=Paper=
{{Paper
|id=Vol-2903/IUI21WS-HEALTHI-10
|storemode=property
|title=Personalized Meal Classification Using Continuous Glucose Monitors
|pdfUrl=https://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-10.pdf
|volume=Vol-2903
|authors=Projna Paromita,Theodora Chaspari,Seyedhooman Sajjadi,Anurag Das,Bobak J. Mortazavi,Ricardo Gutierrez-Osuna
|dblpUrl=https://dblp.org/rec/conf/iui/ParomitaCSDMG21
}}
==Personalized Meal Classification Using Continuous Glucose Monitors==
https://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-10.pdf
Personalized Meal Classification
Using Continuous Glucose Monitors
Projna Paromitaa , Theodora Chasparia , Seyedhooman Sajjadia ,
Anurag Dasa , Bobak J. Mortazavia and Ricardo Gutierrez-Osunaa
a Dept. of Computer Science & Engr., Texas A&M University
Abstract
Managing diabetes mellitus (DM) requires monitoring the glucose response to meals, also known as
the postprandial glucose response (PPGR). The PPGR to a meal is significantly affected by the amount
of carbohydrates, but other macronutrients (e.g., protein, fat, fiber) are also known to affect the PPGR.
This suggests that the type of meal consumed can be automatically identified by analyzing the shape
of the PPGR, as measured by a continuous glucose monitor (CGM). As a step towards this goal, this
study proposes a metric-learning approach to learn personalized PPGR embeddings to account for the
inherently large inter-individual variability in PPGRs. Metric learning is implemented with a Siamese
neural network (SNN) that models the relative distance between meals consumed by a participant. Em-
beddings learned with the SNN outperform features directly extracted from PPGRs, yielding 50% and
77% accuracy on the considered tertiary and binary meal classification tasks, respectively. Findings from
this work would ultimately help in designing intelligent user interfaces for assisting patients with DM
in dietary monitoring.
Keywords
Personalization, metric learning, inter-individual variability, continuous glucose monitors, postprandial glucose
response
1. Introduction though new technologies based on food pho-
Diabetes mellitus (DM) is a chronic progres- tography and wearable sensors are also be-
sive metabolic disorder that requires signif- ing investigated, each with their own pros
icant self-management, including nutrition, and cons in terms of accuracy and obtru-
exercise, and medication [1]. An impor- siveness [2, 3, 4, 5, 6, 7]. For instance, di-
tant aspect of daily DM management lies in etary monitoring through memory recall can
controlling patients’ postprandial glucose re- be burdensome and is confounded by pa-
sponses (PPGR), mostly by monitoring diet. tients’ bias [8]. Food photography can be
Current forms of dietary monitoring rely less burdensome, therefore promoting user
largely on manual input and memory recall, adherence [8, 9]. However, food photogra-
phy methods still require manual patient in-
put, which can be noisy and potentially re-
Joint Proceedings of the ACM IUI 2021 Workshops, April
13–17, 2021, College Station, USA sult in missing values, especially in the case
" projna.paromita@tamu.edu (P. Paromita); of smaller meals or snacks [9, 10]. The pho-
chaspari@tamu.edu (T. Chaspari); tos collected via food photography also need
hooman130@tamu.edu (S. Sajjadi); adas@tamu.edu (A.
to be analyzed by an expert or an automated
Das); bobakm@tamu.edu (B.J. Mortazavi);
rgutier@tamu.edu (R. Gutierrez-Osuna) machine learning algorithm to obtain food
intake estimates [11].
© 2021 Copyright for this paper by its authors. Use permit-
ted under Creative Commons License Attribution 4.0 Inter- Continuous glucose monitors (CGMs) use
national (CC BY 4.0).
