Publications
The Simulation of Automated Exposure Notification (SimAEN) Model
April 15, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-5
Summary
Automated Exposure Notication (AEN) was implemented in 2020 to supplement traditional contact tracing for COVID-19 by estimating "too close for too long" proximities of people using the service. AEN uses Bluetooth messages to privately label and recall proximity events, so that persons who were likely exposed to SARS-CoV-2 can take the appropriate steps recommended by their health care authority. This paper describes an agent-based model that estimates the effects of AEN deployment on COVID-19 caseloads and public health workloads in the context of other critical public health measures available during the COVID-19 pandemic. We selected simulation variables pertinent to AEN deployment options, varied them in accord with the system dynamics available in 2020-2021, and calculated the outcomes of key metrics across repeated runs of the stochastic multi-week simulation. SimAEN's parameters were set to ranges of observed values in consultation with public health professionals and the rapidly accumulating literature on COVID-19 transmission; the model was validated against available population-level disease metrics. Estimates from SimAEN can help public health officials determine what AEN deployment decisions (e.g., configuration, workflow integration, and targeted adoption levels) can be most effective in their jurisdiction, in combination with other COVID-19 interventions (e.g., mask use, vaccination, quarantine and isolation periods).
Summary
Automated Exposure Notication (AEN) was implemented in 2020 to supplement traditional contact tracing for COVID-19 by estimating "too close for too long" proximities of people using the service. AEN uses Bluetooth messages to privately label and recall proximity events, so that persons who were likely exposed to SARS-CoV-2 can take...
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Bluetooth Low Energy (BLE) Data Collection for COVID-19 Exposure Notification
April 13, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-2
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; however, the privacy-preserving aspects of the protocol make it difficult to assess the performance of the Bluetooth proximity detector in real-world populations. The GAEN service configuration of weights and thresholds enables hundreds of thousands of potential configurations, and it is not well known how the detector performance of candidate GAEN configurations maps to the actual "too close for too long" standard used by public health contact tracing staff. To address this gap, we exercised a GAEN app on Android phones at a range of distances, orientations, and placement configurations (e.g., shirt pocket, bag, in hand), using RF-analogous robotic substitutes for human participants. We recorded exposure data from the app and from the lower-level Android service, along with the phones' actual distances and durations of exposure.
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; however, the privacy-preserving aspects of the protocol make it difficult to assess the performance of the Bluetooth proximity detector in real-world populations. The...
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Wearable technology in extreme environments
March 14, 2022
Book Chapter
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Chapter 2 in: Cibis, T., McGregor AM, C. (eds) Engineering and Medicine in Extreme Environments. Springer, Cham. https://doi-org.ezproxy.canberra.edu.au/10.1007/978-3-030-96921-9_2
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Humans need to work in many types of extreme environments where there is a need to stay safe and even to improve performance. Examples include: medical providers treating infectious disease, people responding to other biological or chemical hazards, firefighters, astronauts, pilots, divers, and people working outdoors in extreme hot or cold temperatures. Wearable technology is ubiquitous in the consumer market but is still needed for extreme environments. For these applications, it is particularly challenging to meet requirements to be actionable, accurate, acceptable, integratable, and affordable. To provide insight into these needs and possible solutions and the technology trade-offs involved, several examples are provided. A physiological monitoring example is described for predicting and avoiding heat injury. A cognitive monitoring example is described for estimating cognitive workload, with broader applicability to a variety of conditions, such as cognitive fatigue and depression. Finally, eye tracking is considered as a promising wearable sensing modality with applications for both physiological and cognitive monitoring. Concluding thoughts are offered on the compelling need for wearable technology in the face of pandemics, wildfires, and climate change, but also for global projects that can uplift mankind, such as long-duration spaceflight and missions to Mars.
Summary
Humans need to work in many types of extreme environments where there is a need to stay safe and even to improve performance. Examples include: medical providers treating infectious disease, people responding to other biological or chemical hazards, firefighters, astronauts, pilots, divers, and people working outdoors in extreme hot or...
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Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study
March 2, 2022
Journal Article
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Sci. Rep., Vol. 12, No. 1, 2 March 2022.
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Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.
Summary
Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily...
