9. Physical activity recognition Decision tree model Classification accuracy > 93% Cycling Active Standing Active Standing Walking Walking Running Lying Sedentary
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Notas del editor
Good afternoon ladies and gentlemen. Welcome colleagues, friends and family to the public defense of my PhD thesis titled: “Physical activity recognition using a wearable accelerometer – new perspectives for energy expenditure estimation and health promotion”
A physically active lifestyle plays a key role for living a healthy life. Indeed, regular participation in physical activity is associated with numerous health benefits essential for reducing the risk of adverse health conditions, such as cardiovascular diseases, type II diabetes, and cancer. Furthermore, physical activity increases energy expenditure, which plays a fundamental role in the regulation of body weight and the development of obesity, currently an epidemic of global concern.
However it is still unclear How much physical activity is needed to gain health benefits, the reason is mainly the lack of objective and accurate methods to measure physical activity and the related physiological responses in free-living conditions.
Objective methods to measure physical activity have been developed to establish the relationship between body movement, energy expenditure and health. Body movement is generated by the contraction of skeletal muscle for performing physical activity and results in energy expenditure. Energy expenditure is composed by three main parts (as presented in the figure): the part due to basal body functions, the part related to diet, the energy required to digest and process food; and the third part of the daily energy expenditure is related to activity, which mainly depends on body movement and on the size of the body displaced during physical activity. This part of the daily energy expenditure, Activity energy expenditure, represents the physiological response to physical activity from which positive health outcomes are derived.
Wearable accelerometers have been developed to objectively measure physical activity in free-living conditions. One of these is the DirectLife activity monitor. Some of its major characteristics are the high unobtrusiveness, as you can see in this picture it has small dimensions and it has a low battery consumption allowing long operational capacity.
The device objectively measures body movement in 3 directions, in the vertical, forward and sideward directions of the body, by using a tri-axial acceleration sensor and summarize body movement in activity counts.
This measure of physical activity showed to predict, together with body weight, activity energy expenditure in free-living subjects as compared to the gold-standard technique for measurements of energy expenditure in the field. As you can see for the figure, there was a high association between measured and predicted activity energy expenditure.
Wearable accelerometers not only quantify body movement in activity counts, but can also be used for physical activity recognition. Physical activity recognition consists in the automatic detection of physical activity types using information collected during body movement. In this picture it is presented the acceleration signal measured at the waist during different types of activities. The picture shows the typical acceleration pattern for sitting, standing, sweeping the floor, walking and cycling. The different characteristics of these patterns can be used to generate the knowledge for identifying activity types in free-living individuals.
The information carried by characteristics of the acceleration pattern can be used by a decision tree algorithm for physical activity recognition. Using data collected in a large population of subjects a decision tree model was developed to evaluate characteristics of the acceleration signal and identify 6 common activity types: lying, sedentary, walking, running, cycling, and active standing activities. The classification accuracy achieved was above 93%.
This activity recognition technique was applied to improve estimation of energy expenditure based on wearable accelerometers. Indeed, the relation between body movement and energy expenditure depends on the activity type.
So we investigated whether physical activity recognition can improve the assessment of energy expenditure as compared to activity counts. A parameter descriptive of the metabolic cost for physical activity, called METD, was developed by measuring the daily duration of activity types and the related metabolic expenditure. This METD parameter was compared to activity counts in estimating activity energy expenditure in free-living conditions. As displayed in the table, the estimation error of AEE based on METD, the parameter based on activity recognition, and body weight was 0.85 MJ/d, using activity counts and body weight the estimation error was higher 0.98 MJ/day. This shows how physical activity recognition can improve estimation accuracy of energy expenditure as compared to the activity-type independent method to measure body movement.
Physical activity recognition was also used to investigate the relationship between the activity behavior, defined by the daily duration of different activity types, and the physical activity level, a parameter related to daily energy expenditure in a sample of the Dutch population.
It appeared that 7% of the daytime was occupied for walking, cycling and running on average, and the sedentary time occupied almost 30% of the day. By analyzing the variability in physical activity level and the variability in the duration of the different activity types it was shown that sedentary time and the daily time spent for walking and cycling were the major determinant of the physical activity level. This information can be used to model the physiological responses to interventions aimed at increasing physical activity.
Indeed, it was possible to model that on average replacing 30 min day of sitting in a car with cycling can increase PAL and the total energy expenditure by 10%. In a group of obese subjects measured before and after a diet, weight loss induces decreases in AEE due to the lower body weight carried during physical activity. it was observed that a behavioral change consisting of 2h reduction of sedentary time for example by increasing generic activities and walking was necessary to restore baseline AEE.
Activity recognition was also used to determine the relationship between the activity behavior and heart rate variability as index of risk for cardiovascular health. Indeed depressed HRV has been frequently associated with the onset of cardiovascular diseases. HRV is negatively influenced by age and body weight. However, the independent relationship between physical activity and HRV is still unknown. It was observed that, independently of age and body weight, heart rate variability indexes were positively associated with the time spent in low-intensity activities as defined by the active standing type. In obese subjects the increased engagement in active standing was significantly associated with better index HRV, independently of age and body. It was hypotesized that if a cause effect relationship exists between the engagement in active standing and HRV indexes exists it could involve an improved baroreflex sensitivity and higher baroreflex sensitivity, which are factors negatively influenced by insulin sensitivity and hyperinsulinemia.
Indeed, it was also observed an association between the engagement in the activity standing type and fasting plasma insulin concentration, suggesting a positive effect of this activity type in improving metabolic profile by diminishing physical inactivity.
In conclusion: 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
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