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Sensorizing a Compression Sleeve for Continuous Pressure Monitoring and Lymphedema Treatment Using Pneumatic or Resistive Sensors

Sensorizing a Compression Sleeve for Continuous Pressure Mon...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Joseph DelPreto Cheryl L. Brunelle Alphonse G. Taghian Daniela Rus MIT Distributed Robotics Lab Cambridge MA USA Massachusetts General Hospital Boston MA USA

Smart soft wearable devices have great potential to change how technology is integrated into daily life. A particularly impactful and growing application is continuous medical monitoring; being able to stream physiological and behavioral information creates personalized datasets that can lead to more tailored treatments, diagnoses, and research. An area that can greatly benefit from these developments is lymphedema management, which aims to prevent a potentially irreversible swelling of limbs due to causes such as breast cancer surgeries. Compression sleeves are the state of the art for treatment, but many open questions remain regarding effective pressure and usage prescriptions. To help address these, this work presents a soft pressure sensor, a way to integrate it into wearable devices, and sensorized compression sleeves that continuously monitor pressure and usage. There are significant challenges to developing sensors for high-pressure applications on the human body, including operating between soft compliant interfaces, being safe and unobtrusive, and reducing calibration for new users. This work compares two sensing approaches for wearable applications: a custom pouch-based pneumatic sensor, and a commercially available resistive sensor. Experiments systematically explore design considerations including sensitivity to ambient temperature and pressure, characterize sensor response curves, and evaluate expected accuracies and required calibrations. Sensors are then integrated into compression sleeves and worn for over 115 hours spanning 10 days.

关键词： Temperature sensors Temperature measurement Wearable computers Sensor phenomena and characterization Robot sensing systems Sensors Calibration

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ODANet: Online Deep Appearance Network for Identity-Consistent Multi-person Tracking 25th

ODANet: Online Deep Appearance Network for Identity-Consiste...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Delorme, Guillaume Ban, Yutong Sarrazin, Guillaume Alameda-Pineda, Xavier Inria Univ. Grenoble-Alpes LJK CNRS Paris France MIT CSAIL Distributed Robotics Lab Cambridge United States

ISBN: (纸本)9783030687793

The analysis of affective states through time in multi-person scenarii is very challenging, because it requires to consistently track all persons over time. This requires a robust visual appearance model capable of re-identifying people already tracked in the past, as well as spotting newcomers. In real-world applications, the appearance of the persons to be tracked is unknown in advance, and therefore one must devise methods that are both discriminative and flexible. Previous work in the literature proposed different tracking methods with fixed appearance models. These models allowed, up to a certain extend, to discriminate between appearance samples of two different people. We propose online deep appearance network (ODANet), a method able to track people, and simultaneously update the appearance model with the newly gathered annotation-less images. Since this task is specially relevant for autonomous systems, we also describe a platform-independent robotic implementation of ODANet. Our experiments show the superiority of the proposed method with respect to the state of the art, and demonstrate the ability of ODANet to adapt to sudden changes in appearance, to integrate new appearances in the tracking system and to provide more identity-consistent tracks. © 2021, Springer Nature Switzerland AG.

关键词： Computers

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TelePulse: Enhancing the Teleoperation Experience through Biomechanical Simulation-Based Electrical Muscle Stimulation in Virtual Reality 25

TelePulse: Enhancing the Teleoperation Experience through Bi...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Seokhyun Hwang Seongjun Kang Jeongseok Oh Jeongju Park Semoo Shin Yiyue Luo Joseph DelPreto Sangbeom Lee Kyoobin Lee Wojciech Matusik Daniela Rus SeungJun Kim Information School University of Washington Seattle Washington USA and Human-Centered Intelligent Systems Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea Human-Centered Intelligent Systems Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea ECE University of Washington Seattle Washington USA Distributed Robotics Lab Massachusetts Institute of Technology (MIT) Cambridge Massachusetts USA Artificial Intelligence Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea CSAIL MIT Cambridge Massachusetts USA Distributed Robotics Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

ISBN: (纸本)9798400713941

This paper introduces TelePulse, a system integrating biomechanical simulation with electrical muscle stimulation (EMS) to provide precise haptic feedback for robot teleoperation tasks in virtual reality (VR). TelePulse has two components: a physical simulation part that calculates joint torques based on real-time force data from remote manipulators, and an electrical stimulation part that converts these torques into muscle stimulation. Two experiments were conducted to evaluate the system. The first experiment assessed the accuracy of EMS generated through biomechanical simulations by comparing it with electromyography (EMG) data during force-directed tasks, while the second experiment evaluated the impact of TelePulse on teleoperation performance during sanding and drilling tasks. The results suggest that TelePulse provided more accurate stimulation across all arm muscles, thereby enhancing task performance and user experience in the teleoperation environment. In this paper, we discuss the effect of TelePulse on teleoperation, its limitations, and areas for future improvement. © 2025 Copyright held by the owner/author(s).

