Publications
Modeling real-world affective and communicative nonverbal vocalizations from minimally speaking individuals
October 1, 2022
Conference Paper
Published in:
IEEE Trans. on Affect. Comput., Vol. 13, No. 4, October 2022, pp. 2238-53.
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Summary
Nonverbal vocalizations from non- and minimally speaking individuals (mv*) convey important communicative and affective information. While nonverbal vocalizations that occur amidst typical speech and infant vocalizations have been studied extensively in the literature, there is limited prior work on vocalizations by mv* individuals. Our work is among the first studies of the communicative and affective information expressed in nonverbal vocalizations by mv* children and adults. We collected labeled vocalizations in real-world settings with eight mv* communicators, with communicative and affective labels provided in-the-moment by a close family member. Using evaluation strategies suitable for messy, real-world data, we show that nonverbal vocalizations can be classified by function (with 4- and 5-way classifications) with F1 scores above chance for all participants. We analyze labeling and data collection practices for each participating family, and discuss the classification results in the context of our novel real-world data collection protocol. The presented work includes results from the largest classification experiments with nonverbal vocalizations from mv* communicators to date.
Summary
Nonverbal vocalizations from non- and minimally speaking individuals (mv*) convey important communicative and affective information. While nonverbal vocalizations that occur amidst typical speech and infant vocalizations have been studied extensively in the literature, there is limited prior work on vocalizations by mv* individuals. Our work is among the first studies...
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Contrast-enhanced ultrasound to detect active bleeding
October 1, 2022
Abstract
Published in:
J. Acoust. Soc. Am. 152, A280 (2022)
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Non-compressible internal hemorrhage (NCIH) is the most common cause of death in acute non-penetrating trauma. NCIH management requires accurate hematoma localization and evaluation for ongoing bleeding for risk stratification. The current standard point-of-care diagnostic tool, the focused assessment with sonography for trauma (FAST), detects free fluid in body cavities with conventional B-mode imaging. The FAST does not assess whether bleeding is ongoing, at which location(s), and to what extent. Here, we propose contrast-enhanced ultrasound (CEUS) techniques to better identify, localize, and quantify hemorrhage. We designed and fabricated a custom hemorrhage-mimicking phantom, comprising a perforated vessel and cavity to simulate active bleeding. Lumason contrast agents (UCAs) were introduced at clinically relevant concentrations (3.5×108 bubbles/ml). Conventional and contrast pulse sequence images were captured, and post-processed with bubble localization techniques (SVD clutter filter and bubble localization). The results showed contrast pulse sequences enabled a 2.2-fold increase in the number of microbubbles detected compared with conventional CEUS imaging, over a range of flow rates, concentrations, and localization processing parameters. Additionally, particle velocimetry enabled mapping of dynamic flow within the simulated bleeding site. Our findings indicate that CEUS combined with advanced image processing may enhance visualization of hemodynamics and improve non-invasive, real-time detection of active bleeding.
Summary
Non-compressible internal hemorrhage (NCIH) is the most common cause of death in acute non-penetrating trauma. NCIH management requires accurate hematoma localization and evaluation for ongoing bleeding for risk stratification. The current standard point-of-care diagnostic tool, the focused assessment with sonography for trauma (FAST), detects free fluid in body cavities with...
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Multimodal physiological monitoring during virtual reality piloting tasks
August 25, 2022
Journal Article
Published in:
Multimodal Physiological Monitoring During Virtual Reality Piloting Tasks (version 1.0.0). PhysioNet. https://doi-org.ezproxy.canberra.edu.au/10.13026/azwa-ge48.
Summary
This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were presented with four levels of difficulty, which were generated by varying wind speed, turbulence, and visibility. Each of the participants performed 12 runs, split into 3 blocks of four consecutive runs, one run at each difficulty, in a single experimental session. The sequence of difficulty levels was presented in a counterbalanced manner across blocks. Flight performance was quantified as a function of horizontal and vertical deviation from an ideal path towards the runway as well as deviation from the prescribed ideal speed of 115 knots. Multimodal physiological signals were aggregated and synchronized using Lab Streaming Layer. Descriptions of data quality are provided to assess each data stream. The starter code provides examples of loading and plotting the time synchronized data streams, extracting sample features from the eye tracking data, and building models to predict pilot performance from the physiology data streams.
