Industry experts and HIE leaders have moved past early "model"-based debates to focus on creating core and value-added services, standardizing the meaning of the clinical data, and simplifying point to point connections necessary for information to flow between the HIE and providers EHRs.

A hybrid approach.

Motion recognition is a topic in software engineering and dialect innovation with a goal of interpreting human signals through mathematical algorithm. Hand gesture is a strategy for nonverbal communication for individuals as it expresses more liberally than body parts. Hand gesture acknowledgment has more prominent significance in planning a proficient human computer interaction framework, utilizing signals as a characteristic interface favorable to circumstance of movements. Regardless, the distinguishing proof and acknowledgment of posture, gait, proxemics and human behaviors is furthermore the subject of motion to appreciate human nonverbal communication, thus building a richer bridge between machines and humans than primitive text user interfaces or even graphical user interfaces, which still limits the majority of input to electronics gadget. In this paper, a study on various motion recognition methodologies is given specific accentuation on available motions. A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques. Difficulties and future investigation bearing are also examined.

Hand posture and gesture recognition techniques for virtual reality applications: a survey

Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthcare costs, existing approaches lack versatility, robustness, and practical relevance. In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises. The main components of the framework are metrics for quantifying movement performance, scoring functions for mapping the performance metrics into numerical scores of movement quality, and deep neural network models for generating quality scores of input movements via supervised learning. The proposed performance metric is defined based on the log-likelihood of a Gaussian mixture model, and encodes low-dimensional data representation obtained with a deep autoencoder network. The proposed deep spatio-temporal neural network arranges data into temporal pyramids, and exploits the spatial characteristics of human movements by using sub-networks to process joint displacements of individual body parts. The presented framework is validated using a dataset of ten rehabilitation exercises. The significance of this work is that it is the first that implements deep neural networks for assessment of rehabilitation performance.

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A Deep Learning Framework for Assessing Physical Rehabilitation Exercises

Graphical abstractDisplay Omitted HighlightsWe propose a new algorithm for the dynamic signature verification.The algorithm implements a new way of signatures division - hybrid partitioning.Hybrid partitions are associated with time and dynamics of the signing process.The algorithm compares test signatures to the reference ones in interpretable way.The algorithm works independently for each signer. Identity verification based on authenticity assessment of a handwritten signature is an important issue in biometrics. There are many effective methods for signature verification taking into account dynamics of a signing process. Methods based on partitioning take a very important place among them. In this paper we propose a new approach to signature partitioning. Its most important feature is the possibility of selecting and processing of hybrid partitions in order to increase a precision of the test signature analysis. Partitions are formed by a combination of vertical and horizontal sections of the signature. Vertical sections correspond to the initial, middle, and final time moments of the signing process. In turn, horizontal sections correspond to the signature areas associated with high and low pen velocity and high and low pen pressure on the surface of a graphics tablet. Our previous research on vertical and horizontal sections of the dynamic signature (created independently) led us to develop the algorithm presented in this paper. Selection of sections, among others, allows us to define the stability of the signing process in the partitions, promoting signature areas of greater stability (and vice versa). In the test of the proposed method two databases were used: public MCYT-100 and paid BioSecure.

A new algorithm for identity verification based on the analysis of a handwritten dynamic signature

Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World Wide Web. To the end of making available large-scale, high quality domain ontologies, effective and usable methodologies are needed to facilitate the process of Ontology Building. Many of the methods proposed so far only partly refer to well-known and widely used standards from other areas, like software engineering and knowledge representation. In this paper we present UPON, a methodology for ontology building derived from the Unified Software Development Process. A comparative evaluation with other methodologies, as well as the results of its adoption in the context of the Athena Integrated Project, are also discussed.

A proposal for a unified process for ontology building : UPON

Most formal rehabilitation facilities are situated in a hospital or care center setting, which may not always be conveniently accessible for patients, especially those in geographically isolated areas Home-based rehabilitation has potential to offer greater accessibility and thus increase consistent uptake In addition, the exercise performed in conventional rehabilitation contexts may be insufficient to ensure the patient's speedy recovery, with complimentary rehabilitation exercises at home required to make a difference The goal is to provide effective home-based rehabilitation offering outcomes similar to those obtained through hospital-based rehabilitation under the supervision of an occupational therapist This paper presents the development of a Kinect-based system for ensuring home-based rehabilitation using a Dynamic Time Warping (DTW) algorithm and fuzzy logic The ultimate goal is to assist patients in conducting safe and effective home-based rehabilitation without the immediate supervision of a physician

Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic

Several powerful trends are contributing to an aging of much of the world's population, especially in economically developed countries. To mitigate the negative effects of rapidly ageing populations, societies must act early to plan for the welfare, medical care and residential arrangements of their senior citizens, and for the manpower and associated training needed to execute these plans. This paper describes the development of an Ambient Intelligent Community Care Platform AICCP, which creates an environment of Ambient Intelligence through the use of sensor network and mobile agent MA technologies. The AICCP allows caregivers to quickly and accurately locate their charges; access, update and share critical treatment and wellness data; and automatically archive all records. The AICCP presented in this paper is expected to enable caregivers and communities to offer pervasive, accurate and context-aware care services.

Pervasive community care platform: Ambient Intelligence leveraging sensor networks and mobile agents

Good physical fitness generally makes the body less prone to common diseases. A personalized exercise plan that promotes a balanced approach to fitness helps promotes fitness, while inappropriate forms of exercise can have adverse consequences for health. This paper aims to develop an ontology-driven knowledge-based system for generating custom-designed exercise plans based on a user's profile and health status, incorporating international standard Health Level Seven International (HL7) data on physical fitness and health screening. The generated plan exposing Representational State Transfer (REST) style web services which can be accessed from any Internet-enabled device and deployed in cloud computing environments. To ensure the practicality of the generated exercise plans, encapsulated knowledge used as a basis for inference in the system is acquired from domain experts. The proposed Ubiquitous Exercise Plan Generation for Personalized Physical Fitness (UFIT) will not only improve health-related fitness through generating personalized exercise plans, but also aid users in avoiding inappropriate work outs.

https://www.mdpi.com/1424-8220/14/3/4560/pdf

Chang-Yu Chiang

Papers

Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic

Pervasive community care platform: Ambient Intelligence leveraging sensor networks and mobile agents

Ontological Knowledge Engine and Health Screening Data Enabled Ubiquitous Personalized Physical Fitness (UFIT)