strive.ai samples the context observed during each activities peaks, best efforts, and segment efforts. We use this context to enhance your experience and identify what factors are influencing your fitness.
Definition: We use machine learning to determine the influence each of your factors has on your fitness (e.g. Peak, Best Effort Pace or Segment Effort). Each factor is assigned with the following three attributes:
Importance: Relfect the amount of influence the variable has on producing a higher fitness.
Direction: Positive factors mean a higher value leads to a higher level of fitness, negative factors occur when a higher factor leads to a lower fitness.
Priority: We stack rack your factors by the absolute value of their importance.
The base Factors are sourced from Strava:
Altitude: Average altitude during sample.
Cadence: Average cadence during sample.
Distance: Distance traveled at midpoint of sample.
Grade: Average gradient during sample.
Heartrate: Average heartrate during sample.
Power: Average power in watts during sample.
Temperature: Average temp during sample.
Velocity: Average speed during sample.
Warmup: Number of seconds leading up to sample.
Activity Type: the type of activity the sample was taken from.
Athlete Weight: Your weight setting at the time of the activity.
FTP: Your functional threshold (bike only) at the time of the activity.
Weather: Two weeks after your activity we sample the weather observed at that location, date and time. Weather data points include:
Wind Heading and Speed
Garmin Connect: If you authorize us to receive your FIT files we sample everything sourced from your fitness sensors.
Sensor Information (e.g. Battery Level, Unique Identifiers, Mfg, etc)
Stryd Running Economy Variables
Garmin Running Dynamics Variables
Moxy or Humon Muscle Oxygen Variables
Extended Bike Power Meter Data
Bio Metrics: We're planning to start to capture key bio metrics to include in our Factors analysis.