Features Insights


  • iTRAIN.U Preview


#Real Footage of our apps - This is innovation. This is passion. This is 3 years ahead of EVERY other app.  Health and Fitness Apps That Apply User Specific Sub-Symbolic Representations. iHealth helps you live healthier by giving you actionable insights into how your physical activity benefits your overall health. Data is the lifeblood of what we do, so it won’t come as a surprise that in order to exploit all the recent advances in the growth of the health and fitness software market, we are doing things very differently. In order to become a significant global player, and mine this abundance of data we must embrace automation and one of its enablers, artificial intelligence.  We have established a certain level of momentum than affords us to innovate quickly, ahead of our competitions expectations whilst keeping up with the mind-boggling speed at which new data is being produced. The rapid increase in smartphone and Apple Watch numbers, health and fitness apps, digital medical services and the increasing thirst for training knowledge and efficiency, sets for the first time the perfect conditions for the application of Biometric logic into health and fitness software.

  •      Data Accuracy


Too many health and fitness apps are letting us down. We have spent the last 4 years in R&D doing everything that the other apps don't. 

We are presently going through clinical studies to fully validate our apps.

Another key feature that sets us apart is that our apps have the ability to be tested via the in-built "Validation Tests" coded into our software, allowing our partners, and any one of our " app users" to test and check our accuracy and methods, 

Our data covers: 

We extract authorised data markers and apply them into formula and algorithms that have been internally tested by (iHEALTH) and or verified by external professional medical institutions, university research departments or clinical trials.

(iHEALTH) apply algorithms through artificial intelligence coding logic. This means (iHEALTH) will predict and make health and fitness assumptions about each individual user from the continuous feeds of (authorised) live data.

(iHEALTH) software will automatically apply algorithmic assumptions and predictions into the “actionable” function and settings features of it’s app’s, so that each user is set individualised physiological challenge / stimuli / targets/ recovery, i.e. “The workout “Kcal target”, “FatMax training zone” “Kcal.burn/min rate”
Actionable function and settings features are intelligently updated in real time without update number restriction, via AI software coding that has been developed with a logic sequence that ensures each user receives safe, and optimally derived actionable requests under the following physiological parameters:


• Fitness Capacity Prediction (estimation of VO2 Max)

• Max. Heart Rate Estimate
• Prediction of Body Fat Percentage
• Prediction of Fat-Ox Threshold
• Prediction of Anaerobic Threshold
• Estimation of Net Energy Expenditure
• Estimation of Fat Expenditure
• Estimation of Energy Ratio During a Workout
• Estimation of % of VO2 Intensity
• Estimation of EPOC
• Prediction of Kcal Burn / min
• Prediction of Fat Burn / min
• Progressive Challenge Training Load Prediction
• Prediction of Workout Kcal Target
• Estimation of Max.HR Factoring Bio-Metric Baseline Changes +/-
• Estimation of VO2 Max Factoring Bio-Metric Baseline Changes +/-
• Estimation of RQ Factoring Bio-Metric Baseline Changes +/-
• Estimation of Available Energy Source (Training Fuel)
• Prediction of VO2 Max Score from The Cooper Test
• Prediction of VO2 Max Score from The Hunt 3 Test
• Prediction of VO2 Max Score from The EK-BAK Test
• Prediction of Workout Selection
• Prediction of HR. Rate Recovery
• Prediction of Mean Arterial Pressure
• Prediction of Pulse Pressure
• Prediction of RHR Classification
• Prediction of Standing Biometric Baseline
• Estimation of Daily Steps
• Estimation of Sleep Duration
• Estimation of Total Heart Beats
• Prediction of Biological Age
• Estimation of HRM Accuracy