Summary
This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were...
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Development and validation of the public-facing SimAEN web application
August 1, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-6
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Summary
During a pandemic such as COVID-19, non-pharmaceutical interventions (NPIs) can help protect public health; however, it is not always clear which actions will have the greatest positive impact, or what the trade-offs are between different options. Exposure Notification (EN) was introduced as a prevention measure during the COVID-19 pandemic to supplement traditional contact tracing activities. To predict the estimated impacts of EN, a model for "simulation of automated exposure notification" (SimAEN) was developed by researchers at MIT Lincoln Laboratory (MIT LL) with CDC funding [2]. The model was published through an accessible web interface, available for use by the general public at https://SimAEN.philab.cdc.gov/.
Summary
During a pandemic such as COVID-19, non-pharmaceutical interventions (NPIs) can help protect public health; however, it is not always clear which actions will have the greatest positive impact, or what the trade-offs are between different options. Exposure Notification (EN) was introduced as a prevention measure during the COVID-19 pandemic to...
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Transfer learning for automated COVID-19 B-line classification in lung ultrasound
July 15, 2022
Conference Paper
Published in:
44th Annual Int. Conf. of IEEE Engineering in Medicine & Biology Society (EMBC), DOI: 10.1109/EMBC48229.2022.9871894.
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Summary
Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs. normal. We developed transfer learning models based on ResNet networks to classify B-lines as pathologic (at least 3 B-lines per lung field) vs. normal using COVID-19 LUS data. Assessment of B-line severity on a 0-4 multi-class scale was also explored. For binary B-line classification, at the frame-level, all ResNet models pretrained with ImageNet yielded higher performance than the baseline nonpretrained ResNet-18. Pretrained ResNet-18 has the best Equal Error Rate (EER) of 9.1% vs the baseline of 11.9%. At the clip-level, all pretrained network models resulted in better Cohen's kappa agreement (linear-weighted) and clip score accuracy, with the pretrained ResNet-18 having the best Cohen's kappa of 0.815 [95% CI: 0.804-0.826], and ResNet-101 the best clip scoring accuracy of 93.6%. Similar results were shown for multi-class scoring, where pretrained network models outperformed the baseline model. A class activation map is also presented to guide clinicians in interpreting LUS findings. Future work aims to further improve the multi-class assessment for severity of B-lines with a more diverse LUS dataset.
Summary
Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective...
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Feature importance analysis for compensatory reserve to predict hemorrhagic shock
July 15, 2022
Conference Paper
Published in:
44th Annual Int. Conf. of IEEE Engineering in Medicine & Biology Society (EMBC), DOI: 10.1109/EMBC48229.2022.9871661.
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Summary
Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate on an arterial blood pressure (ABP) waveform acquired via photoplethysmography have been shown to provide an effective early indicator. However, these machine learning approaches lack physiological interpretability. In this paper, we evaluate the importance of nine ABP-derived features that provide physiological insight, using a database of 40 human subjects from a lower-body negative pressure model of progressive central hypovolemia. One feature was found to be considerably more important than any other. That feature, the half-rise to dicrotic notch (HRDN), measures an approximate time delay between the ABP ejected and reflected wave components. This delay is an indication of compensatory mechanisms such as reduced arterial compliance and vasoconstriction. For a scale of 0% to 100%, with 100% representing normovolemia and 0% representing decompensation, linear regression of the HRDN feature results in root-mean-squared error of 16.9%, R2 of 0.72, and an area under the receiver operating curve for detecting decompensation of 0.88. These results are comparable to previously reported results from the more complex black box machine learning models. Clinical Relevance- A single physiologically interpretable feature measured from an arterial blood pressure waveform is shown to be effective in monitoring for blood loss and impending hemorrhagic shock based on data from a human lower-body negative pressure model of progressive central hypolemia.
