Bone Conduction Eating Activity Detection based on YAMNet Transfer
Learning and LSTM Networks
Wei Chen
1 a
, Haruka Kamachi
1 b
, Anna Yokokubo
2 c
and Guillaume Lopez
2 d
Graduate School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan
Department of Integrated Information Technology, Aoyama Gakuin University, Sagamihara, Japan
Bone Conduction, Transfer Learning, Long-short Term Memory (LSTM), Eating Behavioural Activity.
The trivial eating behaviors affect our health and sometimes lead to obesity and other health problems. We
propose an automatic human eating behavior estimation system , which performs real-time inferences using a
sound event detection (SED) deep learning model. In addition, We customized YAMNet, a pre-trained deep
neural network by 521 audio event classes based on Mobilenet v1 depthwise-separable convolution architec-
ture from Tensorflow. We used transfer learning shaped YAMNet as a feature extractor for acoustic signals
and applied an LSTM network as a classification model that can effectively handle time-series environmental
acoustic signal. Dietary events including chewing, swallowing, talking, and other (silence and noises), were
collected on 14 subjects. The classification results show that our proposed method can validly perform se-
mantic analysis of acoustic signals of eating behavior. The overall accuracy and overall F1 scores were both
93.3% in frame level, respectively. The classifier established in this study provided a foundation for preventing
premature eating and a healthier eating behavior monitoring system.
In modern life, the data of all human habits are being
digitized for a healthier lifestyle. Automatic detection
of dietary habits is one of the challenges of human
habits digitization. This paper explored a method for
automated eating activity using a commercially avail-
able bone conduction microphone. Compared to con-
ventional methods for automatic detection in eating
activity analysis-related works, this paper focuses on
improving the accuracy rate of each independent eat-
ing activity identification, with the basic premise of
using acoustic signals from the natural environment.
According to the 2016 global obesity population
distribution by WHO, Approximately 39% of the
world’s population is overweight, of which 13% is
obese (NCD Risk Factor Collaboration, 2016). In
addition, surveys of obese people have shown that
many of them are ”fast eaters” who chew less and
eat for a shorter time (Yamaji et al., 2018). To pre-
vent obesity, automatic detection of eating behavior
using wearable devices has been progressing over the
past decade (Selamat and Ali, 2020). Wearable sen-
sors that have been proposed for automatic detection
of eating behavior include in-ear microphones (Amft,
2010; Shuzo et al., 2010), neck-worn sensors(Chun
et al., 2018), strain sensors (Yang et al., 2019), elec-
tromyography sensors (Huang et al., 2017), and wrist-
worn sensors (Shen et al., 2016).
As the most used method, acoustic sensing is
one of the earliest modalities studied, with advan-
tages such as ease of wear and precise identifica-
tion of chewing. Being able to strike a balance be-
tween high-quality signal acquisition and user com-
fort is the main challenge of acoustic eating activ-
ity sensing. Kamachi et al. proposed a classifica-
tion method of eating behavior by capturing both the
chewing sound based on bone conduction microphone
to capture both chewing and swallowing sound (Ka-
machi et al., 2021). P
aßler et al. used their proposed
design to perform analysis such as analyzing acous-
tical signal energies and chewing detection based on
magnitude squared coherence function (MSC) (P
and Fischer, 2011) . The fact that teeth produce vi-
brations when they tap, slide, or grind against each
other, these vibrations travel through the jaw and skull
bones as surface vibrations can easily reach the outer
ear (Prakash et al., 2020), leads to a high accuracy
Chen, W., Kamachi, H., Yokokubo, A. and Lopez, G.
Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networ ks.
DOI: 10.5220/0010903700003123
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS, pages 74-84
ISBN: 978-989-758-552-4; ISSN: 2184-4305
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
rate of chewing identification. However, there are
still some issues to be left, such as difficulty in rec-
ognizing swallowing and susceptibility to background
noise (Kamachi et al., 2020).
