Infoception 7
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Quantitative or qualitative parameters?the two are relevant for the design of the infoceptive matrix.The art of infoception design is to find the right combination of qulaitative/quatitative clinical parameters according the aim of the problem to solve.
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This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
LowerLimbsRehabilitation
This document proposes an algorithm to calculate angles of the lower limbs using inertial measurement units (IMUs) placed on the lower back, calves, and thighs while a patient performs an overhead squat exercise. The algorithm was tested on patients and compared theoretical measurements to experimental measurements from the IMUs. Error rates were low, ranging from 0.95-10.11% for different joints, showing the potential of using IMU sensors to help physical therapists evaluate rehabilitation exercises more efficiently.
Risk Of Heart Disease Prediction Using Machine Learning
This document describes a study that uses machine learning algorithms to predict the risk of heart disease. It analyzes a dataset containing characteristics of 270 patients using algorithms like logistic regression, naive Bayes, support vector machine, k-nearest neighbors, decision tree, random forest, XGBoost and artificial neural network. The random forest algorithm achieved the highest prediction accuracy of 95%. The model takes patient attributes as input and outputs a prediction of 0 or 1 indicating the presence or absence of heart disease risk. It aims to help detect risk early to reduce death rates from heart disease, which is a leading cause of death worldwide.
Wearable performance devices
This document discusses the growing use of wearable sensor technology in sports medicine. It describes various types of movement sensors like pedometers, accelerometers, gyroscopes, and GPS that can track athlete movements, workloads, and positions. It also discusses physiologic sensors like heart rate monitors and temperature sensors. The document concludes that wearable sensors provide a method to monitor real-time physiology and movement to optimize performance, detect injury risks, and design efficient training programs.
IRJET- Health Monitoring and Stress Detection System
This paper proposes a system that combines health monitoring and stress detection to analyze physiological data and detect a person's stress levels. The system measures health parameters like heart rate, skin conductance, blood pressure and brainwaves using sensors. These parameters are known stress markers. The data is analyzed by a microcontroller and an alert is provided if stress is detected. The system aims to accurately detect stress levels using multiple stress-related data sources, with an integrated health monitoring approach allowing for reduced costs compared to separate systems. Analysis of brainwaves through EEG provides particularly accurate assessment of mental state for stress detection. The system is intended to help manage stress, which has become highly prevalent in modern society.
Evaluating a Potential Commercial Tool for Healthcare Application for People ...
Evaluating a Potential Commercial Tool for Healthcare Application for People ...Artificial Intelligence Institute at UofSC
Presentation of Hexoskin Validation for KHealth's Dementia Project
The paper is available at: http://www.knoesis.org/library/resource.php?id=2155
Citation for the paper: T. Banerjee, P. Anantharam, W. L. Romine, L. Lawhorne, A. Sheth, 'Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia' in Proc. of the Intl Conf on Health Informatics and Medical Systems (HIMS), Las Vegas, July 27-30, 2015.
A fuzzy logic based expert system for determination of health risk level of p...
This document describes a fuzzy logic-based expert system designed to determine the health risk level of patients based on their vital signs. The system uses 5 physiological parameters (blood pressure, pulse rate, oxygen saturation, temperature, and blood sugar) as inputs and assigns a risk level from 0 to 14 as the output. It is based on the Modified Early Warning Score system and implements a Mamdani fuzzy inference system with 1800 rules. The system was developed and simulated using MATLAB's fuzzy logic toolbox. Sample input values were tested, and the corresponding risk level output and fuzzy rule surfaces were displayed.
A fuzzy logic based expert system for determination of health risk level of p...
Abstract The aim of this study is to design a fuzzy expert system for calculating the health risk level of a patient. The fuzzy logic system is a simple, rule-based system and can be used to monitor biological systems that would be difficult or impossible to model with simple, linear mathematics. The designed system is based on the modified early warning score (MEWS).The system has 5 input field and 1 output field. The input fields are blood pressure, pulse rate, SPO2 ( it is an estimation of the oxygen saturation level in blood. ), temperature, and blood sugar. The output field refers the risk level of the patient. The output ranges from 0 to 14. This system uses Mamdani inference method. A larger value of output refers to greater degree of illness of the patient. This paper describes research results in the development of a fuzzy driven system to determine the risk levels of health for the patients. The implementation and simulation of the system is done using MATLAB fuzzy tool box. Keywords: Fuzzy logic, The Modified Early Warning Score (MEWS), Physiological Parameters, Classification of Vital Signs, MATLAB Tool, Fuzzy Inference System, Fuzzification, Defuzzification
REVIEW LCA
The document summarizes evidence from a systematic review and expert consensus on clinical practice guidelines for anterior cruciate ligament (ACL) rehabilitation. The rehabilitation should include three phases: (1) an impairment-based phase, (2) a sport-specific training phase, and (3) a return to play phase. A battery of strength, hop, movement quality, and psychological tests should guide progression between phases over 9-12 months. Return to play criteria should incorporate objective measures like strength and hop tests to assess risk of reinjury.
