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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
Health problem
04 November 2014
MOBIQUITOUS 2014, London
2 of 25
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
The Fun Theory
04 November 2014
MOBIQUITOUS 2014, London
4 of 25
Piano Stairs experiment, Stockholm
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
ClimbTheWorld
04 November 2014
MOBIQUITOUS 2014, London
6 of 25
Goals
Main features:
 Identify stairstep and distinguish them from
walking step
04 November 2014
MOBIQUITOUS 2014, London
7 of 25
A stairstep A step
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
Pipeline
04 November 2014
MOBIQUITOUS 2014, London
9 of 25
Data standardization
04 November 2014
MOBIQUITOUS 2014, London
10 of 25
DATA STANDARDIZATION
Orientation independence
Problem: accelerometer data changes depending on
the orientation of the smartphone
04 November 2014
MOBIQUITOUS 2014, London
11 of 25
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
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
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
Segmentation
04 November 2014
MOBIQUITOUS 2014, London
15 of 25
TIME
FILTER
NO_STAIR
MAYBE_STAIR
SEGMENTATION
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
Segmentation
04 November 2014
MOBIQUITOUS 2014, London
17 of 25
Features & Classification
04 November 2014
MOBIQUITOUS 2014, London
18 of 25
VECTORIAL
REPRESENTATION
FEATURES
STANDARDIZATION
CLASSIFICATION
STAIR
or
NO_STAIR
COUNTING
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
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
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
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
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
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
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
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

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Unit-V; Pricing (Pharma Marketing Management).pptx
 

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
  • 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
  • 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
  • 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