Most of the eating activity detection methods in
the previous papers are based on manually extracted
features from speech signals and well-researched
classification algorithms such as support vector ma-
chines (Zhang et al., 2011; Nkurikiyeyezu et al.,
2021). In recent years, with the advancement of
deep neural networks, classification techniques us-
ing deep learning such as Convolutional neural net-
works (CNN) and Recurrent neural networks (RNN)
are also suitable for acoustic signals (Bae et al., 2016;
Xu et al., 2018). An emerging approach is to apply
transfer learning to the recognition of acoustic signals
(Ntalampiras et al., 2021).
This work uses acoustic signals recorded from
bone conduction microphones as input and trains a
model that combines transfer learning and deep learn-
ing to incorporate them into the automatic eating ac-
tivity detection task compatible with natural envi-
ronments. The experimental results show that our
model significantly improves effectiveness compared
to existing state-of-the-art approaches, which is very
This paper is organized as follows. Section 2
presents the processing pipeline focusing on the archi-
tecture, detail of the datasets, and the primary method
used in this study. In Section 3, we describe the classi-
fication method, evaluation methods, and experimen-
tal results. In section 4, we discuss our proposed
method compared with the previous ones and present
future work. Finally, we conclude this paper in Sec-
tion 5.
This section describes the sample data utilized in
this study and the methods used to process the au-
dio signal data, the feature extractor, and the classifier
model parameters.
1.1 Eating Behavior Signal Data
Figure 1: Data collection environment and devices.
To evaluate the segmentation method for detection
eating activities using bone conduction sound, we col-
lected meal sound data in a natural meal environ-
ment. We used a bone conduction microphone con-
nected wirelessly to a Smartphone using Bluetooth
protocol for dietary activities sound collection. The
smartphone used was a Google Pixel 3, and the bone
conduction microphone was a Motorola Finiti HZ800
Bluetooth Headset. The sound signal sampling from
the microphone was 44100 Hz. After collection, we
transferred data to a computer for labeling and analy-
sis at 16000 Hz. Besides, it was necessary to perform
labeling afterward since data collection in a free envi-
We collected data from 14 participants. All of the
young subjects were between the ages of 11 to 32
years. As shown in Figure 1, which reproduces the
data collection conditions, subjects put the bone con-
duction microphone on one ear. Also, we shot a video
focused on the mouth and throat of the subjects in or-
der to assist the afterward labeling task of audio sec-
tions corresponding to chewing, swallowing, talking,
and other sounds (like noise). The participants will be
asked to say a certain word at the beginning of the ex-
periment, which will be used to synchronize the video
and audio data.
To provide a natural environment, we collected
data in general daily life, such as big surrounding
sounds and eating with a conversation. Participants
were required to have a usual meal as every day’s
meal. For example, in a dining room, a standard
household table with other family members, and at
the university cafeteria with friends, we assume that
represents different noisy conditions. The meal con-
tent was also totally free, and participants ate what-
ever they wanted as usual in daily life, such various
food types were mixed unpredictably during the same
meal. Also, the collected data time varied by cases
collected from the meal’s start or the middle of the
meal. Besides, we collected additional swallowing
sound data because the few swallowing data com-
pared to other classes have been pointed out in our
previous works. The swallow audio data from 8 men
and women aged from 22 to 42, who were required
to have a couple of drinks, were collected the same as
2.1 Architecture of the Proposed
Our proposed architecture is shown in Figure 2. The
first step is to label the acquired data so that it may
Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks
Figure 2: Block Diagram overview in this study.
be used as a reference for training data to predict eat-
ing behavior. The acoustic eating behavior data taken
from a bone conduction microphone was manually se-
lected to include chewing, swallowing, speech, and
other sound events including noises applicable seg-
ments. In the next stage, the labeled sound episodes
are prepossessed by window segmentation, acoustic
enhancement and the features of the data are extracted
from transfer learning using YAMNet (Tensorflow,
2020). Finally The resulting embedding layer is then
classified using a deep learning network Long Short-
Term Memory (LSTM) (Hochreiter and Schmidhu-
ber, 1997) to evaluate the classifier.