关键词： Teleoperation Virtual Reality Haptic Electrical Muscle Stimulation Biomechanics Simulation Wearable Device

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Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation

Helping Robots Learn: A Human-Robot Master-Apprentice Model ...

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2020 IEEE International Conference on robotics and Automation, ICRA 2020

作者： Delpreto, Joseph Lipton, Jeffrey I. Sanneman, Lindsay Fay, Aidan J. Fourie, Christopher Choi, Changhyun Rus, Daniela Mit Distributed Robotics Lab CambridgeMA02139 United States

ISBN: (纸本)9781728173955

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots;the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach;100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves. © 2020 IEEE.

关键词： Demonstrations

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Plug-and-Play Gesture Control Using Muscle and Motion Sensors

Plug-and-Play Gesture Control Using Muscle and Motion Sensor...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Joseph DelPreto Daniela Rus MIT Distributed Robotics Lab Cambridge Massachusetts

As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effective in realistic scenarios, such interfaces should not require significant sensing infrastructure or per-user setup time. The presented work takes a step towards these goals by using wearable muscle and motion sensors to detect gestures without dedicated calibration or training procedures. An algorithm is presented for clustering unlabeled streaming data in real time, and it is applied to adaptively thresholding muscle and motion signals acquired via electromyography (EMG) and an inertial measurement unit (IMU). This enables plug-and-play online detection of arm stiffening, fist clenching, rotation gestures, and forearm activation. It also augments a neural network pipeline, trained only on strategically chosen training data from previous users, to detect left, right, up, and down gestures. Together, these pipelines offer a plug-and-play gesture vocabulary suitable for remotely controlling a robot. Experiments with 6 subjects evaluate classifier performance and interface efficacy. Classifiers correctly identified 97.6% of 1,200 cued gestures, and a drone correctly responded to 81.6% of 1,535 unstructured gestures as subjects remotely controlled it through target hoops during 119 minutes of total flight time. CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI); Gestural input; • Computer systems organization →robotics; • Computing methodologies→Machine learning. ACM Reference Format: Joseph DelPreto and Daniela Rus. 2020. Plug-and-Play Gesture Control Using Muscle and Motion Sensors. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, cambridge, United Kingdom. ACM, New York, NY, USA,

关键词： Human computer interaction Vocabulary Pipelines Neural networks Human-robot interaction Training data Muscles

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Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation

Helping Robots Learn: A Human-Robot Master-Apprentice Model ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joseph DelPreto Jeffrey I. Lipton Lindsay Sanneman Aidan J. Fay Christopher Fourie Changhyun Choi Daniela Rus MIT Distributed Robotics Lab Cambridge MA MIT Interactive Robotics Group Cambridge MA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves.

关键词： Robots Grasping Task analysis Three-dimensional displays Solid modeling Virtual reality Pipelines

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Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection 14th

Plug-and-Play Supervisory Control Using Muscle and Brain Sig...

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14th robotics: Science and Systems, RSS 2018

作者： Del Preto, Joseph Salazar-Gomez, Andres F. Gil, Stephanie Hasani, Ramin M. Guenther, Frank H. Rus, Daniela MIT Distributed Robotics Lab CambridgeMA02139 United States Vienna University of Technology Cyber-Physical Systems Group Austria Boston University Guenther Lab BostonMA02215 United States

ISBN: (纸本)9780992374747

Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rolling-window classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor’s brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows "plug-and-play" operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task. © 2018, mit Press Journals. All rights reserved.

关键词： Safety engineering

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Correcting robot mistakes in real time using EEG signals

Correcting robot mistakes in real time using EEG signals

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Salazar-Gomez, Andres F. Delpreto, Joseph Gil, Stephanie Guenther, Frank H. Rus, Daniela Boston University Guenther Lab BostonMA02215 United States MIT Distributed Robotics Lab CambridgeMA02139 United States

ISBN: (纸本)9781509046331

Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction. © 2017 IEEE.

关键词： Feedback

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Correcting robot mistakes in real time using EEG signals

Correcting robot mistakes in real time using EEG signals

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IEEE International Conference on robotics and Automation (ICRA)

作者： Andres F. Salazar-Gomez Joseph DelPreto Stephanie Gil Frank H. Guenther Daniela Rus Boston University Guenther Lab Boston MA MIT Distributed Robotics Lab Cambridge MA

关键词： Electroencephalography Real-time systems Collaboration Training Visualization Mobile robots

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