CEUR
http://ceur-ws.org
CEUR Workshop Proceedings is common in type I diabetes, and is rapidly
(CEUR-WS.org)
Workshop ISSN 1613-0073
Proceedings
increasing in type II diabetes, the most com- within each participant, rather than absolute
mon form of the disease [12]. CGMs can be patterns, therefore taking into account indi-
a valuable source of information to monitor vidual differences. Metric learning is imple-
diet accurately and in a relatively unobtru- mented with a SNN that learns a PPGR trans-
sive manner by measuring PPGRs, therefore formation to minimize the distance between
they have the potential to accurately capture samples of the same meal consumed by an in-
even small meals [13, 14]. CGMs can also dividual and maximize the distance between
help researchers gain valuable insights into different meals. The proposed pairwise simi-
the complex interplay between PPGRs and larity measure does not require us to estimate
the macronutrient composition of a given the distribution of PPGRs for each type of
meal, due to the fact that the shape of PP- meal, so it can it is suitable for small-sample-
GRs depends on the macronutrient compo- size applications [22]. As an additional step
sition of a meal [15, 16, 17, 18, 19]. For ex- toward personalization, we combine partici-
ample, carbohydrate-rich meals generally re- pants’ anthropometric and metabolic infor-
sult in PPGRs with high and narrow peaks, mation with the learned personalized em-
whereas meals rich in fat cause wider peaks, beddings of the SNN. We evaluate our ap-
and meals rich in protein depict lower and proach on a publicly-available dataset with
moderately broader peaks [20]. However, three types of meals. Results indicate that
the anthropometric and metabolic character- PPGR transformations learned from the pro-
istics of a person also significantly affect PP- posed metric-learning approach outperform
GRs, leading to large inter-individual vari- the conventionally used PPGR statistics for
ability to identical meals [20, 21]. This paper the task of meal classification. Namely, in-
proposes an algorithm to automatically iden- tegrating participants’ anthropometric and
tify meals by analyzing PPGRs collected by metabolic characteristics into classifier fur-
CGM devices. Such way of meal back track- ther increases its accuracy in certain cases.
ing, along with the popularly used methods
of meal tracking would help us to build a 2. Prior work
fool-proof application for proper diet moni- Computational models for CGM signals have
toring. primarily focused on predicting hyper- or
We propose a metric learning approach hypo-glycemic episodes [13, 23]. Prior work
to achieve personalized meal classification has further examined the prediction of the
based on PPGRs. The objective is to learn PPGR given a specific meal [20, 24, 25, 26, 27].
a transformation that embeds a glucose re- Zeevi et. al. showed that personal informa-
sponse while considering within-person dis- tion related to dietary habits, physical activ-
tances of consumed meals. For this reason, ity, and gut microbiota can improve the pre-
we design a metric learning approach, im- diction of PPGRs for specific meals, which
plemented with a Siamese neural network has implications to personalized diet educa-
(SNN) architectures, that 1) learns a trans- tion interventions [20]. However, the inverse
formation that projects PPGRs for a partic- problem of predicting macronutrients from
ipant that belong to similar meals to the PPGRs has not been thoroughly examined,
same region in the new feature space, while 2 except for our own work [18, 19]. Anurag et
also projecting samples from different meals al. proposed a sparse decomposition model
to non-proximal regions of the new fea- for representing PPGRs, while Sajjadi et al.
ture space. Our approach models pairwise explored the use of machine learning mod-
distances between different types of meals
els for predicting the amount of macronutri-
ents in meals. Both works were evaluated in
a set of 15 participants who consumed 9 pre-
designed meals with promising results.
Current methods for dietary monitoring
primarily rely on computer vision algorithms
that seek to detect the components of a meal
by analyzing its photographs [4, 5, 6, 28]. Figure 1: Average PPGR per meal averaged
However, food photography presents chal- across participants for 140-minute analysis win-
lenges for vulnerable populations, such as el- dow (𝐴𝑊 ).
derly adults, who may face challenges related
to dexterity, coordination, and vision when which is a significant benefit compared to
using mobile devices [29]. In addition to food other algorithms that tend to be more “data-
photography, other works have proposed hungry." In addition, the resulting model con-
wearable food monitoring technologies that tains a small number of parameters, therefore
rely on smart utensils and wearable sen- it does not have large memory requirements
sors [7, 30]. These approaches tend to pro- or computational cost, and could be imple-
vide reliable measurements of the food quan- mented as part of edge computing technolo-
tity, but they cannot estimate the macronu- gies, thus also promoting aspects of privacy
trient composition. In addition, smart uten- preservation.
