Gorongosa National Park (Mozambique) to monitor two species in their natural habitat: the waterbuck and the African buffalo. We present movement data and snapshots of animal point‐of‐view videos collected by 14 fully operational devices collared on 10 waterbucks and 4 buffaloes.