2.2 Sound Episode Segmentation
In order to segment the eating behavior to the specific
number of bites, the acoustic signal of a single bite
has to be accurately labeled. For the labeling process,
we used speech intensity to more easily segment the
speech signal (Clark et al., 2014). As the purpose of
this study is to estimate human eating behaviour au-
tomatically and possible to classify the audio signal
in real time, We define eating behavior as four steps:
chewing, swallow, talk and other. Chewing refers to
the vertical opening and closing of the top and bot-
tom teeth during a single chew. Swallowing refers to
the swallowing of food in one sitting. It is also con-
sidered a single swallow if the subject drinks. Talk
refers to what the subject is vocalizing. Other refers
to events that are not all of the above event, such as
when there is no sound or when there is noise. The
number of each sound event we labeled is shown in
Table 1. As you can see the data is not balanced well
due to the number of swallow is low. Because of this
is also in agreement with our philosophy of chewing
as much as possible in a single swallow during eating
We define one acoustic eating episode label by
comparing the raw acoustic data with the sound in-
tensity and referring to the beginning and end points
of the waves that are visually obvious as shown in
Figure 3. The amplitude of swallow episode in the
acoustic data acquired from the bone conduction mi-
crophone tends to be smaller than that of chewing
episode. Therefore, the acoustic data was recorded
and synchronized with video to label specific swal-
BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing
Figure 3: Part of continuous chewing data labeling scene.
Table 1: The number of datasets collected.
Sound episode categories Data number
Chewing data 3395
Swallow data 334
Talk data 491
2.3 Feature Extraction and Signal
The most commonly used features in acoustic signal
processing are Mel-Frequency Cepstral Coefficients
(MFCC) (Ittichaichareon et al., 2012). As a mech-
anism of MFCC, the magnitude spectra projected on
the reduced frequency bands are transformed into log-
arithmic magnitudes, which are then approximately
whitened and compressed by discrete cosine trans-
form (DCT). In contrast, DCT removes information
and destroys spatial relations in deep learning mod-
els, most audio signal processing with deep learning
methods use log-mel spectrograms to perform feature
extraction (Purwins et al., 2019) (Lee et al., 2017)
(Zheng and Yan, 2019).
Also, feature extraction methods based on log-mel
frequencies using transition learning have emerged.
In the evaluation of the Non-Semantic Speech bench-
mark (NOSS), which assess the general usefulness
of speech representations on “non-semantic” tasks,
shows that the correctness of the middle layer out-
puts of YAMNet and TRILL all reach a good accu-
racy rate (Shor et al., 2020). In the same acoustic
signal classification domain, the YAMNet model is
treated as a feature extractor by outputting an inter-
mediate layer embedding using transfer learning for
COVID-19 cough classification (Elizalde and Tomp-
kins, 2021).
2.3.1 Pre-emphasis
Pre-emphasis is a widely used method in audio signal
processing which has the effect of emphasizing the
wide-area components of the audio waveform (Dong
et al., 2020). In this study pre-emphasis is performed
on the raw signal before it is processed to compen-
sate for the frequencies in the high frequency portion
of the acoustic signal. The following filters are used
shown as equation (1):
(n) = Au(n) αAu(n 1) (1)
where, Au and Au
are the raw audio signal before
and after the pre-emphasis operation; n is the index
of each sample in raw audio signal; α is the parame-
ter which was normally assigned a value in the range
of [0.9, 1].
2.3.2 Framing and Windowing
After pre-emphasis, the raw audio signal is split into
slide windows to generate a log-mel spectrogram.
statistics of the collected data showed in Table 2. The
average time of one chewing is around 315ms. Also
There are very short sound episodes in the labeled
data. If the label time is larger than the window size,
the labeling will be inaccurate, so in order to effec-
tively classify more sound episode clips, We define
the window size as 250ms which is below the mean
time of chewing data event. and the hop size of each
window as 93ms, which is less than one-half of win-
dow size and greater than one-third of window size.
Table 2: Statistics of each label timing.
Categories Mean
Chewing 314 28 1042 0.1572
Swallow 405 54 1541 0.2353
Talk 859 92 5067 0.6714
2.3.3 Log-mel Spectrogram
Not only YAMNet, but also many other acoustic deep
learning methods use mel spectrograms as input pre-
processing form for audio signals (Zeng et al., 2019).