sils might not always be readily available to
the user, therefore potentially resulting in
3. Data Description and
missing data. Pre-Processing
Given the expanding use of CGMs in dia- We used a publicly available dataset of PP-
betic populations, our method can contribute GRs contaning data from 30 participants (25-
to providing a feasible alternative to current 65 years) [21]. Individual glucose measure-
food monitoring methods. Overall, our con- ments were collected 5 minutes apart using
tribution to the existing literature is as fol- a Dexcom G4 device. Each participant con-
lows: (1) While most studies have focused on sumed three types of standardized meals at
predicting one’s PPGR based on a given meal, most twice, resulting in at most six meal
the inverse problem of estimating the con- samples per participant. The meals were
stituents of a meal based on the PPGR is rela- isocaloric, but varied in the amount of pro-
tively unexplored. Limited prior work from tein, fat, and fiber [21]. The three meals were:
our group has investigated the problem of (1) cornflakes and milk (CF), which were low
estimating macronutrient composition from in fiber and high in carbohydrates; (2) peanut
PPGRs [31, 18, 19], but meal classification butter sandwich (PB), which had a high
from PPGRs has not been yet examined; and amount of fat and protein; and (3) a PRO-
(2) in contrast with the majority of work, BAR protein bar (Bar), which had moderate
which relies on modeling class-wise distri- amounts of fat and protein. Fig. 1 illustrates
butions of PPGRs [20, 24, 25], our work pro- the average PPGR across individuals for each
poses a metric learning approach that mod- of the three meals, which indicates that there
els the pairwise distance between different are marked differences in PPGRs across the
meals consumed by each participant. The three types of meals. The data further include
proposed metric learning approach does not participants’ anthropometric characteristics,
require a large number of labelled samples,
including age and body mass index (BMI),
as well as metabolic characteristics, such as
insulin, fasting blood glucose (FBG), an oral
glucose tolerance test (OGTT), Hemoglobin
A1c (HbA1C), high sensitive C-reactive pro-
tein (hsCRP), and triglyceride to high-density
lipoprotein (HDL) ratio (tri/HDL). Anthro-
pometric and metabolic characteristics were
combined with the PPGR features and com- Figure 2: PPGR signals and family of Gaussian
kernels centered at five-time points and used to
prised the input of the proposed machine
compute the area under the curve (AUC) of the
learning models as an additional step to per- PPGR.
sonalization.
Prior to feeding PPGRs to the SNN, we by subtracting its minimum value and divid-
performed data-cleaning procedures, includ- ing by the difference between the maximum
ing linear interpolation of missing samples and minimum value. Finally, we extracted
and baseline correction. Baseline correction the area under the curve (AUC) at 3, 5, and 8
was done by subtracting the mean of the distinct time points, following Huo et.al. [31],
first 6 data points prior to meal consumption, resulting in a total of 16 features. We experi-
which we assumed served as the fasting glu- mented with these features extracted both in
cose level. Following prior work [20, 21, 31], the normalized and non-normalized PPGRs.
we also experimented with various analy- AUCs for 5-time points of the analysis win-
sis windows (𝐴𝑊 ), including 140, 90, and dow are illustrated in Fig. 2. These AUC fea-
65 minutes. For the purpose of our experi- tures capture fasting glucose levels, as well as
ments, we aim to model pairwise distances glucose rise and recovery patterns in various
between the same meal and different meals. time resolutions.
For this reason, our analysis included only
those participants who consumed each meal
4.2. Personalized metric learning
at least twice. This resulted in 20 participants of PPGR embeddings
we could use to model pairwise distances be- We designed a metric learning algorithm
tween Bar and CF and, and 19 participants that models the pairwise distance between
for the Bar vs. PB meal and PB vs. CF meals. meals consumed by a participant (Fig. 3).