PREDICTION OF HEART DISEASE USING LOGISTIC REGRESSION
This document discusses predicting heart disease using logistic regression. It begins with an introduction to heart disease and risk factors. It then discusses previous work using machine learning to predict diseases. The proposed system uses a logistic regression model to predict heart disease risk using 14 attributes from a dataset. It discusses the logistic regression technique and provides a workflow diagram. The results show the logistic regression model achieving 81% accuracy. It concludes that logistic regression is effective for predicting heart disease risk from simple clinical predictors.
Smart System using Fuzzy, Neural and FPGA for Early Diagnosis of Renal Disease
In this work we propose, fuzzy logic,neural network and FPGAbased solution for early diagnosis of renal disease. Proposed system also provides a preliminary remedy in terms of medicine by proper indication. Pathophysiological parameters for detecting renal function abnormalities are identified and based on these data, next state of the patient is predicted using Neural Network and the system is designed which can provide the diagnosis for patient’s state i.e normal, moderate or critical using Fuzzy Logic. When the system diagnoses it as critical state, preliminary remedial medicines are also suggested by the system, which can be very helpful to patients where patient:doctor ratio is very poor especially in rural areas of developing countries and also for domestic use for early diagnosis of the disease. FPGA based implementation is also easy to reconfigure and provides lower time to market.
LIFE EXPECTANCY PREDICTION FOR POST THORACIC SURGERY
This document discusses using machine learning algorithms to predict life expectancy after thoracic surgery. Researchers used attribute ranking and selection methods to identify the most important attributes from a dataset of patient health records. They evaluated algorithms like logistic regression and random forest on the reduced dataset. Logistic regression achieved the highest prediction accuracy of 85%. The goal was to more accurately predict mortality risk based on a patient's underlying health issues and attributes related to lung cancer.
Purple and white modern advertising presentation
The document summarizes a study that used machine learning models to predict patient survival from heart failure and identify important risk factors. [1] The study analyzed data from 299 heart failure patients, applying classifiers to predict survival and rank risk factors. [2] The models identified serum creatinine and ejection fraction as the most important predictors of survival. [3] Using only these two factors resulted in more accurate predictions than using all available data, suggesting these metrics could help doctors predict patient outcomes.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
Estimation of Walking rate in Complex activity recognition
This document summarizes a study that investigated using a flexible conductive polymer sensor embedded in leggings to monitor knee movement and activity recognition. The sensor was connected to a wireless sensing node to collect data. Twelve subjects performed walking, running, and stair activities while wearing the smart leggings. Test-retest reliability of the sensor output range showed good to excellent reliability. Discrimination of activities was achieved using total power and median frequency features from the sensor signal, demonstrating over 90% accuracy. The system shows potential for assessing knee function during daily activities.
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Comparative Analysis of Heart Disease Prediction Models: Unveiling the Most A...
Comparative Analysis of Heart Disease Prediction Models: Unveiling the Most A...
Heart Disease Prediction using Machine Learning Algorithms
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Genetically Optimized Neural Network for Heart Disease Classification
Genetically Optimized Neural Network for Heart Disease Classification
Risk Of Heart Disease Prediction Using Machine Learning
Risk Of Heart Disease Prediction Using Machine Learning
IRJET- Health Monitoring and Stress Detection System
IRJET- Health Monitoring and Stress Detection System
Evaluating a Potential Commercial Tool for Healthcare Application for People ...
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PREDICTION OF HEART DISEASE USING LOGISTIC REGRESSION
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Smart System using Fuzzy, Neural and FPGA for Early Diagnosis of Renal Disease
Smart System using Fuzzy, Neural and FPGA for Early Diagnosis of Renal Disease
LIFE EXPECTANCY PREDICTION FOR POST THORACIC SURGERY
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Estimation of Walking rate in Complex activity recognition
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More from Christian Agaya
Gt x project a concept-car-like project
The GT-X Project aims to further develop concepts around infoception and its industrial and commercial applications. It is a multifocal effort bringing together the RD team to focus on infoception techniques, applications and products, as well as an innovative business model. GT-X will generate many apps based on APS's philosophy, enabling teams to address different focuses throughout the project cycle. Once adopted, tools may be used as potential commercial successes. The project context involves developing a "southern analog" of the Internet of Things, drawing from examples of digital empowerment and innovation in countries like India. Key challenges include establishing international partnerships, creating an effective multidisciplinary collaboration platform, developing strong data aggregation strategies, and ensuring successful user experiences
Gt x project lecture12
GT-X est le projet-phare de APS en 2016.
Cette présentation le replace dans le contexte qui a prévalu à son développement:digital empowerment,usages numériques,quantified self,infoception.
