Publications

Refine Results

(Filters Applied) Clear All

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.
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

Development of a real-time hardware-in-the-loop power systems simulation platform to evaluate commercial microgrid controllers

Summary

This report describes the development of a real-time hardware-in-the-loop (HIL) power system simulation platform to evaluate commercial microgrid controllers. The effort resulted in the successful demonstration of HIL simulation technology at a Technical Symposium organized by the Mass Clean Energy Center (CEC) for utility distribution system engineers, project developers, systems integrators, equipment vendors, academia, regulators, City of Boston officials, and Commonwealth officials. Actual microgrid controller hardware was integrated along with actual, commercial genset controller hardware in a particular microgrid configuration, which included dynamic loads, distributed energy resources (DERs), and conventional power sources. The end product provides the ability to quickly and cost-effectively assess the performance of different microgrid controllers as quantified by certain metrics, such as fuel consumption, power flow management precision at the point of common coupling, load-not-served (LNS) while islanded, peak-shaving kWh, and voltage stability. Additional applications include protection system testing and evaluation, distributed generation prime mover controller testing, integration and testing of distribution control systems, behavior testing and studies of DER controls, detailed power systems analysis, communications testing and integration, and implementation and evaluation of smart grid concepts. Microgrids and these additional applications promise to improve the reliability, resiliency, and efficiency of the nation's aging but critical power distribution systems. This achievement was a collaborative effort between MIT Lincoln Laboratory and industry microgrid controller manufacturers. This work was sponsored by the Department of Homeland Security (DHS), Science and Technology Directorate (S&T) and the Department of Energy (DOE) Office of Electricity Delivery and Energy Reliability.
READ LESS

Summary

This report describes the development of a real-time hardware-in-the-loop (HIL) power system simulation platform to evaluate commercial microgrid controllers. The effort resulted in the successful demonstration of HIL simulation technology at a Technical Symposium organized by the Mass Clean Energy Center (CEC) for utility distribution system engineers, project developers, systems...

READ MORE

Showing Results

1-7 of 7