sing Artificial Intelligence techniques to understand the cardiorespiratory system
Not Applicable
Recruiting
- Conditions
- Coronary DiseaseLung DiseasesDiabetes MellitusRisk FactorsHealthy VolunteersC14.280.647.250C08.381C18.452.394.750M01.774.500E05.318.740.600.800.725
- Registration Number
- RBR-9yqtqn
- Lead Sponsor
- niversidade Federal de São Carlos (UFSCar)
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- Not specified
- Target Recruitment
- Not specified
Inclusion Criteria
Will be included volunteers men and women; with a body mass index of less than 35kg / m2; non-alcoholic; non-drug users; non-neurological or osteoarticular disease patients that impede the exercise protocol
Exclusion Criteria
Volunteers who exhibit electrocardiographic changes at rest; clinical exercise test (ST segment depression; ventricular; supraventricular arrhythmias; atrial fibrillation; atrioventricular block; sustained supraventricular tachycardia; non-sustained atrial tachycardia) are excluded; do not complete all of the proposed assessments.
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Primary outcome is to measure the vital signs collected (heart rate, respiratory rate; hip cadence) by wearables and process these data using the artificial intelligence algorithm method that will predict cardiorespiratory health as a measure of maximal oxygen uptake.
- Secondary Outcome Measures
Name Time Method Secondary outcome is to verify the validity of the predicted maximal oxygen uptake through the wearable system with maximal oxygen uptake directly measured by the exhaled and inspired gas method.