We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the most likely sensor model for a time series of unknown measurement data, given an extendable catalog of predefined sensor models. Sensor model definitions may require states for rigid-body calibrations and dedicated reference frames to replicate a measurement based on the robot's localization state. A health metric is introduced, which verifies the outcome of the selection process in order to detect false positives and facilitate reliable decision-making. In a second stage, an initial guess for identified calibration states is generated, and the necessity of sensor world reference frames is evaluated. The identified sensor model with its parameter information is then used to parameterize and initialize a state estimation application, thus ensuring a more accurate and robust integration of new sensor elements. This method is helpful for inexperienced users who want to identify the source and type of a measurement, sensor calibrations, or sensor reference frames. It will also be important in the field of modular multi-agent scenarios and modularized robotic platforms that are augmented by sensor modalities during runtime. Overall, this work aims to provide a simplified integration of sensor modalities to downstream applications and circumvent common pitfalls in the usage and development of localization approaches.

In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency, a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model to ensure precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.

Artificial Intelligence (AI) and Machine Learning (ML) are two rapidly growing fields in technology, and Python has become the go-to programming language for both. Python has a vast array of libraries and tools available for AI and ML development, making it an ideal language for beginners to get started with these fields. In this article, we will discuss the basics of using Python for machine learning and provide some code samples to help you get started. Machine learning is a subset of AI that involves training machines to learn from data and make predictions or decisions. It is a form of statistical analysis that involves the use of algorithms to find patterns in data and use those patterns to make predictions.

The increasing complexity of patient care, difficulties with time management, and managing administrative tasks while complying with regulations are a few overarching difficulties that go hand-in-hand with the job. Fortunately, big data and smart technology are helping hospitalists overcome these issues. Here are some fascinating ways data and smart technology are helping hospitalists. Medical billing is notoriously erroneous. Some estimates propose that upward of 80% of medical bills have errors.

Prior to the coronavirus pandemic, the use of digital technology in healthcare was on a steady rise; however, the pandemic has spurred rapid development of digital health technology as well as rapid adoption and utilization of that technology in the industry. Digital health holds the promise of increased accessibility to high-quality, patient-centered care that can also increase patient engagement and reduce costs. However, the full realization of this promise may be threatened by policy and regulation that is failing to keep pace with and encourage this evolution. There is no universally accepted definition of digital health. In fact, researchers studying the definition recently came across no fewer than 95 published definitions for the concept of digital health.1 There were, however, some clear patterns: there is an emphasis on how data is used to improve care; there is a focus on the provision of healthcare, rather than the use of technology; and the definitions tend to highlight the well-being of people and populations over the caring of patients with diseases. As used in this article, digital health encompasses the use of digital tools and technologies to improve and manage an individual's or a population's health and wellness.

Washington – People returning to the office following the pandemic will find an array of tech-infused gadgetry to improve workplace safety but which could pose risks for long-term personal and medical privacy. Temperature checks, distance monitors, digital "passports," wellness surveys and robotic cleaning and disinfection systems are being deployed in many workplaces seeking to reopen. Tech giants and startups are offering solutions that include computer vision detection of vital signs to wearables that can offer early indications of the onset of COVID-19 and apps that keep track of health metrics. Salesforce and IBM have partnered on a "digital health pass" to let people share their vaccination and health status on their smartphone. Clear, a tech startup known for airport screening, has created its own health pass which is being used by organizations such as the National Hockey League and MGM Resorts.