The number of participants reduced to 11 The problem was formulated as both binary
when we considered all three meals for ter- (i.e., CF/PB, PB/Bar, Bar/CF) and tertiary (i.e.,
tiary classification. When considering 𝐴𝑊 CF/PB/Bar) classification tasks (Section 3).
of 65 and 90 minutes, the number of partici- Inputs to the model consisted of the 16 AUC
pants became 14 and 16 for the tertiary com- features of PPGRs (Section 4.1). Let 𝑔𝐖 be
bination, respectively. a function parameterized by 𝐖, which per-
forms a transform of the original AUC space
4. Methodology 𝐱 and transformed embedding 𝑔𝐖 (𝐱). Also,
let 𝑛𝑐 be the set of samples belonging to
4.1. Glucose response feature class 𝑐 from participant 𝑛. The parameters 𝐖
extraction are learned to minimize the distance of sam-
We computed the maximum and minimum of ples from the same meal-type and maximize
each participant’s baseline-corrected PPGR the distance of samples from different meals
(Section 3). Then, we normalized the signal
Figure 3: Schematic representation of the proposed personalized meal classification based on PPGRs.
Metric learning learns PPGR embeddings based on pairwise meal distances within a participant. An-
thropometric and metabolic characteristics are combined with the learned PPGR embeddings for the
final meal classification.
within a participant: We compare the proposed personalized
𝐖∗ = 𝑎𝑟𝑔 min max(∑ ∑ ∑ 𝑑 (𝑔𝐖𝟏 (𝐱), 𝑔𝐖𝟏 (𝐱′ ))
glycemic embeddings learned by the SNN
𝐖𝟏 𝐖𝟐 𝑛 𝑐
𝐱,𝐱′ ∈𝑛𝑐 architecture to the raw AUC features that
(1)
+∑∑ ∑ 𝑑 (𝑔𝐖𝟐 (𝐱), 𝑔𝐖𝟐 (𝐱′ ))) comprised the input to two baseline mod-
𝑛 𝑐≠𝑐 ′ ′
els: a 1-layer feed-forward neural network
𝑐 ′
𝐱 ∈ 𝑛 , 𝐱 ∈ 𝑛 𝑐
where 𝑑(⋅, ⋅) was the 𝑙2−norm. Pairwise (FNN) with 16 nodes and a logistic regres-
metric learning was performed via a SNN sion model. The architecture of the FNN
(Fig. 3). The SNN included 3 layers with 16 was selected to be equivalent to the 16-node
neurons each (i.e., siamese network; Fig. 3). fully-connected layer following the output of
The third layer of the SNN comprised of the SNN architecture. We anticipate that the
a 16-dimensional output, which represented proposed model performs well in all scenar-
the transformed PPGR input samples 𝑔𝐖 (𝐱) ios where learning the original distribution is
and 𝑔𝐖 (𝐱′ ) in each branch. Following that, difficult due to the inherent inter- and intra-
the 𝑙2−norm between 𝑔𝐖 (𝐱) and 𝑔𝐖 (𝐱′ ) was subject variability. However, because we do
computed. The learned glycemic embedding not have this data, comparing against deeper
𝑔𝐖 (𝐱) was fed into a set of fully-connected networks with larger numbers of tunable pa-
layers comprised of 16 neurons (i.e., feedfor- rameters would either underfit or overfit.
ward neural network; Fig. 3), which learned All classification experiments were per-
a feature transformation between the PPGR formed using a leave-one-subject-out cross-
embedding and the type of meal. Glycemic validation. Simple classification accuracy
embeddings were learned based on the orig- was averaged over 40 iterations to remove
inal PPGR, as well as the normalized PPGR random effects from parameter initialization
using min-max normalization. Bayesian hy- and dropout. The classes considered here
perparameter optimization [32] was used to are balanced, therefore the chance accuracy
optimize the dropout between layers (i.e., is approximately 33.33% for the tertiary and
{0.0, 0.1, 0.2, 0.3, 0.4, 0.5}) and the 𝑙2−kernel 50% for the binary task. A paired t-test with
regularization of each layer of the network the assumption of unequal variance among
(i.e., {10 , 10 , 10 , 10 }).
