All formulas, model parameters and sources behind the generator. Every one of the 34 sport profiles is calibrated against published heart rate studies — the effort ranges in the table below are taken from those papers, not estimated.
Without your own measurement, HRmax is estimated from age and — optionally — sex:
Tanaka provides more accurate estimates than the classic "220 − age" rule. Gulati was validated on over 5,000 women. A self-measured value always takes precedence.
All intensity values in this app are expressed as % of heart rate reserve (HRR). The Karvonen formula (1957) converts them to a target HR:
Mono-exponential approach with asymmetric time constants (τ_up < τ_down), auto-correlated noise (AR(1), φ = 0.85) and cardiac drift (Coyle & González-Alonso 2001). Trained individuals have smaller τ values (Imai et al. 1994). A deterministic PRNG produces the same curve for identical inputs.
| Level | % HRR | Zone | Application |
|---|---|---|---|
| Easy | 35–68 % | Z1–Z2 | Technique, warm-up, recovery |
| Moderate | 42–78 % | Z2–Z3 | Endurance base, light sparring |
| Normal | 50–88 % | Z3–Z4 | Regular training, Randori |
| Hard | 57–94 % | Z4–Z5 | Intense sparring, intervals |
| Maximum | 62–100 % | Z5 | Competition, tournament |
Aerobic TE: TRIMP mapped to 0–5 (Firstbeat 2017). Anaerobic TE: quadratic weighting above 80% HRR (Böning 1984).
Energy expenditure: Swain & Franklin (2002). Fat oxidation crossover: Brooks & Mercier (1994).
Every sport has its own calibrated profile: target heart rate during work and rest, time constants for onset and recovery, volatility, cardiac drift, load pattern and anticipatory offset. Values below refer to heart rate during the main effort, not the session average.
| Sport | Effort HR | Pattern | Source |
|---|---|---|---|
| Boxing | 85–93 % | Rounds | de Lira 2013; Slimani 2018 |
| Kickboxing | 86–90 % | Rounds | Chaabène 2012 |
| Wrestling | 85–95 % | Chaotic | Barbas 2011; Nilsson 2002 |
| Judo | 85–93 % | Chaotic | Franchini 2013; Slimani 2018 |
| Brazilian Jiu-Jitsu | 75–85 % | Long phases | Andreato 2015 |
| MMA | 88–94 % | Rounds | Amtmann 2008; Folhes 2023 |
| Taekwondo | 86–100 % | Rounds | Bridge 2009; Slimani 2018 |
| Karate (Kumite) | 83–94 % | Rounds | Tabben 2014; Slimani 2018 |
| Muay Thai | ~95 % | Rounds | Slimani 2018 |
| Running | 70–85 % | Continuous | Fleckenstein 2023 |
| Cycling | 65–80 % | Continuous | Muyor 2020 |
| Mountain Bike | 75–90 % | Intermittent | Impellizzeri 2005 |
| Swimming | 70–85 % | Intervals | DiCarlo 1991 |
| Rowing | 70–88 % | Continuous | Brown 2012 |
| Cross-Country Skiing | 87–89 % | Continuous | Talsnes 2023; Sandbakk |
| Weight Training | 50–75 % | Intermittent | Sci Rep 2023 |
| Circuit Training | 70–85 % | Intervals | ACSM |
| CrossFit | 90–95 % | Intervals | Tibana 2020 |
| Football / Soccer | 80–90 % | Intermittent | Alexandre 2012 |
| Handball | 85–90 % | Intermittent | Belka 2014 |
| Basketball | 85–92 % | Intermittent | Ben Abdelkrim 2007 |
| Volleyball | 60–75 % | Intermittent | Sci Rep 2022 |
| Ice Hockey | 85–95 % | Shifts | Spiering 2003; Stanula 2014 |
| Field Hockey | 77–93 % | Intermittent | Macleod 2016 |
| Tennis | 60–80 % | Intermittent | Fernandez 2006 |
| Table Tennis | 68–80 % | Intermittent | Pradas 2023 |
| Badminton | 80–89 % | Intermittent | Faude 2007 |
| Squash | 81–92 % | Intermittent | Girard 2007; James 2021 |
| Padel | 70–80 % | Intermittent | Guijarro-Herencia 2023 |
| Climbing | 74–85 % | Intervals | Draper 2020 |
| Yoga | ~49 % | Steady | Sherman 2017 |
| Pilates | ~50 % | Steady | Olson 2004 |
Percentages refer to HRmax. Values are population means from time-motion and heart rate studies; individual deviations are expected.
| Field | FIT-Nr. | Encoding |
|---|---|---|
| avg / max / min HR | 15 / 16 / 56 | bpm (uint8) |
| total_calories | 11 | kcal (uint16) |
| total_training_effect | 24 | ×10 (uint8) |
| training_stress_score | 35 | ×10 (uint16) |
| intensity_factor | 36 | ×1000 (uint16) |
| threshold_heart_rate | 27 | bpm (uint8) |
| time_in_hr_zone[0–4] | 65–69 | ms (uint32) |
| user_profile | Global 3 | age, sex, weight, HRmax, resting HR |
| zones_target | Global 7 | HRmax, LTHR, hr_calc_type=HRR |
The file is finalized with a correct CRC-16 and created entirely locally in the browser — no data is transmitted.
Karvonen J et al. (1957). Ann Med Exp Biol Fenn 35(3):307.
Tanaka H, Monahan KD, Seals DR (2001). J Am Coll Cardiol 37:153.
Gulati M et al. (2010). Circulation 122(2):130.
Banister EW (1991). Physiological Testing of the High-Performance Athlete. Human Kinetics.
Firstbeat Technologies (2017). Training Effect. White Paper.
Allen H, Coggan A (2003). Training and Racing with a Power Meter. VeloPress.
Seiler S, Kjerland GØ (2006). Scand J Med Sci Sports 16(1):49.
Swain DP, Franklin BA (2002). Med Sci Sports Exerc 34(1):152.
Brooks GA, Mercier J (1994). J Appl Physiol 76(6):2253.
Imai K et al. (1994). J Am Coll Cardiol 24(6):1529.
Coyle EF, González-Alonso J (2001). Exerc Sport Sci Rev 29(2):88.
Slimani M et al. (2018). Heart rate monitoring during combat sports matches. Int J Perf Anal Sport 18(2).
Bridge CA et al. (2009). Physiological responses during international Taekwondo competition. Int J Sports Physiol Perform.
Tabben M et al. (2014). Physiological responses during international karate kumite competition.
Talsnes RK et al. (2023). Sprint cross-country skiing competition responses. Eur J Appl Physiol.
Spiering BA et al. (2003). Cardiovascular demands in women’s ice hockey. J Strength Cond Res 17(2).
Girard O et al. (2007). Physiological responses to squash match play. Br J Sports Med.
Guijarro-Herencia J et al. (2023). Conditional performance factors in padel. Front Sports Act Living.
Garmin (2024). FIT SDK Profile.xlsx, Version 21.x.