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Gu H, Möckli M, Ehmke C, Kim M, Wieland M, Moser S, Bechinger C, Boehler Q, Nelson BJ. Self-folding soft-robotic chains with reconfigurable shapes and functionalities. Nat Commun 2023; 14:1263. [PMID: 36882398 PMCID: PMC9992713 DOI: 10.1038/s41467-023-36819-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 02/17/2023] [Indexed: 03/09/2023] Open
Abstract
Magnetic continuum soft robots can actively steer their tip under an external magnetic field, enabling them to effectively navigate in complex in vivo environments and perform minimally invasive interventions. However, the geometries and functionalities of these robotic tools are limited by the inner diameter of the supporting catheter as well as the natural orifices and access ports of the human body. Here, we present a class of magnetic soft-robotic chains (MaSoChains) that can self-fold into large assemblies with stable configurations using a combination of elastic and magnetic energies. By pushing and pulling the MaSoChain relative to its catheter sheath, repeated assembly and disassembly with programmable shapes and functions are achieved. MaSoChains are compatible with state-of-the-art magnetic navigation technologies and provide many desirable features and functions that are difficult to realize through existing surgical tools. This strategy can be further customized and implemented for a wide spectrum of tools for minimally invasive interventions.
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Affiliation(s)
- Hongri Gu
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland. .,Department of Physics, University of Konstanz, Konstanz, Germany.
| | - Marino Möckli
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Claas Ehmke
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Minsoo Kim
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
| | - Matthias Wieland
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Simon Moser
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | | | - Quentin Boehler
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
| | - Bradley J Nelson
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
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Chai PR, Rupp P, Huang HW, Chen J, Vaz C, Sinha A, Ehmke C, Thomas A, Dadabhoy F, Liang JY, Landman AB, Player G, Slattery K, Traverso G. Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff. BMJ Open 2022; 12:e062707. [PMID: 36600328 PMCID: PMC9742841 DOI: 10.1136/bmjopen-2022-062707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital. DESIGN Single-site, observational cohort study. SETTING An urban, academic hospital in Boston, Massachusetts, USA. PARTICIPANTS We enrolled adult hospital staff entering the hospital at a key ingress point. INTERVENTIONS Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence. OUTCOME MEASURES Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm. RESULTS One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.
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Affiliation(s)
- Peter R Chai
- Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
- The Fenway Institute, Boston, MA, USA
| | - Phillip Rupp
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
| | - Hen-Wei Huang
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
- Medicine/Division of Gastroenterology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jack Chen
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
- Medicine/Division of Gastroenterology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Clint Vaz
- Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Anjali Sinha
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
| | - Claas Ehmke
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
| | - Akhil Thomas
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
| | - Farah Dadabhoy
- Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jia Y Liang
- Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
- Medicine/Division of Gastroenterology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Adam B Landman
- Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Digital Innovation Hub, Brigham and Women's Hospital, Boston, MA, USA
| | - George Player
- Facilities, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Kevin Slattery
- Security, Safety and Parking, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Giovanni Traverso
- Medicine/Division of Gastroenterology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Huang HW, Ehmke C, Steiger C, Ballinger I, Jimenez M, Phan N, Sun H, Ishida K, Kuosmanen J, Jenkins J, Korzenik J, Hayward A, Traverso G. In Situ Detection of Gastrointestinal Inflammatory Biomarkers Using Electrochemical Gas Sensors. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:2491-2494. [PMID: 36085797 DOI: 10.1109/embc48229.2022.9871468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
More than two decades ago it was discovered that nitric oxide (NO) concentrations in gas aspirated during colonoscopy were more than 100 times higher in patients diagnosed with Ulcerative Colitis (UC) than controls. While this provides a diagnostic opportunity, it has not been possible to perform in situ detection of NO via a non-invasive manner. This work presents the feasibility of in situ detection of NO by means of a capsule-like electrochemical gas sensor. Our in vivo results in a large animal model of intestinal inflammation show that NO can be directly detected at the site of inflammation and that it quickly dissipates to surrounding tissues, demonstrating the importance of in situ detection.
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Huang HW, Chen J, Chai PR, Ehmke C, Rupp P, Dadabhoy FZ, Feng A, Li C, Thomas AJ, da Silva M, Boyer EW, Traverso G. Mobile Robotic Platform for Contactless Vital Sign Monitoring. Cyborg and Bionic Systems 2022; 2022. [DOI: 10.34133/2022/9780497] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The COVID-19 pandemic has accelerated methods to facilitate contactless evaluation of patients in hospital settings. By minimizing in-person contact with individuals who may have COVID-19, healthcare workers can prevent disease transmission and conserve personal protective equipment. Obtaining vital signs is a ubiquitous task that is commonly done in person by healthcare workers. To eliminate the need for in-person contact for vital sign measurement in the hospital setting, we developed Dr. Spot, a mobile quadruped robotic system. The system includes IR and RGB cameras for vital sign monitoring and a tablet computer for face-to-face medical interviewing. Dr. Spot is teleoperated by trained clinical staff to simultaneously measure the skin temperature, respiratory rate, and heart rate while maintaining social distancing from patients and without removing their mask. To enable accurate, contactless measurements on a mobile system without a static black body as reference, we propose novel methods for skin temperature compensation and respiratory rate measurement at various distances between the subject and the cameras, up to 5 m. Without compensation, the skin temperature MAE is 1.3°C. Using the proposed compensation method, the skin temperature MAE is reduced to 0.3°C. The respiratory rate method can provide continuous monitoring with a MAE of 1.6 BPM in 30 s or rapid screening with a MAE of 2.1 BPM in 10 s. For the heart rate estimation, our system is able to achieve a MAE less than 8 BPM in 10 s measured in arbitrary indoor light conditions at any distance below 2 m.