According to the YAMNet summary, we generate the
mel-scaled spectrograms with a triangular filterbank
of 64 log-energies. The relationship between the Mel
spectrum and the frequency is shown in equation (2):
= 2595 · lg(1 +
) (2)
where, f
is the mel frequency; f is the linear fre-
Then use the short-time Fourier transform (STFT)
to find out the frequency of shorter intervals. We de-
fined the window size of STFT to be 25ms and the hop
size of STFT to be 10ms. The progress feeding the
signal into the filterbanks to get the H
(k) is shown
Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks
in equation (3):
(k) =
k f (m 1)
f (m) f (m 1)
, f (m 1) k f (m)
f (m + 1) k
f (m + 1) f (m)
, f (m) k f (m + 1)
0, others
where, f (m) is the m
filter’s centre frequency;
(k) is the returned filterbank as matrix.
The result of multiplying filterbanks by STFT,
which is the raw audio signal processed to energy
spectrum, is shown in equation (4):
LogMelSpec(m) =
f (m+1)
k= f (m1)
(k) · |X(k)
where, |X(k)
| is the energy spectrum is the point of
energy; m is the filterbanks and k is the point of
2.3.4 YAMNet Embedding
YAMNet is a pre-trained deep network which pre-
dicts 521 audio event classes based on the AudioSet-
YouTube corpus (Gemmeke et al., 2017). Employ-
ing the Mobilenet v1 (Depthwise-separable convolu-
tion) architecture (Howard et al., 2017). The audio
set for training the YAMNet model contains more
than 632 audio events sampled from a 10-second clip
of a YouTube video that has been played more than
1000 times. Due to the properties of deep learning
and YAMNet, a feature extraction layer is built into
the model. Therefore, the log-mel spectrogram of
the speech signal directly becomes the input for Mo-
bilenet v1.
The network operators on input mel spectrogram
of size (48, 32, 32) as we get in previous section. The
structure of YAMNet is shown in Table 3. Inputs sig-
nal is processed by an 1-D convolution layer, which
the kernel size of 3 × 3. Then pass the value through
the number of filter 64-1024. The global average
pooling (AP) layer is in the next to prevent potential
over-fitting by reducing the total number of parame-
ters of the model. At last, the network comes with
two fully connected (FC) layers of size 1024 and 64.
With the last list of value and a softmax layer to com-
pute one of 521 result determine which sound episode
this log-mel spectrogram belong. The most important
point of YAMNet is the last second fully-connected
layer which have 1024 values. We customize the
YAMNet network to contain with the network struc-
ture until last second fully-connected layer. We use
YAMNet to output a 1024 values of embedding layer
as feature extractor and treating YAMNet as a transfer
learning method. The advantage of this method is that
it has enough acoustic features even when the number
of data for this signal classification problem is not so
large as to overflow, and provides great trade-off be-
tween performance and computational cost.
Table 3: YAMNet body architecture.
Type Filter shape Input size
3 × 3 × 3 48 × 32 × 32
dw 3 × 3 × 3 dw 48 × 32 × 32
pw 1 × 1 × 32 × 64 48 × 32 × 64
dw 3 × 3 × 64 dw 24 × 16 × 64
pw 3 × 3 × 128 24 × 16 × 128
dw 3 × 3 × 128 dw 24 × 16 × 128
pw 1 × 1 × 128 × 128 24 × 16 × 128
dw 3 × 3 × 128 dw 12 × 8 × 128
pw 1 × 1 × 128 × 256 12 × 8 × 256
dw 3 × 3 × 256 dw 12 × 8 × 256
pw 1 × 1 × 256 × 256 12 × 8 × 256
dw 3 × 3 × 256dw 6 × 4 × 512
pw 1 × 1 × 256 × 512 6 × 4 × 512
dw 3 × 3 × 512 6 × 4 × 512
dw 3 × 3 × 512 3 × 2 × 1024
pw 1 × 1 × 512 × 1024 3 × 2 × 1024
dw 3 × 3 × 1024 dw 3 × 2 × 1024
pw 1 × 1 × 1024 × 1024 3 × 2 × 1024
AP&Pool 1 × 1 × 1024
3 × 2
FC 1024 × 512 1 × 1 × 512
So f tmax Classifier 1 × 1 × 512
2.4 Classifier Training and Evaluating
2.4.1 LSTM Model
Recurrent Neural Networks (RNNs) are very effective
for analyzing sequences of text, acoustic signals, and
video (Zhang and Man, 1998). The input signal can
be persistently held by looping in the network. Basi-
cally, the main feature of RNN is that it remembers
the previous state and uses that information to deter-
mine the next state. Therefore, models using RNN
networks are very suitable for analyzing time series.
