This project uses a wrist-worn device to track wrist motion all day
and detect periods of time in which the person wearing the device is eating.
Our current work uses the
The Shimmer 3 device, but we have also processed data collected by the
Actigraph gt9x device, Apple iPhones, and others.
Detecting periods of time of eating
Shimmer 3
ActiGraph gt9x
Method
The below figure shows a plot of wrist motion energy (total amount of
linear acceleration) of a person for an entire day (7:30 AM to 7:30 PM).
The subject kept a record of periods of time of eating, which are indicated
by arrows along with the names of the meal (e.g. breakfast).
We discovered that eating periods tend to have lower values of wrist motion
energy surrounded by peaks of higher wrist motion energy.
The higher peaks are typically due to meal preparation and cleanup in which
the hands and wrists are moving a lot more than during actual consumption.
Our algorithm uses peaks to segment the data and then calculates additional
features between peaks to classify the periods of time.
Example output
The below figure shows an example of our classifier output.
The y-axis is wrist motion energy (total amount of
linear acceleration) and x-axis is time of day (10:30 AM to midnight).
The subject's self-reported times of eating for lunch and dinner are
indicated.
Arrows above wrist motion energy indicate the boundaries of time our
algorithm used to segment the data.
Classifier output (eating, other, rest or walking) is indicated below
the wrist motion energy.
Data
We have collected a very large data set (351 people, 1 day each)
of recordings like the one shown above.
See the
Clemson All-day
Dataset (CAD) webpage for information and download.
