FiTrack
By Jimmy Ding and Chong Wong
Declutter your mind
Why this matters?
According to [1], Decision Fatigue is what drains you physically and mentally throughout the day as you make hard decisions. If writing down your agenda can help clear your subconscious mind from clutter and increase productivity, we wanted to create an app to help the end users decrease the amount of worrying they have in life. Our app relies on the fact that you let the machine decide when you exercise based on machine learning in order to add variety [3] to improve your training regime (by decreasing boredom and plateaus), and declutter your mind. This is especially relevant as coronavirus has made us more sedentary sitting in front of a computer - harming our posture and health.
[1] - https://www.psychologytoday.com/files/attachments/584/decision200602-15vohs.pdf
[2] - https://pubmed.ncbi.nlm.nih.gov/21688924/
[3] - https://www.acefitness.org/education-and-resources/lifestyle/blog/1210/why-is-it-important-to-vary-my-workout-routines/
How we do it?
React Native
Expo
Machine Learning
Live Demo!
Setting up the profile
Factors For Planning The Schedule
White and black listed times entered by the user
Historical data of whether the user started the workout
The user’s current location and geofencing data entered by the user
Holidays and weekends
The user’s activity throughout the week
Distance towards the user’s goal
White and black listed times
White List
Temp White List
Less White List
Black List
Temp Black List
Less Black List
Significantly increases the chance of sessions being started
Same as white list, but gets deleted after the time passes
Slightly increases the chance of sessions being started
Disallows sessions from being started
Same as black list, but gets deleted after the time passes (use case: traveling)
Decreases the chance of sessions being started
Historical data
Factors:
Time in week - for non-uniform schedules such as college class schedules
Time in month - for monthly events such as periods
Time in year - for major holidays such as thanksgiving
Geofencing
Factors:
Distance from home - To detect events such as travelling
Distance from nearby gyms - To notify user when they are close to a gym
Black List Areas - To avoid areas such as workplaces or schools
White List Areas - To increase the chance in areas such as home
User’s activity
Factors:
When is the app opened - detect range of leisure time for user
Amount of temporary lists added - detect whether a user is busy for the week
Amount of manual entries added - detect whether a user is free for the week
Distance Towards User’s Goal
User is greatly underperforming - slightly lower the goal to adapt to the user
User is slightly underperforming - increase the frequency in order to reach the goal
User is slightly over performing - decrease the frequency to give the user rest
User is greatly over performing - slightly raise the goal to adapt to the user
Future plans