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TrainAware: Work Smarter, Not Harder
- 1. TrainAware: Work Smarter, Not Harder David K. Kirui Product Demo
- 2. Fitness industry valued at $80B annually 2
- 3. Step 1: Engineered Time-Variant Measures of Exercise Load and Overexertion 3 Heart Rate Variability Calories Burned from Activity Exercise Load OUTPUT: OVEREXERTION? Age, Gender, Height, Weight, BMI, Pulse, Body Temp, Steps Taken, Flights Climbed, Distance Walked/Run, Blood Oxygen Levels ➔ Data from Welltory COVID-19 & Wearables Open Data Project ➔ Daily measurements from 68 people over four months
- 4. Step 2: Process, Train & Test, Deploy... 4 Deployment ● Convert Standardized Features ● Predicted Probabilities Preprocessing ● One-Hot Encoding ● Feature Standardization eXtreme Gradient Boosting (XGBoost) ● Imbalanced classes ● Hyperparameter Tuning/CV ● Recall/Precision
- 5. Heart Rate, Training Load, Blood Oxygen Levels Matter! 5
- 6. 6 RECALL 92% ● 9+ times out of 10, algorithm correctly predicts that it’s safe to continue working out! ● Train with confidence! IMPROVEMENT OVER BASELINE 25% ● Improvement over a model without measures of heart rate change or exercise load USERS REACHING THEIR FITNESS GOALS! MORE ● Workout safely without fear of injury!
- 8. David K. Kirui Ph.D, Sociology M.S., Statistics 8
Editor's Notes
- Hi everyone, I’m David and I’m excited to share TrainAware, a research-based solution that uses machine learning to help exercise enthusiasts avoid overexertion and injury.
- This is Ed. Ed works out regularly and tries to stays active but recently suffered a workout related injury that has kept him away from his fitness goals. Ed is not alone. Every day, there are more than 10,000 people treated in emergency rooms across the country for injuries stemming from sports, recreation, and exercise. I too have suffered a workout related injury. Injuries keep people from reaching their fitness and wellness goals and can be very discouraging, especially for beginners. Moreover, the global fitness industry is valued at around $80 billion annually.
- To develop TrainAware, I used panel data from a fitness tracking and wellness company called Welltory. This data included daily fitness and biometric measures from 68 people over a four-month period. I used this data to forecast their chance of sustaining a workout-related injury. The biggest challenge that I had to overcome in this process was that there was no measure of overtraining in the raw data. Luckily, I had a measures of heart rate variability and calories burned from activity, which research has shown can be used to approximate exercise load and indicate whether an individual is over-exerting themselves. I used these features to engineer time-dependent measures of exercise load and overexertion. I also wrangled a number of other features, and did date-time conversions to ensure the data were in a suitable form for analysis. I imputed or interpolated missing values in a way that accounted for the longitudinal nature of the data.
- Next, I preprocessed the data. This included one-hot encoding categorical features, and standardizing continuous features. I then employed the Extreme Gradient Boosting (XGBoost) algorithm to build a classifier to indicate whether or not someone will overtrain. This processed involved automated hyperparameter tuning and cross-validation using the GridSearch function in SKlearn, contending with imbalanced classes on my target, and evaluating model performance in the test data. Lastly, I developed TrainAware via Streamlit and Python - in order to do this I had to transform normalized features back to their original scale so that my model could predict the probability of overtraining based on user-specified inputs. Once TrainAware was finalized, I deployed it to the cloud via an AWS EC2 instance.
- One of the nice things about the XGBoost algorithm is that you can get measures of which features matter the most in forecasting your target. Research suggests that measures of heart rate variability and training load have the highest predictive power for forecasting over-exertion, so it’s not surprising that beats per minute (BPM) and workload ratio were among the most important features. This also makes for a good sanity check. Interestingly, blood oxygen saturation also matters in forecasting over-exertion.
- So how does TrainAware perform? The classifier has 92% Recall - meaning algorithm is highly accurate when forecasting that a user will not sustain injury. THis is important because I want to avoid false negatives - a situation where the app tells the user they won’t overtrain, so continue working out and then overtrain and get injured. The algorithm behind TrainAware is a 25% improvement over the baseline, which was a model without either engineered feature of heart rate variability or exercise load. -Ultimately, this will lead to more users reaching their fitness goals SAFELY and without the fear of injury!
- So to wrap up, TrainAware is a web app that helps users reduce their chance of overexertion and possibly sustaining a work-out related injury. This will help users maximize their workouts and minimize their risk of sustaining an injury that can keep them away from their goals.
- I am a quantitative social scientist that with lots experience deriving actionable insights from complex, noisy, and messy data. I am excited to start my career in data science and am passionate about leveraging the power of data to gain new insights into some of the most complex and vexing problems facing companies today. I love to travel and stay active and am ready to apply that curiosity, consistency, accountability, and goal-driven attitude to your team’s future success.