INTRODUCTION:The U.S. Space Force was stood up on December 20, 2019 as an independent branch under the Air Force consisting of about 16,000 active duty and civilian personnel focused singularly on space. In addition to the Space Force, the plans by NASA and private industry for exploration-class long-duration missions to the moon, near-earth asteroids, and Mars makes semi-independent medical capability in space a priority. Current practice for space-based medicine is limited and relies on a "life-raft" scenario for emergencies. Discussions by working groups on military space-based medicine include placing a Role III equivalent facility in a lunar surface station. Surgical capability is a key requirement for that facility.MATERIALS AND METHODS:To prepare for the eventuality of surgery in space, it is necessary to develop low-mass, low power, mini-surgical robots, which could serve as a celestial replacement for existing terrestrial robots. The current study focused on developing semi-autonomous capability in surgical robotics, specifically related to task automation. Two categories for end-effector tissue interaction were developed: Visual feedback from the robot to detect tissue contact, and motor current waveform measurements to detect contact force.RESULTS:Using a pixel-to-pixel deep neural network to train, we were able to achieve an accuracy of nearly 90% for contact/no-contact detection. Large torques were predicted well by a trained long short-term memory recursive network, but the technique did not predict small torques well.CONCLUSION:Surgical capability on long-duration missions will require human/machine teaming with semi-autonomous surgical robots. Our existing small, lightweight, low-power miniature robots perform multiple essential tasks in one design including hemostasis, fluid management, suturing for traumatic wounds, and are fully insertable for internal surgical procedures. To prepare for the inevitable eventuality of an emergency surgery in space, it is essential that automated surgical robot capabilities be developed.

Current techniques for diagnosing skin cancer lack specificity and sensitivity, resulting in unnecessary biopsies and missed diagnoses. Automating tissue palpation and morphology quantification will result in a repeatable, objective process. LesionAir is a low-cost skin cancer diagnostic tool that measures the full-field compliance of tissue by applying a vacuum force and measuring the precise deflection using structured light three-dimensional (3D) reconstruction. The technology was tested in a benchtop setting on phantom skin and in a small clinical study. LesionAir has been shown to measure deflection with a 0.085 mm root-mean-square (RMS) error and measured the stiffness of phantom tissue to within 20% of finite element analysis (FEA) predictions. After biopsy and analysis, a dermatopathologist confirmed the diagnosis of skin cancer in tissue that LesionAir identified as noticeably stiffer and the regions of this stiffer tissue aligned with the bounds of the lesion. A longitudinal, full-scale study is required to determine the clinical efficacy of the device. This technology shows initial promise as a low-cost tool that could rapidly identify and diagnose skin cancer.

Maintaining the health and well‐being of animals is critical to the efficiency and profitability of livestock operations. However, it can be difficult to monitor the health of animals in large group‐housed settings without the assistance of technology. This study presents a system that uses depth images to continuously track individual pigs in a group‐housed environment. It is an alternative to traditional manual observation used by both researchers and producers for the analysis of animal activities and behaviours. The tracking method used by the system exploits the consistent shape and fixed number of the targets in the environment by applying expectation maximisation as a policy for fitting an ellipsoid to each target. Results demonstrate that the system can maintain the correct positions and orientations of 15 group‐housed pigs for an average of 19.7 min between failure events.

Minimally-invasive laparoscopic procedures have proven efficacy for a wide range of surgical procedures, but have notable shortcomings, including limited instrument motion and reduced dexterity. Endoscopic robots, like the intuitive surgical da Vinci system, have become an effective tool for many types of surgeries; however, these tools still have fundamental limitations with manipulator access, which reduces their effectiveness for many surgical procedures, like colectomy, cholecystectomy, and gynecologic oncology. Laparo-endoscopic single-site (LESS) robots operate in vivo, and overcome many of these limitations. Here, a four-degrees of freedom (DOF) surgical robot is presented as a tool to enable refinement of the LESS platform as a surgical tool, while also looking forward to applications in telesurgery and haptic feedback.

Shifting demographics in the U.S. has created an urgent need to reform the policies, practices, and technology associated with delivering healthcare to geriatric populations. Automated monitoring systems can improve the quality of life while reducing healthcare costs for individuals aging in place. For these systems to be successful, both activity detection and localization are important, but most existing research focuses on only one of these technologies and systems that do collect both data treat these data sources separately. Here, we present SLAD {Simultaneous Localization and Activity Detection a novel framework for simultaneously processing data collected from localization and activity classification systems. Using a hidden Markov model and machine learning techniques, SLAD fuses these two sources of data in realtime using a probabilistic likelihood framework, which allows activity data to refine localization, and vice-versa. To evaluate the system, a wireless sensor network was deployed to collect RSSI data and IMU data concurrently from a wrist-worn watch; the RSSI data was processed using a radial basis function neural network localization algorithm, and the resulting position likelihoods were combined with the likelihoods from an IMU acitivty classification algorithm. In an experiment conducted in an indoor office environment, the proposed method produces 97% localization accuracy and 85% activity classification.

