Unsupervised Activity Clustering to Estimate Energy Expenditure with a Single Sensor

1. Unsupervised Activity Clustering to Estimate Energy Expenditure with a Single Sensor Shanshan Chen, John Lach Marco Altini, JulienPenders Oliver Amft

2. Existing Solutions 2H218O BSN?

3. Research on Energy Expenditure (EE) Estimation with BSN • Detailed Activity Recognition (AR) + Metabolic Equivalents (METs) • Annotation labeling work at the development stage • Lots of sensors to wear for the users • Lack of accuracy due to static number of METs • Detailed AR + regression • Labeling work at the development stage • More inertial sensors needed for better recognition accuracy • Detailed AR  Grouped AR + regression • Reduced number of sensors – ECG + Accelerometer • Reduced challenges in high accuracy recognition • Data-driven clustering + regression • Bypass activity recognition • No labeling at the development stage

4. Proposed method Model1 Features from Data Group 1 • Focus on accurate EE estimation, not AR • Clustering based on motion and heart rate, not activities • Data-driven clustering • Apply regression model based on data cluster • Unsupervised learning • No need to label activities during development stage • EE accuracy independent of AR accuracy Model 2 Group 2 Clustering Model N Group N

5. Experiment Setup • Single sensor node data (acceleration + heart rate) and validation data (circulatory calorimeter) collection • 10 subjects of various BMI • 52 types of activities (sedentary activities and physical exercises)

6. Feature Extraction -- Preprocessing • Heart rate • Removing the motion artifact • Count peaks every 15 seconds • Extract heart rate above rest • Acceleration features extraction • 4 seconds time window • 18 features extracted in total Feature Extraction Machine Learning

7. Framework of Machine Learning Feature Selection (LASSO) Multiple Linear Regression 19 Features Dimension Reduction

8. Model Comparison • Proposed model • Apply different regression models to different data clusters • Single multiple-linear regression model • Also activity-oblivious • Single regression model • AR-based model (Grouped AR + Regression) • Perfectly separated based on known activity labels • Non-ideally separated based on AR algorithms

9. Regression Results • Proposed model is better than the single regression model • With perfect labeling, activity specific model is the best • However, accuracy of AR based method drop quickly when misclassification happens

10. Future Work • Explore other unsupervised learning techniques • Study interpretations of clusters • Histogram of activities inside each cluster • Real-time implementation • Monitoring intensive activities only to save battery • Greater subject diversity • Combine with emerging energy intake techniques

11. Conclusion • Data-driven clustering for EE estimation • One light-weight sensor patch, easy for the users to wear • No labeling of activities at the development stage • Final estimation accuracy does not depend on accuracy of AR • Improve linear regression model and AR based clustering • Drawback: • Does not track activities – orthogonal problem of accurate energy expenditure estimation

12. Thanks! ?

13. Histogram of Activities in Clusters å

14. Clustering Results Training set clustering Testing set clustering

15. Physical Activities Comparison • Physical activities are more interesting to monitor instead of the sedentary ones • The proposed model achieves almost as good accuracy as activity specific model