Self-folding soft-robotic chains with reconfigurable shapes and functionalities.

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.

In Situ Detection of Gastrointestinal Inflammatory Biomarkers Using Electrochemical Gas Sensors.

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.

Mobile Robotic Platform for Contactless Vital Sign Monitoring.

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.

An automated all-in-one system for carbohydrate tracking, glucose monitoring, and insulin delivery.

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.

Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff.

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.