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Artificial intelligence for detecting COVID-19 with the aid of human cough, breathing and speech signals: scoping review
February 14, 2022
Journal Article
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IEEE Open J. Eng. Med. Biol., 14 February 2022, pp. 235-41.
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Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data forpreliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in theliterature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15st August 2021. Terms were selected based on thetarget intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of thedisease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Halfof the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 for cough-, breathing- or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. However, the proposed methods with some time and appropriate clinical testing would prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in human body.
Summary
Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data forpreliminary screening may help alleviate these issues. Objective: This scoping review...
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Speech as a biomarker: opportunities, interoperability, and challenges
February 11, 2022
Journal Article
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Perspectives of the ASHA Special Interest Groups, Vo. 7, February 2022, pp. 276-83.
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Purpose: Over the past decade, the signal processing and machine learning literature has demonstrated notable advancements in automated speech processing with the use of artificial intelligence for medical assessment and monitoring (e.g., depression, dementia, and Parkinson's disease, among others). Meanwhile, the clinical speech literature has identified several interpretable, theoretically motivated measures that are sensitive to abnormalities in the cognitive, linguistic, affective, motoric, and anatomical domains. Both fields have, thus, independently demonstrated the potential for speech to serve as an informative biomarker for detecting different psychiatric and physiological conditions. However, despite these parallel advancements, automated speech biomarkers have not been integrated into routine clinical practice to date. Conclusions: In this article, we present opportunities and challenges for adoption of speech as a biomarker in clinical practice and research. Toward clinical acceptance and adoption of speech-based digital biomarkers, we argue for the importance of several factors such as robustness, specificity, diversity, and physiological interpretability of speech analytics in clinical applications.
Summary
Purpose: Over the past decade, the signal processing and machine learning literature has demonstrated notable advancements in automated speech processing with the use of artificial intelligence for medical assessment and monitoring (e.g., depression, dementia, and Parkinson's disease, among others). Meanwhile, the clinical speech literature has identified several interpretable, theoretically motivated...
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EEG alpha and pupil diameter reflect endogenous auditory attention switching and listening effort
January 19, 2022
Journal Article
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Eur. J. Neurosci., 2022, pp. 1-16.
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Everyday environments often contain distracting competing talkers and background noise, requiring listeners to focus their attention on one acoustic source and reject others. During this auditory attention task, listeners may naturally interrupt their sustained attention and switch attended sources. The effort required to perform this attention switch has not been well studied in the context of competing continuous speech. In this work, we developed two variants of endogenous attention switching and a sustained attention control. We characterized these three experimental conditions under the context of decoding auditory attention, while simultaneously evaluating listening effort and neural markers of spatial-audio cues. A least-squares, electroencephalography (EEG) based, attention decoding algorithm was implemented across all conditions. It achieved an accuracy of 69.4% and 64.0% when computed over non-overlapping 10 and 5-second correlation windows, respectively. Both decoders illustrated smooth transitions in the attended talker prediction through switches at approximately half of the analysis window size (e.g. the mean lag taken across the two switch conditions was 2.2 seconds when the 5-second correlation window was used). Expended listening effort, as measured by simultaneous EEG and pupillometry, was also a strong indicator of whether the listeners sustained attention or performed an endogenous attention switch (peak pupil diameter measure (p = 0.034) and minimum parietal alpha power measure (p = 0.016)). We additionally found evidence of talker spatial cues in the form of centrotemporal alpha power lateralization (p = 0.0428). These results suggest that listener effort and spatial cues may be promising features to pursue in a decoding context, in addition to speech-based features.
Summary
Everyday environments often contain distracting competing talkers and background noise, requiring listeners to focus their attention on one acoustic source and reject others. During this auditory attention task, listeners may naturally interrupt their sustained attention and switch attended sources. The effort required to perform this attention switch has not been...
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Gait instability and estimated core temperature predict exertional heat stroke
January 12, 2022
Journal Article
Published in:
Br J Sports Med., online ahead of print, 12 January 2022.
