Download Trend analysis of human activity data

Philips Research
Student project: Analysis and prediction of human activity and
behavioural data in a wellbeing program
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Philips Research Europe, User Experiences
Eindhoven, the Netherlands
dr. Steffen Pauws,
dr. Jan Korst,
dr.ir. Verus Pronk
[email protected]
040 – 27 47663
to be agreed
to be agreed
Introduction
The User Experiences group within Philips Research investigates the everyday use of a
wearable sensor system that assesses movement and mobility in real life or free-living
circumstances with minimal discomfort. The sensor system consists of a single small tri-axial
accelerometer that can be worn easily on the body. The device comes with a key cord and a
clip, so various ways of wearing are possible. It has been shown that there is a reliable and
consistent relationship between energy expenditure due to physical activity and acceleration
measured on the human body. A simple USB connection allows docking the sensor data to a
PC platform. The current configuration uses a long-term ambulatory data collection method in
which acceleration data are integrated over time periods of a single minute resulting in
activity counts per minute. This activity count is a strong correlate of energy expenditure (i.e.,
calories burnt by the human body). Recordings up to three weeks are possible without
recharging. In Figure 1, a plot is presented of a 24 hour recording of a person’s activity count.
Figure 1. Raw 24 hour activity count data of a single person.
Philips Research
The sensor system constitutes an important element of an ongoing physical activity coaching
intervention program aimed at increasing people’s daily life physical activity. In addition, the
intervention program includes various motivation modalities, for instance, an intake
assessment, a web site, regular communication and coaching facilities. At the intake and
during the course of the program, various participant data are collected including personal
characteristics (e.g., gender, age, health status), device diagnostics data (e.g.,
charging/docking times) and participants behaviour and program data (e.g., sensor system
wearing data, web site consultation, coaching intervention, targets and performance). Many
individual health / wellbeing programs require motivation for the participant to persevere,
especially if these activities require a behaviour change with a long-term commitment. As a
result, the drop-out rate is rather high in these programs.
Aim
The current student internship/graduation project aims at recognizing data patterns in a large
data space including ambulatory activity data, participant characteristics and participant
behaviour data to answer the following set of research questions (in order of priority):
1. How can we predict likely drop-outs of participants in a wellbeing coaching program
from their activity data, possibly in combination with participant characteristics,
device diagnostics data and participant behaviour data? It would be valuable, if we
are able to detect and explain that a given participant requires more attention to help
her persevere in the program, either right after the intake or during the course of
program.
2. How can we determine optimal coaching intervention moments to prevent
participants from dropping out of the program? It would be valuable to intervene,
remind or provide the right resources or incentives to participants in time in order to
facilitate a true behaviour change.
3. What part of the activity data can be typified as a baseline everyday physical activity
level and what can be identified as additional physical effort?
4. How does the participant behave in the program? For instance, does she handle a
sufficient level of discipline or does she behave more erratic?
Above research questions can be re-phrased as data mining, pattern recognition and/or time
series modeling problems. The number of solution methods (i.e., algorithms) for these
problems is large. A large anonymised data set is available for research. This set includes
ambulatory activity data for 84 days that is recorded from a tri-axial accelerometer worn in
daily life conditions under a wellbeing coaching program. In addition, participant
characteristics, device diagnostics data, website consultation data, coaching
intervention/program data and device wearing data are also logged.
Recruitment
We are looking for university students with an interest and understanding in data mining, machine
learning, pattern recognition and/or time series modelling, who want to do their internship or
graduation project at Philips Research. The student needs good programming skills for realizing the
project and good technical writing skills for reporting. Creativity, commitment, a proactive attitude and
own initiatives are important.
Students will work under guidelines applying at Philips Research in Eindhoven on a temporary
contract. For students living and studying in the Netherlands, allowance is determined at 330 euro per
month for an internship and 420 euro per month for a graduation project. Students do not get a
contribution in travel costs. For students living and studying abroad, other rules apply. Special
arrangement for summer student fellowship is also possible.
For more information about the project, please contact Steffen Pauws; [email protected]; 040
– 27 47663.