Summary
Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate...
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Axon tracing and centerline detection using topologically-aware 3D U-nets
July 11, 2022
Journal Article
Published in:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022, pp. 238-242
Summary
As advances in microscopy imaging provide an ever clearer window into the human brain, accurate reconstruction of neural connectivity can yield valuable insight into the relationship between brain structure and function. However, human manual tracing is a slow and laborious task, and requires domain expertise. Automated methods are thus needed to enable rapid and accurate analysis at scale. In this paper, we explored deep neural networks for dense axon tracing and incorporated axon topological information into the loss function with a goal to improve the performance on both voxel-based segmentation and axon centerline detection. We evaluated three approaches using a modified 3D U-Net architecture trained on a mouse brain dataset imaged with light sheet microscopy and achieved a 10% increase in axon tracing accuracy over previous methods. Furthermore, the addition of centerline awareness in the loss function outperformed the baseline approach across all metrics, including a boost in Rand Index by 8%.
Summary
As advances in microscopy imaging provide an ever clearer window into the human brain, accurate reconstruction of neural connectivity can yield valuable insight into the relationship between brain structure and function. However, human manual tracing is a slow and laborious task, and requires domain expertise. Automated methods are thus needed...
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Fun as a strategic advantage: applying lessons in engagement from commercial games to military logistics training
July 1, 2022
Journal Article
Published in:
Train., Educ., Learn. Sci., Vol. 59, 2022, p. 180-7.
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Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players and hold their attention. But do those engagement-improving methods have a place in instructional environments with a captive and motivated audience? Our experience building a logistics supply chain training game for the Marine Corps University suggests that yes; applying lessons in engagement from commercial games can both help improve player experience with the learning environment, and potentially improve learning outcomes.
Summary
Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players...
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Toward improving EN adoption: Bridging the gap between stated intention and actual use
June 3, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-7
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact Tracing (PACT) protocol and related projects to assist the public health objective of slowing the spread of SARS-CoV-2 through digital contact tracing. The joint Google and Apple deployed protocol (Google-Apple Exposure Notifications, also known as GAEN or EN), which became the de facto standard in the U.S., employs the same features as detailed by PACT. The protocol leverages smartphone Bluetooth communications to alert users of potential contact with those carrying the COVID-19 virus in a way that preserves the privacy of both the known-infected individual, and the users receiving the alert. Contact tracing and subsequent personal precautions are more effective at reducing disease spread when more of the population participates, but there are known difficulties with the adoption of novel technology. In order to help the U.S. Centers for Disease Control and Prevention (CDC) and U.S. state-level public health teams address these difficulties, a team of staff from MIT's Lincoln Laboratory (MIT LL) and Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) focused on studying user perception and information needs.
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact...
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COVID-19 exposure notification in simulated real-world environments
April 15, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-3
Summary
Privacy-preserving contact tracing mobile applications, such as those that use the Google-Apple Exposure Notification (GAEN) service, have the potential to limit the spread of COVID-19 in communities, but the privacy-preserving aspects of the protocol make it difficult to assess the performance of the apps in real-world populations. To address this gap, we exercised the CovidWatch app on both Android and iOS phones in a variety of scripted realworld scenarios, relevant to the lives of university students and employees. We collected exposure data from the app and from the lower-level Android service, and compared it to the phones' actual distances and durations of exposure, to assess the sensitivity and specificity of the GAEN service configuration as of February 2021. Based on the app's reported ExposureWindows and alerting thresholds for Low and High alerts, our assessment is that the chosen configuration is highly sensitive under a range of realistic scenarios and conditions. With this configuration, the app is likely to capture many long-duration encounters, even at distances greater than six feet, which may be desirable under conditions with increased risk of airborne transmission.
Summary
Privacy-preserving contact tracing mobile applications, such as those that use the Google-Apple Exposure Notification (GAEN) service, have the potential to limit the spread of COVID-19 in communities, but the privacy-preserving aspects of the protocol make it difficult to assess the performance of the apps in real-world populations. To address this...
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