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Objective Exertional heat stroke (EHS), characterised by a high core body temperature (Tcr) and central nervous system (CNS) dysfunction, is a concern for athletes, workers and military personnel who must train and perform in hot environments. The objective of this study was to determine whether algorithms that estimate Tcr from heart rate and gait instability from a trunk-worn sensor system can forward predict EHS onset. Methods Heart rate and three-axis accelerometry data were collected from chest-worn sensors from 1806 US military personnel participating in timed 4/5-mile runs, and loaded marches of 7 and 12 miles; in total, 3422 high EHS-risk training datasets were available for analysis. Six soldiers were diagnosed with heat stroke and all had rectal temperatures of >41°C when first measured and were exhibiting CNS dysfunction. Estimated core temperature (ECTemp) was computed from sequential measures of heart rate. Gait instability was computed from three-axis accelerometry using features of pattern dispersion and autocorrelation. Results The six soldiers who experienced heat stroke were among the hottest compared with the other soldiers in the respective training events with ECTemps ranging from 39.2°C to 40.8°C. Combining ECTemp and gait instability measures successfully identified all six EHS casualties at least 3.5 min in advance of collapse while falsely identifying 6.1% (209 total false positives) examples where exertional heat illness symptoms were neither observed nor reported. No false-negative cases were noted. Conclusion The combination of two algorithms that estimate Tcr and ataxic gate appears promising for real-time alerting of impending EHS.
Summary
Objective Exertional heat stroke (EHS), characterised by a high core body temperature (Tcr) and central nervous system (CNS) dysfunction, is a concern for athletes, workers and military personnel who must train and perform in hot environments. The objective of this study was to determine whether algorithms that estimate Tcr from...
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Alternative cue and response modalities maintain the Simon effect but impact task performance
January 7, 2022
Journal Article
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Appl. Ergon., Vol. 100, 2022, 103648.
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Inhibitory control, the ability to inhibit impulsive responses and irrelevant stimuli, enables high level functioning and activities of daily living. The Simon task probes inhibition using interfering stimuli, i.e., cues spatially presented on the opposite side of the indicated response; incongruent response times (RT) are slower than congruent RTs. Operational applicability of the Simon task beyond finger/hand manipulations and visual/auditory cues is unclear, but important to consider as new technologies provide tactile cues and require motor responses from the lower extremity (e.g., exoskeletons). In this study, twenty participants completed the Simon task under four conditions, each combination of two cue (visual/tactile) and response (upper/lower-extremity) modalities. RT were significantly longer for incongruent than congruent cues across cue-response pairs. However, alternative cue-response pairs yielded slower RT and decreased accuracy for tactile cues and lower extremity responses. Results support operational usage of the Simon task to probe inhibition using tactile cues and lower-extremity responses relevant for new technologies like exoskeletons and immersive environments.
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Inhibitory control, the ability to inhibit impulsive responses and irrelevant stimuli, enables high level functioning and activities of daily living. The Simon task probes inhibition using interfering stimuli, i.e., cues spatially presented on the opposite side of the indicated response; incongruent response times (RT) are slower than congruent RTs. Operational...
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Individuals differ in muscle activation patterns during early adaptation to a powered ankle exoskeleton
January 1, 2022
Journal Article
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Applied Ergonomics Volume 98, January 2022, 103593
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Exoskeletons have the potential to assist users and augment physical ability. To achieve these goals across users, individual variation in muscle activation patterns when using an exoskeleton need to be evaluated. This study examined individual muscle activation patterns during walking with a powered ankle exoskeleton. 60% of the participants were observed to reduce medial gastrocnemius activation with exoskeleton powered and increase with the exoskeleton unpowered during stance. 80% of the participants showed a significant increase in tibialis anterior activation upon power addition, with inconsistent changes upon power removal during swing. 60% of the participants that were able to adapt to the system, did not de-adapt after 5 min. Muscle activity patterns differ between individuals in response to the exoskeleton power state, and affected the antagonist muscle behavior during this early adaptation. It is important to understand these different individual behaviors to inform the design of exoskeleton controllers and training protocols.
Summary
Exoskeletons have the potential to assist users and augment physical ability. To achieve these goals across users, individual variation in muscle activation patterns when using an exoskeleton need to be evaluated. This study examined individual muscle activation patterns during walking with a powered ankle exoskeleton. 60% of the participants were...
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