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COVID-19 exposure notification in simulated real-world environments

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.
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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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The Simulation of Automated Exposure Notification (SimAEN) Model

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).
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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

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.
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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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Alternative cue and response modalities maintain the Simon effect but impact task performance

Published in:
Appl. Ergon., Vol. 100, 2022, 103648.

Summary

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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Summary

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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AI-enabled, ultrasound-guided handheld robotic device for femoral vascular access

Summary

Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions. However, central access is normally performed by highly experienced critical care physicians in a hospital setting. We developed a handheld AI-enabled interventional device, AI-GUIDE (Artificial Intelligence Guided Ultrasound Interventional Device), capable of directing users with no ultrasound or interventional expertise to catheterize a deep blood vessel, with an initial focus on the femoral vein. AI-GUIDE integrates with widely available commercial portable ultrasound systems and guides a user in ultrasound probe localization, venous puncture-point localization, and needle insertion. The system performs vascular puncture robotically and incorporates a preloaded guidewire to facilitate the Seldinger technique of catheter insertion. Results from tissue-mimicking phantom and porcine studies under normotensive and hypotensive conditions provide evidence of the technique's robustness, with key performance metrics in a live porcine model including: a mean time to acquire femoral vein insertion point of 53 plus or minus 36 s (5 users with varying experience, in 20 trials), a total time to insert catheter of 80 plus or minus 30 s (1 user, in 6 trials), and a mean number of 1.1 (normotensive, 39 trials) and 1.3 (hypotensive, 55 trials) needle insertion attempts (1 user). These performance metrics in a porcine model are consistent with those for experienced medical providers performing central vascular access on humans in a hospital.
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Summary

Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions...

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Relationships between cognitive factors and gait strategy during exoskeleton-augmented walking

Published in:
Proc. Human Factors and Ergonomics Society Annual Mtg, HFES, Vol. 65, No. 1, 2021.

Summary

Individual variation in exoskeleton-augmented gait strategy may arise from differences in cognitive factors, e.g., ability to respond quickly to stimuli or complete tasks under divided attention. Gait strategy is defined as different approaches to achieving gait priorities (e.g., walking without falling) and is observed via changes in gait characteristics like normalized stride length or width. Changes indicate shifting priorities like increasing stability or coordination with an exoskeleton. Relationships between cognitive factors and exoskeleton gait characteristics were assessed. Cognitive factors were quantified using a modified Simon task and a speed achievement task on a self-paced treadmill with and without a secondary go/no-go task. Individuals with faster reaction times and decreased ability to maintain a given speed tended to prioritize coordination with an exoskeleton over gait stability. These correlations indicate relationships between cognitive factors and individual exoskeleton-augmented gait strategy that should be further investigated to understand variation in exoskeleton use.
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Summary

Individual variation in exoskeleton-augmented gait strategy may arise from differences in cognitive factors, e.g., ability to respond quickly to stimuli or complete tasks under divided attention. Gait strategy is defined as different approaches to achieving gait priorities (e.g., walking without falling) and is observed via changes in gait characteristics like...

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Utility of inter-subject transfer learning for wearable-sensor-based joint torque prediction models

Published in:
43rd Annual Intl. Conf. of the IEEE Engineering in Medicine & Biology, 31 October 2021-4 November 2021.

Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single- and multi-subject based parameter transfer on LSTM models trained for "sensor-to-joint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single- and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.
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Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning...

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Metrics for quantifying cognitive factors that may underlie individual variation in exoskeleton use

Published in:
Proc. of the Human Factors and Ergonomics Society Annual Meeting, Vol. 65, No. 1, 2021, pp. 216-20.

Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and gait strategy. Parameters were measured in 15 subjects via two tasks: (1) a modified Simon task and (2) a speed-achievement task with secondary go/no-go cues on a self-paced treadmill. Outcome metrics were assessed for significantly different intra- versus inter-subject variability. Reaction time measures from the modified Simon task, as well two speed-achievement metrics and one gait-strategy characteristic were found to show significant differences in intra- versus inter-subject variability. These results suggest that select cognitive factors may differentiate between individuals and be potential predictors for individual variation during exoskeleton system operation.
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Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and...

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A neural network estimation of ankle torques from electromyography and accelerometry

Summary

Estimations of human joint torques can provide clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Predicting joint torques into the future can also be useful for anticipatory robot control design. In this work, we present a method of mapping joint torque estimates and sequences of torque predictions from motion capture and ground reaction forces to wearable sensor data using several modern types of neural networks. We use dense feedforward, convolutional, neural ordinary differential equation, and long short-term memory neural networks to learn the mapping for ankle plantarflexion and dorsiflexion torque during standing,walking, running, and sprinting, and consider both single-point torque estimation, as well as the prediction of a sequence of future torques. Our results show that long short-term memory neural networks, which consider incoming data sequentially, outperform dense feedforward, neural ordinary differential equation networks, and convolutional neural networks. Predictions of future ankle torques up to 0.4 s ahead also showed strong positive correlations with the actual torques. The proposed method relies on learning from a motion capture dataset, but once the model is built, the method uses wearable sensors that enable torque estimation without the motion capture data.
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Summary

Estimations of human joint torques can provide clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Predicting joint torques into the future can also be useful for anticipatory robot control design. In this work, we present a method of mapping joint torque...

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Geographic source estimation using airborne plant environmental DNA in dust

Summary

Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis of dust components, we developed a pipeline that utilizes the airborne plant environmental DNA (eDNA) in settled dust to estimate geographic origin. Metabarcoding of settled airborne eDNA was used to identify plant species whose geographic distributions were then derived from occurrence records in the USGS Biodiversity in Service of Our Nation (BISON) database. The distributions for all plant species identified in a sample were used to generate a probabilistic estimate of the sample source. With settled dust collected at four U.S. sites over a 15-month period, we demonstrated positive regional geolocation (within 600 km2 of the collection point) with 47.6% (20 of 42) of the samples analyzed. Attribution accuracy and resolution was dependent on the number of plant species identified in a dust sample, which was greatly affected by the season of collection. In dust samples that yielded a minimum of 20 identified plant species, positive regional attribution was achieved with 66.7% (16 of 24 samples). For broader demonstration, citizen-collected dust samples collected from 31 diverse U.S. sites were analyzed, and trace plant eDNA provided relevant regional attribution information on provenance in 32.2% of samples. This showed that analysis of airborne plant eDNA in settled dust can provide an accurate estimate regional provenance within the U.S., and relevant forensic information, for a substantial fraction of samples analyzed.
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Summary

Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis of dust components, we developed a pipeline that utilizes the airborne plant environmental DNA (eDNA) in settled dust to...

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