However, the gradient of a traditional RNN depends
not only on the present error, but also on the past error,
so the retro-propagated gradients tend to grow enor-
mously or fade over time.
LSTM is a type of RNN network that has a
built-in function to determine which information to
store and which to delete, and is a model that can
store long-term dependencies without accumulating
errors(Navarro et al., 2020). The LSTM module has
BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing
Figure 4: General structure of an Long Short-Term Memory
neural networks (LSTM).
three internal gates, termed input, forgotten and out-
put shown in Figure 4. The input gate controls when
any new information will be put into memory. For-
gotten gates allow the cell state to identify important
and unwanted data when a piece of information is for-
gotten, leaving space for new data. The output gate is
used to control the result of the memory stored in the
cell state. The cell state has a weighting optimization
mechanism and controls each gate based on the out-
put error of the network. The cell state has a weight-
ing optimization mechanism that controls each gate
based on the output error of the network and sends
the prediction to the next LSTM module.
2.4.2 Evaluation of Classifier
We use recall, specificity, precision, accuracy, and F1-
score to evaluate the performance of the classifier. To
calculate those values there are four types of possible
results for the classification task. If the sample true la-
bel is positive and it is classified as positive is counted
as a true positive (TP). If the sample is positive and it
is classified as negative is counted as a false negative
(FN). If a sample is negative and is classified as neg-
ative or positive, it is considered a true negative (TN)
or false positive (FP), respectively. Based on them the
result of recall, specificity, precision, accuracy, and
F1-score is defined by the following equation:
Speci f icity =
FP + T N
Precision =
T P + FP
Accuracy =
T P + T N
T P + T N + FP + FN
= 2 ×
Precision × Recall
Precision + Recall
3.1 YAMNet Feature Extraction
The acoustic dietary data collected in this study was
segmented and feature extraction was performed as
described in Section 2.4, as shown in Figure 5. All
wav files of recorded audio signals are resampled to
1.6khz, Pre-emphasised then cut into sliding windows
of window size 250ms and hop size 93ms, which win-
dow size has 4000 sample points and hop size has
1500 sample points. The slide window is further ap-
plied to splitting the signal into short frames, applied
STFT to generate a spectrogram. Finally applied to a
64 log-energies mel filterbank to output log-mel spec-
trogram prepared for feeding the YAMNet transfer
Figure 5: Sound clip splitting and generating the embedding
layer as feature extractor using YAMNet.
Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks
3.2 LSTM Model Implementation
The feature extracted sound clips are divided into
train data, validation data and test data. The num-
ber of data used for classification is shown in the fol-
lowing table 4. We set the ratio of train data, vali-
dation data and test data to be 75%-10%-15% (Va-
sudevan et al., 2020). As an input to the model, three
sound clips are stored as a single continuous clip in
order to increase the storage area in the time domain
of the LSTM gate, and the overlapping of the three
clips connected back and forth is performed. From
the shape of our input data, we set the batch size, time
steps, and feature to 16, 3, and 1024, respectively.
Table 4: Number of the sound clip for each label.
Category Train Validation Test Total
Chewing 8283 1155 1672 11304
Swallow 874 179 158 1129
Talk 3357 470 724 4504
Other 20913 2676 4134 27658
Total 33427 4480 6688 44595
The parameters of each layer of the LSTM model
were set as follows and shown as table 5. The num-
ber of the hidden layers and the iterations for each is
set to 128 and 256 (Altch
e and de La Fortelle, 2017).