−1 −2 −3 −4 the two groups was used to calculate signifi-
Table 1 4.3. Integrating anthropometric
Accuracy (%) of tertiary meal classification, us- and metabolic
ing the area under the curve (AUC) of PPGRs as characteristics to the PPGR
an input to a feedforward neural network (FNN)
and a logistic regression (LR) model, as well as the embedding
AUC embeddings learned by the proposed metric
We incorporated the anthropometric and
learning approach. metabolic measurements of each participant
Normalized PPGR
to (1) the glycemic embedding 𝑔𝐖 (𝐱) learned
Analysis window Metric learning FNN LR
by the SNN (Section 4.2), followed by a fully-
140 minutes 50.00 47.84 50.00 connected layer (Fig. 3); and (2) the raw
90 minutes 46.14*** 42.71 42.85 AUC features followed by the FNN and logis-
65 minutes 45.89 45.2 45.83
*: 𝑝 <0.05 ; **: 𝑝 <0.01; ***:𝑝 <0.001 tic regression models. We report the corre-
Non-normalized PPGR sponding classification results in order to see
Analysis window Metric learning FNN LR whether the inclusion of these features fur-
140 minutes 44.86 47.84 50 ther improves the meal classification perfor-
90 minutes 45.59* 42.7 44.04
65 minutes 45.63 45.21 45.63 mance. Anthropometric and metabolic fea-
*: 𝑝 <0.05 ; **: 𝑝 <0.01; ***:𝑝 <0.001 tures were included in five unique combina-
tions according to their individual and group
Table 2 characteristics. Combinations 1 and 2 con-
Accuracy (%) of binary meal classification be- sisted of only anthropometric features (i.e.,
tween pairwise combinations of peanut butter age, BMI) and only metabolic measurements
(PB), cornflakes (CF), and protein bar (Bar), us- (i.e., insulin, FBG, OGTT, HbA1C, hsCRP,
ing the area under the curve (AUC) of PPGRs in Tri/HDL), respectively. Meanwhile, combi-
a feedforward neural network (FNN) and a logis- nation 3 included all features from both com-
tic regression (LR) model, as well as the AUC em-
binations 1 and 2. In combination 4, we
beddings learned by the proposed metric learning
approach. The analysis window is 140 minutes.
only considered features that were signifi-
cantly correlated with the AUCs extracted
Normalized PPGR from PPGR (i.e., age, insulin, OGTT, HbA1C,
Task Metric learning FNN LR Tri/HDL). The final combination 5 consisted
PB-CF 74.79*** 72.29 72.22 of three metabolic measurements (i.e., FBG,
PB-Bar 69.68*** 65.19 68.42 OGTT, HbA1C), which were directly related
Bar-CF 52.49 53.59 56.25 to the participant’s glucose level.
*: 𝑝 <0.05 ; **: 𝑝 <0.01; ***:𝑝 <0.001
Non-normalized PPGR 5. Results
Task Metric learning FNN LR We report the accuracy of the proposed met-
PB-CF 66.36 66.54 69.73 ric learning and baseline models (Section 4.2).
PB-Bar 61.92 60.39 64.47 Table 1 presents the tertiary classification
Bar-CF 51.33 52.96 52.5 results over various analysis window (𝐴𝑊 )
*: 𝑝 <0.05 ; **: 𝑝 <0.01; ***:𝑝 <0.001 lengths, while Table 2 presents the binary
classification results for 𝐴𝑊 = 140 minutes
cant differences between the proposed metric for all meal combinations. We observe that
learning and the two baselines. the metric learning approach performs sig-
nificantly better than the 1-FNN and logistic
regression baseline in the majority of cases
for the tertiary task (Table 1) and in many
Table 3
Accuracy (%) of binary meal classification between pairwise combinations of peanut butter (PB), corn-
flakes (CF), and protein bar (Bar), combining the PPGR embeddings learned by the proposed metric
learning approach with anthropometric and metabolic characteristics. The analysis window is 140
minutes. The normalized PPGR was used.
