physical activity recognition using a wearbale accelerometer

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Public defense of my PhD thesis

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Page 1: Physical activity recognition using a wearbale accelerometer
Page 2: Physical activity recognition using a wearbale accelerometer

Physical activity and health

• A physically active lifestyle:

Reduces risk for adverse health conditions– Cardiovascular diseases, type 2 diabetes, cancer

Increases energy expenditure– Plays a role in the regulation of body weight

Page 3: Physical activity recognition using a wearbale accelerometer

Physical activity and health

• A physically active lifestyle:

Reduces risk for adverse health conditions– Cardiovascular diseases, type 2 diabetes, cancer

Increases energy expenditure– Plays a role in the regulation of body weight

• How much physical activity is needed to gain health benefits?

Page 4: Physical activity recognition using a wearbale accelerometer

Ene

rgy

expe

nditu

re

Measuring physical activity• Objective methods are required to establish the

relationship between:

Energy expenditureBody movement Health

Activity

Basal

Diet

Page 5: Physical activity recognition using a wearbale accelerometer

Wearable accelerometer

• DirectLife activity monitor – unobtrusive, small – low battery consumption

Page 6: Physical activity recognition using a wearbale accelerometer

Wearable accelerometer

• DirectLife activity monitor – unobtrusive, small– low battery consumption

• Measures body movement – activity counts Vertical

Forward

Sideward

Page 7: Physical activity recognition using a wearbale accelerometer

Wearable accelerometer

• Predicts activity energy expenditure

Measured expenditure, MJ/day

Pre

dict

ed e

xpen

ditu

re,

MJ/

day

Explained variation = 53%

Page 8: Physical activity recognition using a wearbale accelerometer

Physical activity recognition

Walking CyclingWalking

Standing

SittingFloor sweeping

Acceleration signal

Page 9: Physical activity recognition using a wearbale accelerometer

Physical activity recognition

Cycling

Active Standing

Active Standing

Walking

Walking

Running Lying Sedentary

Decision tree model

Classification accuracy > 93%

Page 10: Physical activity recognition using a wearbale accelerometer

Energy expenditure estimation

• Relation between body movement and energy expenditure depends on the activity type

Page 11: Physical activity recognition using a wearbale accelerometer

Energy expenditure estimation

• Relation between body movement and energy expenditure depends on the activity type

• Activity recognition improves the estimation of activity energy expenditure– METD, metabolic cost of physical activity based on activity types

– activity counts

Page 12: Physical activity recognition using a wearbale accelerometer

Behaviour and the activity level

• Objectively measured physical activity behaviour– Sample of the Dutch population

Page 13: Physical activity recognition using a wearbale accelerometer

• Objectively measured physical activity behaviour– Sample of the Dutch population

Behaviour and the activity level

Walk 5.3%Cycle 1.6%Run 0.2%

Active standing

27%

Sedentary29%

Sleep37%

7%

Page 14: Physical activity recognition using a wearbale accelerometer

• Objectively measured physical activity behaviour– Sample of the Dutch population

• On average, replacing 30 min/day of sitting with cycling increases daily energy expenditure by 10%

– Obese subjects measured before and after a diet

• After weight loss, activity energy expenditure decreases mainly because of the lower body weight carried during physical activity

• reducing 2 hours/day of sedentary time restores baseline activity energy expenditure

Behaviour and the activity level

Page 15: Physical activity recognition using a wearbale accelerometer

Cardiovascular health

• In a group of obese subjects the time spent on active standing is associated with:– better index of heart rate variability

Increase in active standing, min/day

Adj

uste

d H

RV

, lo

g m

s2

Explained variation = 62%

Page 16: Physical activity recognition using a wearbale accelerometer

Cardiovascular health

• In a group of obese subjects the time spent on active standing is associated with:– Lower plasma insulin concentration

Active standing, min/day

Pla

sma

insu

lin,

mm

ol/L

Explained variation = 18%

Page 17: Physical activity recognition using a wearbale accelerometer

Conclusions

Physical activity recognition:

• was achieved using a single wearable accelerometer

• Improved the estimation of activity energy expenditure

• unraveled the relation between body movement and health with respect to cardiovascular diseases prevention

Page 18: Physical activity recognition using a wearbale accelerometer