To prevent overtraining of the LSTM model, we set
up a dropout layer with a rate of 0.25%, the dropout
rate of 0.25% can effectively prevent memory loss
in the model (Semeniuta et al., 2016). Finally, the
Softmax layer outputs a vector with 4 elements to
produce the results of the LSTM model, where the
probability of each of the vectors is in the order of
other data, chewing data, swallow data, and talk data.
When the LSTM model complies, we fine-tune the
model using the Adam optimizer. Adam is an opti-
mization algorithm that can use instead of the clas-
sical stochastic gradient descent procedure to update
network weights iteratively in training data (Kingma
and Ba, 2014). The learning rate is set to 0.001,
and the sparse categorical cross entropy is selected
as the loss function. Use sparse categorical cross en-
tropy when your classes are mutually exclusive such
as when each sample belongs exactly to one class (To-
takura et al., 2020) and expressed in Equation (10).
Where, N presents the number of categories which is
4; y
and log
respectively represents the label value
and its log probability. The Model Loss on training
and validation datasets is shown in Figure 6. It can
be observed that there was no gradient explosion ap-
peared from the plotted data. Train loss drops below
0.05 from 80 epochs and stays at about 0.02 from 200
epochs. Validation loss floats and stabilizes at about
0.5 from 120 epochs. We implemented 200 epochs,
where the loss is stable, as a parameter during model
Loss =
· log
Table 5: LSTM body architecture for classification.
Type Filter shape Input size
LST M1 kernel size 256 1 × 3 × 256
LST M2 kernel size 128 1 × 128
Dropout drop rate 0.25 1 × 128
FC 128 × 4 1 × 128
So f tmax Classifier 1 × 4
Figure 6: The model loss on training and validation
3.3 Classification Performances
As section 2.5.2 described the evaluation of classi-
fier, a confusion matrix is to evaluate the performance
of the classifier. The 6688 sound clip from the test
datasets described in section 3.1 is used for evaluat-
ing, which shown as Figure 7.
Figure 7: Confusion matrix of YAMNet as feature extractor.
According to the results of the confusion matrix,
the sound clips of Chewing, Swallow, Talk, and Other,
whose features were extracted by YAMNet, retain a
significant percentage of accuracy. The accuracy rate
was 91.54%, 73.64%, 90.73%, 95.13% respectively.
The weighted average reaches 93.30%. And the over-
all F1-score reaches 93.28%. To understand the per-
BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing
Table 6: Evaluation result of the trained LSTM classifier each categories.
Evaluation metric Chewing Swallow Talk Other Average Weighted Average
Recall 91.55% 73.65% 90.74% 95.13% 87.77% 93.3%
Specificity 96.86% 99.83% 98.91% 91.46% 96.77% 93.78%
Precision 90.74% 90.83% 90.61% 94.86% 91.76% 93.29%
Accuracy 91.54% 73.64% 90.73% 95.13% 87.76% 93.30%
F1-Score 91.14% 81.34% 90.67% 94.99% 89.54% 93.28%
formance of the Classifier, equations (5) (9) in Section
2.5.2 were calculated the recall, specificity, precision,
accuracy, and F1-score for 4 categories as Chewing,
Swallow, Talk, and Other, shown as Table 6.
The accuracy rate for Swallow is 73.64% with
the lowest value and the accuracy of Ture label for
Chewing and predicted as Other was 21.62%. Which
showed that this model is still in a difficult state to
classify swallowing data perfectly. Several reason
leads to the low accuracy of swallow data compared to
other sound events. First reason is that the number of
swallowing data in this datasets is lowest. In recorded
data, approximately 15 or more chewing event cor-
responding to one swallow event. Therefore, the total
number of swallow sound clip data is only 1129, 2.5%
of the total sound clip data number (44595), 10% of
the total chewing sound clip data number (11304).
This may result in an imbalance in the data. To ac-
count for this, we incorporated the weighting of the
LSTM model. However, we need to further improve
the balance of the data.