Morbidite exprimée 2
La morbidité exprimée reflète le ressenti du patient par rapport à une situation pathologique.Elle est estimée par un questionnaire exploratoire administré par le clinicien ou l'épidémiologiste.Elle représente un forme de Quantified Self appelée à connaître un essor particulier , notamment à cause de l'avènement de capteurs de données cliniques/biologiques les plus variés et des outils d'analyses pour le traitement de ces données personnelles.Le traitement des données issues de l'estimation de la morbidité exprimée est utile pour le contrôle et le suivi de maladies chroniques telles que le glaucome primitif à angle ouvert, et pour l'élaboration de stratégies santé digitales subséquentes.
Morbiité infraclinique exprrimée
La charge de morbidité infraclinique exprimée est le marqueur plus fin du statut d'un agent , en matière de prévention des maladies à composantes professionnelles.
Infoception and quantified self
We demonstrate why Quantified Self must be seen as an instance of Infoception.We present the 2 concepts and then, in one slide, we show how infoception produced Quantified Self.
7 steps infoceptive clinical objects implementation
Digital health procedure according APSCONSEILS PROTOCOL.We used this protocol to perform healthcare monitoring among computerwork agents for industrial companies.The collected clinical data were then included in an infoceptive matrix for health and safety in workplce programs.The infoceptive matrix allows the monitoring of clinical,cybernetics and environmental matters in a wide range of situations.
Indeed, the combination of these parameters provide new toolos for multidisciplinary Platform settlement in order to resolve global or specific problems linked to health,technology or environment issues.Risk management advisers and experts are among the users of infoceptive matrix in their day to day risk monitoring procedures.
7 steps infoceptive clinical objects implementation
Digital health procedure design according APSCONSEILS PROTOCOL.
I add this picture to show the whole process in a single picture.further explanations in next slides.The fact is , people are surrounded by infoceptive sensors,but the clinical ones,particularly the digital devices are going to take a bigger place in the next years.
Infoceptive networks
nfoception is a word we use in APSCONSEILS protocols to describe a
situation where the following domains are involved:
-health
-technology
-environment
Each domain provides infoception with parameters in order to set up an infoceptive profile.
Infoceptive network is a set of healthcare,cybernetics,environmental parameters with dynamic relationships.
The infoceptive network manager uses the already known relationships and create new ones to achieve a goal
given a problem to solvein medicine,technology,environment matters.
Infoception 8:going modal
In order to implement user experience assessment protocol , we merge to approaches of human computer interaction : KEYSTROKE (Card et all.) and EPIC (Kiera's).The two models allow to define a wide range of modal operators usable for HCI purpose.But is it possible to exted their usability to clinical matters?
Infoception6 x
The document discusses using eye tracking devices to assess cognitive functions through ocular-complex measurements during human-computer interaction. It describes that eye fixations allow users to process information by refreshing and extending short-term memory. Eye tracking is presented as the best technique to assess functions of the ocular complex like saccades, pursuits, and accommodation which relate to mental processes like attention, awareness, and working memory. Examples of potential tests are provided that measure fixation duration, number of fixations and saccades using a gradient scale.
Infoception5
Biometrics clinical testings for the human computer interation assessment , usable for user experience studies,human ressources management,health and safety in workplace,software design...
Infoception pregnant figures
This very short document discusses pregnancy but provides little information. It mentions "pregnant figures" but does not elaborate on what or who those figures are. The document is incomplete as it states "to be continued" but does not continue, leaving the reader without important context or details.
Infoception2
I present the basics of the approach we designed in APSCONSEILS R-D Unit.APSCONSEILS is a start-up dedicated to the production of structured infos on the area of human computer professional interaction.
Our methods are useful in other areas , the aims is to share it with reseachers from those areas.
Infoception
INFOCEPTION is a new term coined by APSCONSEILS Research and Development Unit to renew the approach to human-computer interaction and design new patterns for information technology that focus on people and their experiences. It can also be used in medical and clinical procedures to summarize complex information concisely.
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7 steps infoceptive clinical objects implementation
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Recomendamos muito.
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Esta publicação só está disponível em inglês até o momento.
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- 1. Infoception 7 Step by step Implementation of the Infoceptive profile Clinical Biometrics/Somatosystemic parameters Christian Agaya.apsconseils r-d unit.2011
- 6. Musculoskeletal status of the User (examples) Inflammation and movement limitations are the main points to explore . Christian Agaya.apsconseils r-d unit.2011 Somatosystemic segment/ Neck Gradient 0 1 2 3 4 5 Somatosystemic segment/ Bones Gradient 0 1 2 3 4 5
- 7. Global Systemic Indicators (examples) Classical clinical parameters allows to get a systemic scope of the User ‘s status. Christian Agaya.apsconseils r-d unit.2011 Somatosystemic segment/ Blood pressure Gradient 0 1 2 3 4 5 Somatosystemic segment/ weight Gradient 0 1 2 3 4 5 Somatosystemic segment/ auditive acuity Gradient 0 1 2 3 4 5
- 9. Christian Agaya.apsconseils r-d unit.2011