The Texas Instruments eZ430-Chronos development kit is a popular wireless platform for researchers, commercial developers, and hobbyists. Built into the form factor of a standard wrist watch, the Chronos is ideal for wireless body area networks (WBANs), indoor localization, and activity detection. Many of these applications requires knowledge of the RF performance of the platform, which has never been characterized by Texas Instruments nor any third parties. Here, we provide experimental data characterizing the RF performance of the eZ430-Chronos watch in various configurations, including when worn by a person while sitting, standing, and reaching. The RF performance of the Chronos watch is compared with an Angelos Ambient wireless mote that uses a common type of ceramic chip antenna. Our results indicate the Chronos has excellent omnidirectionality and reasonably good RF gain in all tested configurations.

As a first step toward building a smart home behavioral monitoring system capable of classifying a wide variety of human behavior, a wireless sensor network (WSN) system is presented for RSSI localization. The low-cost, non-intrusive system uses a smart watch worn by the user to broadcast data to the WSN, where the strength of the radio signal is evaluated at each WSN node to localize the user. A method is presented that uses simultaneous localization and mapping (SLAM) for system calibration, providing automated fingerprinting associating the radio signal strength patterns to the user's location within the living space. To improve the accuracy of localization, a novel refinement technique is introduced that takes into account typical movement patterns of people within their homes. Experimental results demonstrate that the system is capable of providing accurate localization results in a typical living space.

Many wireless sensor network applications require a gateway device to interface with services running on the Internet. Because of the software complexity involved in this device, it is often realized using a real-time operating system running on an application processor. Most systems burden the user with developing the protocol handling and device configuration and management inside the application. In this paper, we present the Angelos Gateway - a turnkey, low-cost, Linux-powered WSN gateway that provides a socket-based environment for rapid network-enabled application development. Experimental results demonstrate that the proposed device is capable of high-throughput packet I/O confirming the efficacy of the proposed implementation.

Respiratory training is a critical component of many rehabilitation plans, including those of stroke patients. Many current respiratory training techniques lack efficient methods for quantifying progress and updating testing parameters. A previously-developed microcontroller-based device, designed in conjunction with clinicians at the Institute for Rehabilitation Science and Engineering at Madonna Rehabilitation Hospital, has demonstrated promising results. Here, a prototype of a revised device that is network connected and remoatly sends trial information is presented. The proposed device demonstrates enhanced functionality, while being smaller and using less power than the original prototype.

Most Wireless Sensor Network platforms - such as the Mica, Iris, and Telos B families of motes - use low-power 8-bit microprocessors which have limited memory and processing capabilities, thus requiring researchers to implement communication protocols and data processing routines using low-level programming practices that are tedious and cumbersome. Rich features available in modern desktop operating systems - such as threads, memory management, and exception-handling - are largely absent. The Microsoft .NET Micro Framework implements a scaled-back .NET framework suitable for development on low-cost, low-power wireless sensors, while providing developers a rapid software development environment for prototyping embedded applications. Here, this technology is explored by comparing performance characteristics with those of traditional 8-bit platforms, as well as Sun SPOT, a popular platform that also uses a managed-language runtime. The .NET Micro Framework platform was found to offer researchers the most flexibility in terms of hardware and software prototyping.

Background Accurate real-time 3D models of the operating field have the potential to enable augmented reality for endoscopic surgery. A new system is proposed to create real-time 3D models of the operating field that uses a custom miniaturized stereoscopic video camera attached to a laparoscope and an image-based reconstruction algorithm implemented on a graphics processing unit (GPU).Methods The proposed system was evaluated in a porcine model that approximates the viewing conditions of in vivo surgery. To assess the quality of the models, a synthetic view of the operating field was produced by overlaying a color image on the reconstructed 3D model, and an image rendered from the 3D model was compared with a 2D image captured from the same view.Results Experiments conducted with an object of known geometry demonstrate that the system produces 3D models accurate to within 1.5 mm.Conclusions The ability to produce accurate real-time 3D models of the operating field is a significant advancement toward augmented reality in minimally invasive surgery. An imaging system with this capability will potentially transform surgery by helping novice and expert surgeons alike to delineate variance in internal anatomy accurately.

Inexpensive, high-throughput, low maintenance systems for precise temporal and spatial measurement of mouse home cage behavior (including movement, feeding, and drinking) are required to evaluate products from large scale pharmaceutical design and genetic lesion programs. These measurements are also required to interpret results from more focused behavioral assays. We describe the design and validation of a highly-scalable, reliable mouse home cage behavioral monitoring system modeled on a previously described, one-of-a-kind system [1]. Mouse position was determined by solving static equilibrium equations describing the force and torques acting on the system strain gauges; feeding events were detected by a photobeam across the food hopper, and drinking events were detected by a capacitive lick sensor. Validation studies show excellent agreement between mouse position and drinking events measured by the system compared with video-based observation - a gold standard in neuroscience.

Robotic surgical platforms require vision feedback systems, which often consist of low-resolution, expensive, single-imager analog cameras. These systems are retooled for 3D display by simply doubling the cameras and outboard control units. Here, a fully-integrated digital stereoscopic video camera employing high-definition sensors and a class-compliant USB video interface is presented. This system can be used with low-cost PC hardware and consumer-level 3D displays for tele-medical surgical applications including military medical support, disaster relief, and space exploration.