ClimbTheWorld: Real-time stairstep counting to increase physical activity
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Thank You for referencing this work, if you find it useful! Reference/Citation: F. Aiolli, M. Ciman, M. Donini, O. Gaggi: "ClimbTheWorld: Real-time stairstep counting to increase physical activity", In Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 14), London, Great Britain, December 2014
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ClimbTheWorld: Real-time stairstep counting to increase physical activity
- 1. 04 November 2014 MOBIQUITOUS 2014, London ClimbTheWorld: Real-time stairstep counting to increase physical activity Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi Department of Mathematics, University of Padua, Italy {aiolli, mciman, mdonini, gaggi}@math.unipd.it
- 2. Health problem 04 November 2014 MOBIQUITOUS 2014, London 2 of 25
- 3. Wrong lifestyle 04 November 2014 MOBIQUITOUS 2014, London 3 of 25 Sedentary life and bad nutrition are increasing overweight people Increasing number of diseases like diabetes, cancer etc. Higher medical costs
- 4. The Fun Theory 04 November 2014 MOBIQUITOUS 2014, London 4 of 25 Piano Stairs experiment, Stockholm
- 5. Related works Use mobile and ubiquitous devices to tackle portability issues Many serious games, gamification systems and activity recognition to incentivize people to live more actively Three main problems Fixed position of the smartphone High energy consumption Not always in real-time 04 November 2014 MOBIQUITOUS 2014, London 5 of 25
- 6. ClimbTheWorld 04 November 2014 MOBIQUITOUS 2014, London 6 of 25
- 7. Goals Main features: Identify stairstep and distinguish them from walking step 04 November 2014 MOBIQUITOUS 2014, London 7 of 25 A stairstep A step
- 8. Goals 04 November 2014 MOBIQUITOUS 2014, London 8 of 25 Main features: Identify stairstep and distinguish them from walking step Support for (partial) orientation independence Segmentation vs Sliding windows Energy consumption analysis
- 9. Pipeline 04 November 2014 MOBIQUITOUS 2014, London 9 of 25
- 10. Data standardization 04 November 2014 MOBIQUITOUS 2014, London 10 of 25 DATA STANDARDIZATION
- 11. Orientation independence Problem: accelerometer data changes depending on the orientation of the smartphone 04 November 2014 MOBIQUITOUS 2014, London 11 of 25
- 12. Orientation Independence First proposal: Mizell in 2003 1. Take a window of data of fixed time length 2. Estimate the gravity component g=(gx , gy , gz) averaging the readings of the window 3. Calculate dynamic component as: d=(ax – gx , ay – gy , az – gz) for every reading a=(ax , ay , az) 4. Calculate vertical component p= 𝑑 ∙ 𝑚 𝑚 ∙ 𝑚 𝑚 5. Horizontal component h = d - p 04 November 2014 MOBIQUITOUS 2014, London 12 of 25
- 13. Orientation independence 1. We use a buffer of accelerometer data of the last 500ms 2. We estimate the gravity component g = (gx , gy , gz) as mean value of the buffer readings 3. We calculate the real movement d=(ax – gx , ay – gy , az – gz); 4. Using data from the rotation sensor, we rotate d into d’ to a fixed coordinate system 04 November 2014 MOBIQUITOUS 2014, London 13 of 25
- 14. Orientation independence Step 1, 2, 3 of gravity estimation and real movement estimation are natively supported using the Linear sensor. 04 November 2014 MOBIQUITOUS 2014, London 14 of 25 Our solution Native solution
- 15. Segmentation 04 November 2014 MOBIQUITOUS 2014, London 15 of 25 TIME FILTER NO_STAIR MAYBE_STAIR SEGMENTATION
- 16. Segmentation A stair step has a specific pattern in the fixed coordinate system Instead of using sliding window, we segment data Energy reduction Time becomes a feature Easier learning task User variability 04 November 2014 MOBIQUITOUS 2014, London 16 of 25
- 17. Segmentation 04 November 2014 MOBIQUITOUS 2014, London 17 of 25
- 18. Features & Classification 04 November 2014 MOBIQUITOUS 2014, London 18 of 25 VECTORIAL REPRESENTATION FEATURES STANDARDIZATION CLASSIFICATION STAIR or NO_STAIR COUNTING
- 19. Features Basic features to reduce energy consumption FFT coefficients could be computationally expensive 74 different values, like average, STD, variance, Signal Magnitude Area For the Mizell approach, features becomes 74x2 = 148 04 November 2014 MOBIQUITOUS 2014, London 19 of 25
- 20. Test Data collected from 7 different users with their own smartphone 8000 windows, 1500 stairsteps We test Mizell method, Linear method and our solution at three different frequencies: 20Hz, 30Hz and 50Hz Learning algorithms: Decision Tree, kNN and KOMD 04 November 2014 MOBIQUITOUS 2014, London 20 of 25
- 21. Results 04 November 2014 MOBIQUITOUS 2014, London 21 of 25 0.65 0.7 0.75 0.8 0.85 0.9 Mizell Linear Our Method Mizell Linear Our Method Mizell Linear Our Method 20Hz 30Hz 50Hz F-score DT KNN KOMD
- 22. Energy consumption Energy consumption is a big problem and one of the most important aspect for final users The best approach is the one that combines low energy and high precision 04 November 2014 MOBIQUITOUS 2014, London 22 of 25 Power Monitor to measure consumed energy
- 23. Energy consumption – Data Stand. 04 November 2014 MOBIQUITOUS 2014, London 23 of 25 7200 7400 7600 7800 8000 8200 8400 8600 8800 9000 20Hz 30Hz 50Hz Energy Consumption (uAh) Mizell Linear Our Method
- 24. Sliding window vs Segmentation 04 November 2014 MOBIQUITOUS 2014, London 24 of 25 11500 12000 12500 13000 13500 14000 14500 15000 Sliding window Data segmentation Energy consumption (uAh) About 1hour saved
- 25. Conclusions Real-time stairstep counter to increase physical activity during everyday life Main features Partial support for orientation independence Data segmentation for energy consumption reduction Energy efficiency as key aspect of design Future works: Use history to increase overall precision of the system Support for trousers pocket 04 November 2014 MOBIQUITOUS 2014, London 25 of 25
- 26. 04 November 2014 MOBIQUITOUS 2014, London ClimbTheWorld: Real-time stairstep counting to increase physical activity Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi Department of Mathematics, University of Padua, Italy {aiolli, mciman, mdonini, gaggi}@math.unipd.it