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Affiliation(s)
- Hen-Wei Huang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, USA
- The Koch Institute of Integrated Cancer Research, Massachusetts Institute of Technology, USA
- Division of Gastroenterology, Brigham and Women’s Hospital, Harvard Medical School, USA
| | - Jack Chen
- Division of Gastroenterology, Brigham and Women’s Hospital, Harvard Medical School, USA
- Department of Engineering Science, University of Toronto, Canada
| | - Peter R. Chai
- The Koch Institute of Integrated Cancer Research, Massachusetts Institute of Technology, USA
- Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School, USA
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, USA
- The Fenway Institute, USA
| | - Claas Ehmke
- The Koch Institute of Integrated Cancer Research, Massachusetts Institute of Technology, USA
| | - Philipp Rupp
- Division of Gastroenterology, Brigham and Women’s Hospital, Harvard Medical School, USA
| | - Farah Z. Dadabhoy
- Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School, USA
| | - Annie Feng
- The Koch Institute of Integrated Cancer Research, Massachusetts Institute of Technology, USA
| | - Canchen Li
- The Koch Institute of Integrated Cancer Research, Massachusetts Institute of Technology, USA
| | - Akhil J. Thomas
- The Koch Institute of Integrated Cancer Research, Massachusetts Institute of Technology, USA
| | | | - Edward W. Boyer
- Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School, USA
- The Fenway Institute, USA
| | - Giovanni Traverso
- Department of Mechanical Engineering, Massachusetts Institute of Technology, USA
- Division of Gastroenterology, Brigham and Women’s Hospital, Harvard Medical School, USA
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Huang HW, You SS, Di Tizio L, Li C, Raftery E, Ehmke C, Steiger C, Li J, Wentworth A, Ballinger I, Gwynne D, Nan K, Liang JY, Li J, Byrne JD, Collins J, Tamang S, Ishida K, Halperin F, Traverso G. An automated all-in-one system for carbohydrate tracking, glucose monitoring, and insulin delivery. J Control Release 2022; 343:31-42. [PMID: 34998917 DOI: 10.1016/j.jconrel.2022.01.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 12/21/2021] [Accepted: 01/01/2022] [Indexed: 12/12/2022]
Abstract
Glycemic control through titration of insulin dosing remains the mainstay of diabetes mellitus treatment. Insulin therapy is generally divided into dosing with long- and short-acting insulin, where long-acting insulin provides basal coverage and short-acting insulin supports glycemic excursions associated with eating. The dosing of short-acting insulin often involves several steps for the user including blood glucose measurement and integration of potential carbohydrate loads to inform safe and appropriate dosing. The significant burden placed on the user for blood glucose measurement and effective carbohydrate counting can manifest in substantial effects on adherence. Through the application of computer vision, we have developed a smartphone-based system that is able to detect the carbohydrate load of food by simply taking a single image of the food and converting that information into a required insulin dose by incorporating a blood glucose measurement. Moreover, we report the development of comprehensive all-in-one insulin delivery systems that streamline all operations that peripheral devices require for safe insulin administration, which in turn significantly reduces the complexity and time required for titration of insulin. The development of an autonomous system that supports maximum ease and accuracy of insulin dosing will transform our ability to more effectively support patients with diabetes.
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Affiliation(s)
- Hen-Wei Huang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Siheng Sean You
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Luca Di Tizio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Canchen Li
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Erin Raftery
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Claas Ehmke
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christoph Steiger
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Junwei Li
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Adam Wentworth
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ian Ballinger
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Declan Gwynne
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kewang Nan
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jia Y Liang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jason Li
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James D Byrne
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joy Collins
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Siddartha Tamang
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Keiko Ishida
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Florencia Halperin
- Division of Endocrinology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Giovanni Traverso
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Kabzan J, Valls MI, Reijgwart VJF, Hendrikx HFC, Ehmke C, Prajapat M, Bühler A, Gosala N, Gupta M, Sivanesan R, Dhall A, Chisari E, Karnchanachari N, Brits S, Dangel M, Sa I, Dubé R, Gawel A, Pfeiffer M, Liniger A, Lygeros J, Siegwart R. AMZ Driverless: The full autonomous racing system. J FIELD ROBOT 2020. [DOI: 10.1002/rob.21977] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Inkyu Sa
- Autonomous Systems Lab (ASL)ETH Zürich Zurich Switzerland
| | - Renaud Dubé
- Autonomous Systems Lab (ASL)ETH Zürich Zurich Switzerland
| | - Abel Gawel
- Autonomous Systems Lab (ASL)ETH Zürich Zurich Switzerland
| | - Mark Pfeiffer
- Autonomous Systems Lab (ASL)ETH Zürich Zurich Switzerland
| | | | - John Lygeros
- Automatic Control Laboratory (IfA)ETH Zürich Zurich Switzerland
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