Publications
Predicting ankle moment trajectory with adaptive weighted ensemble of LSTM network
November 1, 2022
Conference Paper
Published in:
2022 IEEE High Perf. Extreme Comp. Conf. (HPEC), 19-23 September 2022, DOI: 10.1109/HPEC55821.2022.9926370.
Summary
Estimations of ankle moments can provide clinically helpful information on the function of lower extremities and further lead to insight on patient rehabilitation and assistive wearable exoskeleton design. Current methods for estimating ankle moments leave room for improvement, with most recent cutting-edge methods relying on machine learning models trained on wearable sEMG and IMU data. While machine learning eliminates many practical challenges that troubled more traditional human body models for this application, we aim to expand on prior work that showed the feasibility of using LSTM models by employing an ensemble of LSTM networks. We present an adaptive weighted LSTM ensemble network and demonstrate its performance during standing, walking, running, and sprinting. Our result show that the LSTM ensemble outperformed every single LSTM model component within the ensemble. Across every activity, the ensemble reduced median root mean squared error (RMSE) by 0.0017-0.0053 N. m/kg, which is 2.7 – 10.3% lower than the best performing single LSTM model. Hypothesis testing revealed that most reductions in RMSE were statistically significant between the ensemble and other single models across all activities and subjects. Future work may analyze different trajectory lengths and different combinations of LSTM submodels within the ensemble.
Summary
Estimations of ankle moments can provide clinically helpful information on the function of lower extremities and further lead to insight on patient rehabilitation and assistive wearable exoskeleton design. Current methods for estimating ankle moments leave room for improvement, with most recent cutting-edge methods relying on machine learning models trained on...
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Affective ratings of nonverbal vocalizations produced by minimally-speaking individuals: What do native listeners perceive?
October 18, 2022
Conference Paper
Published in:
10th Intl. Conf. Affective Computing and Intelligent Interaction, ACII, 18-21 October 2022.
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Individuals who produce few spoken words ("minimally-speaking" individuals) often convey rich affective and communicative information through nonverbal vocalizations, such as grunts, yells, babbles, and monosyllabic expressions. Yet, little data exists on the affective content of the vocal expressions of this population. Here, we present 78,624 arousal and valence ratings of nonverbal vocalizations from the online ReCANVo (Real-World Communicative and Affective Nonverbal Vocalizations) database. This dataset contains over 7,000 vocalizations that have been labeled with their expressive functions (delight, frustration, etc.) from eight minimally-speaking individuals. Our results suggest that raters who have no knowledge of the context or meaning of a nonverbal vocalization are still able to detect arousal and valence differences between different types of vocalizations based on Likert-scale ratings. Moreover, these ratings are consistent with hypothesized arousal and valence rankings for the different vocalization types. Raters are also able to detect arousal and valence differences between different vocalization types within individual speakers. To our knowledge, this is the first large-scale analysis of affective content within nonverbal vocalizations from minimally verbal individuals. These results complement affective computing research of nonverbal vocalizations that occur within typical verbal speech (e.g., grunts, sighs) and serve as a foundation for further understanding of how humans perceive emotions in sounds.
Summary
Individuals who produce few spoken words ("minimally-speaking" individuals) often convey rich affective and communicative information through nonverbal vocalizations, such as grunts, yells, babbles, and monosyllabic expressions. Yet, little data exists on the affective content of the vocal expressions of this population. Here, we present 78,624 arousal and valence ratings of...
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Science applications of phased array radars
October 1, 2022
Journal Article
Published in:
Bull. Amer. Meteorological Soc. (BAMS), Vol. 103, No. 10, 2022, pp. E2370-E2390.
Summary
Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors, including (i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and (ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.
Summary
Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to...
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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/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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The tale of discovering a side channel in secure message transmission systems
August 13, 2022
Conference Paper
Published in:
The Conf. for Failed Approaches and Insightful Losses in Cryptology, CFAIL, 13 August 2022.
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Secure message transmission (SMT) systems provide information theoretic security for point-to-point message transmission in networks that are partially controlled by an adversary. This is the story of a research project that aimed to implement a flavour of SMT protocols that uses "path hopping" with the goal of quantifying the real-life efficiency of the system, and while failing to achieve this initial goal, let to the discovery a side-channel that affects the security of a wide range of SMT implementations.
Summary
Secure message transmission (SMT) systems provide information theoretic security for point-to-point message transmission in networks that are partially controlled by an adversary. This is the story of a research project that aimed to implement a flavour of SMT protocols that uses "path hopping" with the goal of quantifying the real-life...
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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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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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