Real-time Assessment and Visual Feedback for Patient Rehabilitation Using Inertial Sensors

Rehabilitation exercises needs have been continuously increasing and have been projected to increase in future as well based on its demand for aging population, recovering from surgery, injury and illness and the living and working lifestyle of the people. This research aims to tackle one of the most critical issues faced by the exercise administers-Adherence or Non-Adherence to Home Exercise problems especially has been a significant issue resulting in extensive research on the psychological analysis of people involved. In this research, a solution is provided to increase the adherence of such programs through an automated real-time assessment with constant visual feedback providing a game like an environment and recording the same for analysis purposes. Inertial sensors like Accelerometer and Gyroscope has been used to implement a rule-based framework for human activity recognition for measuring the ankle joint angle. This system is also secure as it contains only the recordings of the data and the avatar that could be live fed or recorded for the treatment analysis purposes which could save time and cost. The results obtained after testing on four healthy human subjects shows that with proper implementation of rule parameters, good quality and quantity of the exercises can be assessed in real time

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

Life Sciences Program Tasks and Bibliography for FY 1996

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1996. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web page

Six Decades of Flight Research: An Annotated Bibliography of Technical Publications of NASA Dryden Flight Research Center, 1946-2006

Titles, authors, report numbers, and abstracts are given for nearly 2900 unclassified and unrestricted technical reports and papers published from September 1946 to December 2006 by the NASA Dryden Flight Research Center and its predecessor organizations. These technical reports and papers describe and give the results of 60 years of flight research performed by the NACA and NASA, from the X-1 and other early X-airplanes, to the X-15, Space Shuttle, X-29 Forward Swept Wing, X-31, and X-43 aircraft. Some of the other research airplanes tested were the D-558, phase 1 and 2; M-2, HL-10 and X-24 lifting bodies; Digital Fly-By-Wire and Supercritical Wing F-8; XB-70; YF-12; AFTI F-111 TACT and MAW; F-15 HiDEC; F-18 High Alpha Research Vehicle, F-18 Systems Research Aircraft and the NASA Landing Systems Research aircraft. The citations of reports and papers are listed in chronological order, with author and aircraft indices. In addition, in the appendices, citations of 270 contractor reports, more than 200 UCLA Flight System Research Center reports, nearly 200 Tech Briefs, 30 Dryden Historical Publications, and over 30 videotapes are included

A Laguerre Neural Network-Based ADP Learning Scheme with its Application to Tracking Control in the Internet of Things

Sensory data have becoming widely available in large volume and variety due to the increasing presence and adoption of the Internet of Things. Such data can be tremendously useful if they are processed properly in a timely fashion. They could play a key role in the coordination of industrial production. It is thus desirable to explore an effective and efficient scheme to support data tracking and monitoring. This paper intends to propose a novel automatic learning scheme to improve the tracking efficiency while maintaining or improving the data tracking accuracy. A core strategy in the proposed scheme is the design of Laguerre neural network (LaNN)-based approximate dynamic programming (ADP). As a traditional optimal learning strategy, ADP is a popular approach for data processing. The action neural network (NN) and the critic NN as two important components in ADP have big impact on the performance of ADP. In this paper, a LaNN is employed as the implementation of the action NN in ADP considering Laguerre polynomials’ approximation capability. In addition, this LaNN-based ADP is integrated into an online parameter-tuning framework to optimize those parameters of characteristic model that is used to trace the data in the tracking control system. Meanwhile, this article provides an associated Lyapunov convergence analysis to guarantee a uniformly ultimately boundedness property for tracking errors in the proposed approach. Furthermore, the proposed LaNN-based ADP optimal online parameter-tuning scheme is validated using a temperature dynamic tracking control task. The simulation results demonstrate that the scheme has satisfactory learning performance over time

A Laguerre Neural Network-Based ADP Learning Scheme with its Application to Tracking Control in the Internet of Things

Sensory data have becoming widely available in large volume and variety due to the increasing presence and adoption of the Internet of Things. Such data can be tremendously useful if they are processed properly in a timely fashion. They could play a key role in the coordination of industrial production. It is thus desirable to explore an effective and efficient scheme to support data tracking and monitoring. This paper intends to propose a novel automatic learning scheme to improve the tracking efficiency while maintaining or improving the data tracking accuracy. A core strategy in the proposed scheme is the design of Laguerre neural network (LaNN)-based approximate dynamic programming (ADP). As a traditional optimal learning strategy, ADP is a popular approach for data processing. The action neural network (NN) and the critic NN as two important components in ADP have big impact on the performance of ADP. In this paper, a LaNN is employed as the implementation of the action NN in ADP considering Laguerre polynomials’ approximation capability. In addition, this LaNN-based ADP is integrated into an online parameter-tuning framework to optimize those parameters of characteristic model that is used to trace the data in the tracking control system. Meanwhile, this article provides an associated Lyapunov convergence analysis to guarantee a uniformly ultimately boundedness property for tracking errors in the proposed approach. Furthermore, the proposed LaNN-based ADP optimal online parameter-tuning scheme is validated using a temperature dynamic tracking control task. The simulation results demonstrate that the scheme has satisfactory learning performance over time