03-09-2012, 12:45 PM
A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing
[attachment=33188]
Abstract
Objective and automatic sensor systems to monitor
ingestive behavior of individuals arise as a potential solution to
replace inaccurate method of self-report. This paper presents a
simple sensor system and related signal processing and pattern
recognition methodologies to detect periods of food intake based on
non-invasive monitoring of chewing. A piezoelectric strain gauge
sensor was used to capturemovement of the lower jaw from 20 volunteers
during periods of quiet sitting, talking and food consumption.
These signals were segmented into non-overlapping epochs
of fixed length and processed to extract a set of 250 time and frequency
domain features for each epoch. A forward feature selection
procedure was implemented to choose the most relevant features,
identifying from 4 to 11 features most critical for food intake
detection. Support vector machine classifiers were trained to create
food intake detection models. Twenty-fold cross-validation demonstrated
per-epoch classification accuracy of 80.98% and a fine time
resolution of 30 s. The simplicity of the chewing strain sensor may
result in a less intrusive and simpler way to detect food intake. The
proposed methodology could lead to the development of a wearable
sensor system to assess eating behaviors of individuals.
INTRODUCTION
OVERWEIGHT and obesity, defined as the abnormal
or excessive body fat accumulation, is dramatically
expanding from high-income countries to low and middle-income
countries, especially in urban settings. The World Health
Organization estimated that the overweight adult population
would increase from 1.5 billion in 2008 to 2.3 billion in 2015
and that obese adult population would rise from 500 to 700
million worldwide during the same period [1].
Food Intake Detection
Objective and automatic methods of MIB based on wearable
sensors and/or portable devices were introduced as a potential
solution to replace the manual self-reporting methods. MIB
methods are being developed to measure periods of food intake
with minimal individual’s active participation, which may
lead to a better understanding of eating behaviors by improving
the accuracy of energy intake estimation, reducing the underreporting
and relieving the subject from the recording burden.
Incorporation of newtechnology helped participants to automatically
report food consumption [11]. In [12], custom designed
software was integrated into a mobile phone with a camera to
capture images of foods before and after the meal as well as
to include additional food information using voice record. In
[13], a similar methodology for mobile phone food record was
proposed. A total of 79% of adolescents participating in device
evaluation agreed that the software was easy to use. These automatic
dietary monitoring methodologies showed to increase
the accuracy of food intake but they still rely on individuals
taking useful images and self-reporting all consumed foods. In
[14], a wearable device that integrates a miniature camera, a
microphone and several other sensors (accelerometers, reference
lights, etc) for recording food intake was presented. The
device is currently under development and evaluation
RESULTS
The size of the epoch that best represented the chewing signals
was selected from three different values. Results of 20-fold
cross-validation indicated that a 30 s epoch size presented higher
accuracy than 15 s and 60 s epoch sizes. In all cases, the classifiers
were trained with features from the current epoch plus
features from one adjacent epoch data (lag ) to account
for the time variability of the chewing signal. Fig. 5 illustrates an
improvement in the classification accuracy for the three epoch
sizes as more features were added to the training vector. No further
improvement was observed after the training vector comprised
a certain number of features. For an epoch size of 30 s,
only 8 features were needed to reach the maximum accuracy
value of 80.98% whereas for an epoch size of 15 s the accuracy
increment stopped after 11 features were added. When the epoch
size increased up to 60 s, the classifier discriminated ’chewing’
and ’no chewing’ epochs with an accuracy of 78.18% using only
5 features although this value did not increase when more features
were included in the training vector.
CONCLUSION
This paper presented a sensor and signal processing and pattern
recognition methodology to detect periods of food intake
by monitoring characteristic jaw motion during food consumption.
The most relevant features representing the sensor signals
were chosen by a forward selection procedure. The population-
based model created was able to classify chewing epochs
with an averaged accuracy of 80.98% and a time resolution
of 30 s. This epoch-based classification approach would allow
the monitoring of short events of food consumption, such as
snacking. Practical implementation of this methodology in a
wearable device is highly possible due to the use of a simple
and inexpensive strain gauge sensor to monitor chews plus a
simple classification algorithm that can be easily implemented
in a microcontroller. The development of such wearable device
would allow the real-time monitoring of ingestive behavior
under free-living conditions.