Only PPGR & Anthropometric/Metabolic Combination
Task
PPGR 1 2 3 4 5
PB-CF 74.79 77.41*** 71.4 72.56 72.11 72.11
PB-Bar 69.68 68.1 61.6 61.44 60.85 65.54
Bar-CF 52.49 53.93 54.75* 55.01** 53.42 53.79
*: 𝑝 <0.05 ; **: 𝑝 <0.01; ***:𝑝 <0.001
Combination 1: [Age, BMI]; Combination 2: [Insulin, FBG, OGTT, HbA1C, hsCRP, Tri/HDL];
Combination 3: [Age, BMI, Insulin, FBG, OGTT, HbA1C, hsCRP, Tri/HDL]; Combination 4: [Age,
Insulin, OGTT, HbA1C, Tri/HDL]; Combination 5: [FBG, OGTT, HbA1C]
cases for the binary task (Table 2). This indi- nessed in other cases.
cates that learning a personalized embedding
learning through metric learning can bene- 6. Discussion
fit meal classification performance, even af- Our results indicate that personalized PPGR
ter normalizing the corresponding PPGR. We embeddings through metric learning can ef-
further compare AUC features from the nor- fectively differentiate between meals. While
malized and non-normalized PPGR through a the scope of our current work is limited due
paired t-test with the assumption of unequal to the sparsity of datasets that include CGM
variance. For tertiary classification with 140 signals with concurrent meal intake anno-
minutes analysis window, we achieve accu- tation, we believe that the ability to predict
racy up to 50% for the normalized signal, dietary intake has a broad range of appli-
which is significantly higher (𝑝 < 0.001) than cations in the context of automated real-life
the 44% accuracy from the non-normalized dietary monitoring and interventions. Par-
signal (Table 1). Similarly, for the binary clas- ticularly, these can have valuable implica-
sification, results demonstrate the effective- tions for improving the accuracy of auto-
ness of normalizing PPGR (Table 2), reaching matic diet monitoring based on CGM devices
75% accuracy. for people with (pre)(diabetes. The model re-
The combination of age and BMI with the quires small amount of data for personaliza-
PPGR embeddings learned by the proposed tion and also includes a small number of pa-
metric learning approach depicted the best rameters, therefore making it ideal for a light
results. Also, combinations 2 and 3, which in- user interface (UI). Moreover, the continuous
clude participants’ metabolic characteristics, PPGR collection through CGMs ensures that
improve classification for the Bar-CF meal no meal–no matter how small–is overlooked,
pair from 52.49% to 54.75% and 55.01%, re- therefore can potentially accommodate users
spectively. Individual characteristics seem to with non-routine eating patterns. Overall,
benefit classification tasks that are difficult to the model could be easily added to the exist-
learn solely from the PPGR, such as the CF ing user platforms that are compatible with
and Bar which depicted similar PPGR pat- CGM signals, therefore allowing patients to
terns (Fig. 1), while no improvement is wit- monitor their PPGR patterns and better un-
derstanding the effect of each meal on their acteristics appears to partially help in certain
PPGR. These can be also beneficial for de- cases. As part of our future work, we plan
veloping new technology-assisted dietary in- to explore the feasibility of this system in
terventions, in which patients can visualize, classifying diverse real-life meals, which can
understand, and internalize the interplay be- eventually contribute to effective dietary in-
tween meal intake and PPGR, therefore pro- terventions. We also plan to collect data from
moting positive behavior change [33]. 90 participants, which will provide us with
Despite the encouraging results, this study the opportunity to evaluate our approach at
presents various limitations. First, the stan- a broader scale with a wider array of repeated
dardized meals considered here are similar meals.
in terms of calorie intake and carbohydrate
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