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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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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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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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Health-informed policy gradients for multi-agent reinforcement learning

Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on simple particle environments that have elements of system health, risk-taking, semi-expendable agents, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.
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Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then...

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Ablation analysis to select wearable sensors for classifying standing, walking, and running

Summary

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.
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Summary

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable...

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Ankle torque estimation during locomotion from surface electromyography and accelerometry

Published in:
2020 8th IEEE Intl. Conf. on Biomedical Robotics and Biomechatronics, BioRob, 29 November - 1 December 2020.

Summary

Estimations of human joint torques can provide quantitative, clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Standard methods for estimating joint torques are limited to laboratory or clinical settings since they require expensive equipment to measure joint kinematics and ground reaction forces. Wearable sensor data combined with neural networks may offer a less expensive and obtrusive estimation method.We present a method of mapping joint torque estimates obtained from motion capture and ground reaction forces to wearable sensor data. We use several different neural networks to learn the torque mapping for the ankle joints during standing, walking, running, and sprinting. Our results show that neural networks that consider time (recurrent and long short-term memory networks) outperform feedforward network architectures, producing results in the range of 0.005-0.008 N m/kg mean squared error (MSE) when compared to the inverse dynamics model on which it was trained. As a point of reference, the typical measurement errors from inverse dynamics models are in the range of 0.0004-0.0064 N m/kg MSE. Errors tended to increase with locomotion speed, with the highest errors during sprinting and the lowest during standing or walking. Future work may investigate model generalizability across sensor placements, subjects, locomotion variants, and usage duration. 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. These methods also have potential uses for the design and testing of wearable robotic systems outside of a laboratory environment.
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Summary

Estimations of human joint torques can provide quantitative, clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Standard methods for estimating joint torques are limited to laboratory or clinical settings since they require expensive equipment to measure joint kinematics and ground reaction...

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Design, simulation, and fabrication of three-dimensional microsystem components using grayscale photolithography

Summary

Grayscale lithography is a widely known but underutilized microfabrication technique for creating three-dimensional (3-D) microstructures in photoresist. One of the hurdles for its widespread use is that developing the grayscale photolithography masks can be time-consuming and costly since it often requires an iterative process, especially for complex geometries. We discuss the use of PROLITH, a lithography simulation tool, to predict 3-D photoresist profiles from grayscale mask designs. Several examples of optical microsystems and microelectromechanical systems where PROLITH was used to validate the mask design prior to implementation in the microfabrication process are presented. In all examples, PROLITH was able to accurately and quantitatively predict resist profiles, which reduced both design time and the number of trial photomasks, effectively reducing the cost of component fabrication.
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Summary

Grayscale lithography is a widely known but underutilized microfabrication technique for creating three-dimensional (3-D) microstructures in photoresist. One of the hurdles for its widespread use is that developing the grayscale photolithography masks can be time-consuming and costly since it often requires an iterative process, especially for complex geometries. We discuss...

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A Framework for Evaluating Electric Power Grid Improvements in Puerto Rico(2.58 MB)

Summary

This report is motivated by the recognition that serving highly distributed electric power load in Puerto Rico during extreme events requires innovative methods. To do this, we must determine the type and locations of the most critical equipment, innovative methods, and software for operating the electrical system most effectively. It is well recognized that the existing system needs to be both hardened and further enhanced by deploying Distributed Energy Resources (DERs), solar photovoltaics (PV) in particular, and local reconfigurable microgrids to manage these newly deployed DERs. While deployment of microgrids and DERs has been advocated by many, there is little fundamental understanding how to operate Puerto Rico’s electrical system in a way that effectively uses DERs during both normal operations and grid failures. Utility companies’ traditional reliability requirements and operational risk management practices rely on excessive amounts of centralized reserve generation to anticipate failures, which increases the cost of normal operations and nullifies the potential of DERs to meet loads during grid failures. At present, no electric power utility has a ready-to-use framework that overcomes these limitations. This report seeks to fill this void.
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Summary

This report is motivated by the recognition that serving highly distributed electric power load in Puerto Rico during extreme events requires innovative methods. To do this, we must determine the type and locations of the most critical equipment, innovative methods, and software for operating the electrical system most effectively. It...

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A framework for evaluating electric power grid improvements in Puerto Rico

Summary

This report is motivated by the recognition that serving highly distributed electric power load in Puerto Rico during extreme events requires innovative methods. To do this, we must determine the type and locations of the most critical equipment, innovative methods, and software for operating the electrical system most effectively. It is well recognized that the existing system needs to be both hardened and further enhanced by deploying Distributed Energy Resources (DERs), solar photovoltaics (PV) in particular, and local reconfigurable microgrids to manage these newly deployed DERs. While deployment of microgrids and DERs has been advocated by many, there is little fundamental understanding how to operate Puerto Rico's electrical system in a way that effectively uses DERs during both normal operations and grid failures. Utility companies' traditional reliability requirements and operational risk management practices rely on excessive amounts of centralized reserve generation to anticipate failures, which increases the cost of normal operations and nullifies the potential of DERs to meet loads during grid failures. At present, no electric power utility has a ready-to-use framework that overcomes these limitations. This report seeks to fill this void.
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Summary

This report is motivated by the recognition that serving highly distributed electric power load in Puerto Rico during extreme events requires innovative methods. To do this, we must determine the type and locations of the most critical equipment, innovative methods, and software for operating the electrical system most effectively. It...

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Modular Aid and Power Pallet (MAPP): FY18 Energy Technical Investment Program

Published in:
MIT Lincoln Laboratory Report TIP-93

Summary

Electric power is a critical element of rapid response disaster relief efforts. Generators currently used have high failure rates and require fuel supply chains, and standardized renewable power systems are not yet available. In addition, none of these systems are designed for easy adaptation or repairs in the field to accommodate changing power needs as the relief effort progresses. To address this, the Modular Aid and Power Pallet, or MAPP, was designed to be a temporary, scalable, self-contained, user-focused power system. While some commercial systems are advertised for disaster relief systems, most are limited by mobility, custom battery assemblies (with challenges for air transport, ground mobility, or both), and the ability to power AC loads. While the first year system focused on an open architecture design with distributed DC units that could be combined to serve larger AC loads, the second year succeeded in minimizing or eliminating batteries while providing AC power for both the distributed and centralized systems. Therefore, individual modules can be distributed to power small AC loads such as laptop charging, or combined in series for larger loads such as water purification. Each module is powered by a small photovoltaic (PV) array connected to a prototype off-grid Enphase microinverter that can be used with or without energy storage. In addition, an output box for larger loads is included to provide a ground fault interrupt, under/over voltage relay, and the ability to change the system grounding to fit the needs of a more complicated system. The second year MAPP effort was divided into two phases: Phase 1 from October 2017 to March 20181 focused on refining requirements and vendor selection, and Phase 2 from March 2018 to October 20182 focusing on power electronics, working with the new Enphase microinverter, and ruggedizing the system. The end result is the Phase 2 effort has been designed, tested, and proven to form a robust AC power source that is flexible and configurable by the end user. Our testing has shown that operators can easily set up the system and adapt it to changing needs in the field.
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Summary

Electric power is a critical element of rapid response disaster relief efforts. Generators currently used have high failure rates and require fuel supply chains, and standardized renewable power systems are not yet available. In addition, none of these systems are designed for easy adaptation or repairs in the field to...

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