[attachment=33188]
Abstract
Objective and automatic sensor systems to monitor
ingestive behavior of individuals arise as a potential solution to
replace inaccurate method of self-report. This paper presents a
simple sensor system and related signal processing and pattern
recognition methodologies to detect periods of food intake based on
non-invasive monitoring of chewing. A piezoelectric strain gauge
sensor was used to capturemovement of the lower jaw from 20 volunteers
during periods of quiet sitting, talking and food consumption.
These signals were segmented into non-overlapping epochs
of fixed length and processed to extract a set of 250 time and frequency
domain features for each epoch. A forward feature selection
procedure was implemented to choose the most relevant features,
identifying from 4 to 11 features most critical for food intake
detection. Support vector machine classifiers were trained to create
food intake detection models. Twenty-fold cross-validation demonstrated
per-epoch classification accuracy of 80.98% and a fine time
resolution of 30 s. The simplicity of the chewing strain sensor may
result in a less intrusive and simpler way to detect food intake. The
proposed methodology could lead to the development of a wearable
sensor system to assess eating behaviors of individuals.
INTRODUCTION
OVERWEIGHT and obesity, defined as the abnormal
or excessive body fat accumulation, is dramatically
expanding from high-income countries to low and middle-income
countries, especially in urban settings. The World Health
Organization estimated that the overweight adult population
would increase from 1.5 billion in 2008 to 2.3 billion in 2015
and that obese adult population would rise from 500 to 700
million worldwide during the same period [1].
Food Intake Detection
Objective and automatic methods of MIB based on wearable
sensors and/or portable devices were introduced as a potential
solution to replace the manual self-reporting methods. MIB
methods are being developed to measure periods of food intake
with minimal individual’s active participation, which may
lead to a better understanding of eating behaviors by improving
the accuracy of energy intake estimation, reducing the underreporting
and relieving the subject from the recording burden.
Incorporation of newtechnology helped participants to automatically
report food consumption [11]. In [12], custom designed
software was integrated into a mobile phone with a camera to
capture images of foods before and after the meal as well as
to include additional food information using voice record. In
[13], a similar methodology for mobile phone food record was
proposed. A total of 79% of adolescents participating in device
evaluation agreed that the software was easy to use. These automatic
dietary monitoring methodologies showed to increase
the accuracy of food intake but they still rely on individuals
taking useful images and self-reporting all consumed foods. In
[14], a wearable device that integrates a miniature camera, a
microphone and several other sensors (accelerometers, reference
lights, etc) for recording food intake was presented. The
device is currently under development and evaluation
RESULTS
The size of the epoch that best represented the chewing signals
was selected from three different values. Results of 20-fold
cross-validation indicated that a 30 s epoch size presented higher
accuracy than 15 s and 60 s epoch sizes. In all cases, the classifiers
were trained with features from the current epoch plus
features from one adjacent epoch data (lag ) to account
for the time variability of the chewing signal. Fig. 5 illustrates an
improvement in the classification accuracy for the three epoch
sizes as more features were added to the training vector. No further
improvement was observed after the training vector comprised
a certain number of features. For an epoch size of 30 s,
only 8 features were needed to reach the maximum accuracy
value of 80.98% whereas for an epoch size of 15 s the accuracy
increment stopped after 11 features were added. When the epoch
size increased up to 60 s, the classifier discriminated ’chewing’
and ’no chewing’ epochs with an accuracy of 78.18% using only
5 features although this value did not increase when more features
were included in the training vector.
CONCLUSION
This paper presented a sensor and signal processing and pattern
recognition methodology to detect periods of food intake
by monitoring characteristic jaw motion during food consumption.
The most relevant features representing the sensor signals
were chosen by a forward selection procedure. The population-
based model created was able to classify chewing epochs
with an averaged accuracy of 80.98% and a time resolution
of 30 s. This epoch-based classification approach would allow
the monitoring of short events of food consumption, such as
snacking. Practical implementation of this methodology in a
wearable device is highly possible due to the use of a simple
and inexpensive strain gauge sensor to monitor chews plus a
simple classification algorithm that can be easily implemented
in a microcontroller. The development of such wearable device
would allow the real-time monitoring of ingestive behavior
under free-living conditions.