4.1 Performance Comparison with
Previous Research
Several classification method for detection of eating
behavior have been developed in the last decade. Al-
though the technology of dietary monitoring is evolv-
ing rapidly with the advancement of sensors, auto-
matic monitoring of comprehensive dietary intake in
real time is still one of the big challenge worth study-
ing. There are two ways to classify eating behavior.
One is to classify whether it is during the meal period
or not, or to classify chewing, swallowing, and speech
in real time as in this study.
There are already a large number of dietary pe-
riod classifications in existence and with a sufficiently
high level of accuracy. Gao et al. develop a practi-
cal solution for automatic detection of eating episode
(Gao et al., 2016). They achieve the detection accu-
racy rate over 94% using LOSO (Leave One Sample
Out) method based on deep learning. Bi et al. propose
a wearable system for eating detection in free-living
scenarios and achieve the accuracy rate over 90% but
also belongs to the classification by dietary period
(Bi et al., 2017). However, this kind of classifica-
tion method cannot classify how many times one eat-
ing event has chewed during the meal period though
the high accuracy. Zhang et al. used the same pol-
icy as in this study to achieve real-time classification
of eating episodes (Zhang et al., 2020). They present
the design, implementation, and evaluation of a neck-
lace suit for detection and validation of chewing se-
quences and eating episodes in free living condition.
They achieve the F1-score at 76.2% on per-second
level and 81.6% at the per-episode level. Overall F1-
score of 73.7% in detection the chewing sequences in
a free-living condition. Kamachi et al. suggested an
automatic segmentation method to detect eating ac-
tivity using bone conduction sound the same as this
study and porpoised a segmentation method based on
the chewing model (Kamachi et al., 2021). The result
comparison with all studies above and related study
on chewing sound detection shown in Table 8. From
the result comparison, our study performed a better
F1-score in overall sound episode. Also in this study,
the data was collected in a free-living condition too
with talking and noises.
Table 7: Comparison between the developed YAMNet fea-
tured LSTM classifier and previous classifier.
Events F1Score
(Bedriet al.,2017)
(Gaoet al.,2016)
AR Ea Ac 94%
(Zhanget al.,2020)
(Diouet al.,2017)
(Kamachiet al.,2021)
Pre 88.1%
Our method
Note: Where, F1Score
means comprehensive
F1-score; Mu means Multimodal sensor; AR means
Audio record, Ch means Chewing event; Dr means
Drinking event, Ta means Talking event; Wa means
Walking event; Ea means Eating episode; O means
Other(silent) data; Sw means Swallow event; Ac and
Pre means overall accuracy and precision when F1-
score is not provide, respectively.
Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks
4.2 Future Work for Classification
As indicated by the results in this study, the swallow
data holds a low percentage of accuracy rate. The
addition of sound episodes is becoming essential in
the future to improve the classifier. It is also possi-
ble that the volume of swallowing sounds taken with
bone conduction microphones varies greatly and that
sound episodes of swallowing that are too small were
not adequately captured in the labeling phase of this
study. The future plan designed as following:
Eliminate artificial error during labeling by em-
ploying throat microphones in addition to bone
conduction microphones and video in datasets
Classification of eating behavior by simultaneous
input of throat microphone and bone conduction
microphone when classification need better swal-
lowing sound estimation result.
In this study, we developed a classification system for
human eating behavior in a natural environment us-
ing YAMNet transition learning and LSTM networks.
The data for the classification of eating behavior con-
sists of chewing, swallowing, speech, and other sig-
nals including noises. In particular, we found that the
use of transition learning in YAMNet can enhance the
features of speech signals and improve the accuracy
rate of classification models for machine learning and
Deep Learning. Using LSTM, we built a classifier
using the embedding layer of YAMNet as the input
feature. The F1-score and accuracy rate of the over-
all classified data reached 93.28% and 93.3%, respec-
tively. By using the classification prediction of this
research, we can canonically estimate the number of
chewing and swallowing in real time, and expect to
build a smarter eating environment by digitizing eat-
ing behavior and preventing fast eating.
This research was supported by Lotte Research Pro-
motion Grant. Entire experimental protocols were ap-
proved by the ethics committee of Aoyama Gakuin
University (H18-003).
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