According to the objectives, the performance of the blood glucose measuring model is evaluated by two different metrics, which are mean absolute error and mean absolute percentage error. In particular, we divided the test dataset into two subsets with including one with blood glucose level within under 100 mg/dL and the other with blood glucose level within 100 mg/dL or more. For the lower subset, we use MAE to evaluate, and mean absolute percentage error (MAPE) is used for the higher one. The formula of MAPE is defined as follow:
𝑀𝐴𝑃𝐸 = 1
𝑁∑|𝑦̂𝑖 − 𝑦𝑖|
𝑦𝑖 ∗ 100%
𝑁
𝑖=1
(6.2) where N is the number of blood glucose values, 𝑦̂𝑖 and 𝑦𝑖 are predicted and actual blood glucose level, respectively.
The results of the evaluation process are as follow:
• 𝐺𝑙𝑢𝑐𝑜𝑠𝑒 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 < 100𝑚𝑔
𝑑𝐿 : 𝑀𝐴𝐸 = 2.48
• 𝐺𝑙𝑢𝑐𝑜𝑠𝑒 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 ≥ 100𝑚𝑔
𝑑𝐿 : 𝑀𝐴𝑃𝐸 = 3%
Although our model currently doesn’t achieve a state-of-the-art performance, it still satisfies the objectives that we propose, so the current performance is acceptable. However, it can be improved in the future by adding more data as well as optimizing the architecture and hyperparameters.
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CONCLUSION AND DISCUSSION
Overall, we can finish all objectives that we proposed. We built a wearable device that can precisely record the pulse signal from the wrist of a subject. The precision of it has already been evaluated by a trustworthy ECG sensor. Moreover, we can build a deep learning model that can forecast the pulse wave signal 2 seconds into the future and another one that can use the forecasted data to measure the blood glucose level.
However, our project cannot be without shortage. One of which is our dataset. We aware that our data is not large as well diverse enough. It also doesn’t contain data from diabetes patients. This is because we cannot recruit patient for the data acquisition purpose. Besides, we currently don’t have an architecture that is large enough to build a model that can forecast longer.
Hence, our future works are:
• Acquire a larger and more diverse dataset including data from normal subjects, diabetes patients of different ages as well as different compilations of diabetes.
• Increase the scale of model to make it able to forecast longer.
• Make use of the interpretability properties of the attention mechanism of the forecasting model, and apply interpretability to the blood glucose measuring model to provide insights about the decisions of the models.
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I
APPENDIX 1
Proceedings of 2023 International Conference on System Science and Engineering (ICSSE)
1
Development of a Wearable Human Pulse Measuring Device using Piezoelectric Sensor
Quan Dao Thanh
Department of Mechanical Engineering HCMC University of Technology and
Education Ho Chi Minh City quandaoforwork@gmail.com
Tuan Le Quoc
Department of Mechanical Engineering HCMC University of Technology and
Education Ho Chi Minh City lequoctuan.fwork@gmail.com
Duc Bui Ha Ph.D
Department of Mechanical Engineering HCMC University of Technology and
Education Ho Chi Minh City ducbh@hcmute.edu.vn
Abstract— Pulse diagnosis, an ancient technique in traditional Chinese medicine (TCM), is an effective non-invasive method for diagnosing human diseases. Traditionally, the practitioners use their fingertips to sense the pulse of the blood vessels on the patient's wrist. However, this method heavily and subjectively depends on the doctor's experience. Additionally, it’s impossible to store the patient's pulse records for posterior review or analysis. To solve such restrictions, a wearable device that could precisely measure the wrist-based pulse signal and save it as digital data was proposed. In the proposed device, a piezoelectric sensor, which is a highly sensitive, lightweight, stable sensor, was used to record variations of the pulse. Since wrist pulse signals collected by the device contain various types of noises which make it difficult to assess the shape of the pulse wave, a slight low-pass filter was applied to remove high- frequency noises. The processed data was then compared with simultaneous electrocardiogram (ECG) data for the sake of evaluation. The preliminary results showed that recorded data from the proposed device is consistent with the equivalent ECG data.
Keywords—piezoelectric sensor, traditional Chinese medicine, wearable device, pulse wave analysis, Arterial pulse
I. INTRODUCTION
For thousands of years, pulse diagnosis has played an important role in traditional Chinese medicine (TCM) for disease analysis. This is a well-known noninvasive assessment approach that is successfully used not only in China but also in many Eastern countries. Compared with ECG signals which can be used to monitor the health status of the human heart, wrist pulse waves can reflect the resonance oscillation between the heart and other internal organs through the arterial system. Hence, it can be used to get even more information about the human body’s health status besides the heart. In practice, doctors would place the matching fingers on the patient's wrist at three places which are cun, guan, and chi. They then feel the characteristics of the pulse, such as the pulse rate, trend, strength, length, width, etc. to make diagnoses for the patient. However, Chinese medicine practitioners may provide varied answers because the accuracy of the diagnosis depends subjectively on their intuition and experience. Recently, by taking advantage of the developed combination of digital signal processing technology and biomedical knowledge, TCM pulse measuring devices can now visualize and analyze the measured pulse profile instantaneously [1]–[3].
There have been many different approaches to describe the pulse capture technique using different kinds of sensors.
For example, John N. Lygouras et al. [4], Kun-Chan Lan et al. [5], and Leonid S. Lovinsky [6] use a photoplethy- smography sensor to measure pulse signals at the fingertips and wrist. Although they achieved effective outcomes with their device, their processes didn’t follow the nature of wrist pulse diagnosis in traditional Chinese medicine. While a photoplethysmography sensor uses infrared light to measure the volumetric changes in blood, it cannot obtain the pulse strength as well as the resonance oscillation between the heart and other organs.
Lisheng Xu et al. [7] implemented a wavelet-based cas- caded adaptive filter (CAF) to remove the baseline wander of the measured signal from a single piezoelectric sensor, this study gives many positive results about piezoelectric data acquisition as well as noise filtering from the signal, but using a single sensor leads to the loss of important data characteristics and misrepresents the genuine function of pulse capture in TCM.
In some other studies, Chung et al. [8] used three sensors to get the wrist’s pulse information simultaneously and used Three-Dimension Pulse Mapping (3DPM) to simulate the actual touch sensation, or Chuangly Chen et al. [9] used a sensor array composed of 3 rows x 4 columns MEMS sensors to record wrist pulse pressure and mapped it to 3D data. By obtaining 3D pulse wave data, it is certain that they can extract much more important features from the pulse such as depth, and length. However, it is also a huge disadvantage which is the significantly increasing computational weight for the posterior analysis. Additionally, their signal acquisition systems are clunky and expensive so it is difficult to develop them into a wearable device.
To address the limitations of these prior studies [4]–[9], we propose a wearable sensor system including a 3x1 piezoelectric sensor array to record wrist pulse waves at cun, guan, and chi positions.
Our contribution to this work includes designing and implementing a wearable piezoelectric-based device.
Furthermore, the recorded data is saved in comma-separated values format for the latter processing and analyzing format
The remainder of this paper is organized as follows:
Section 2 presents our proposed methodology including data acquisition, and data analysis. The recorded results are presented in Section 3 and the paper is finally concluded in Section 4.
Proceedings of 2023 International Conference on System Science and Engineering (ICSSE)
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II. METHODOLOGY
A. Wrist pulse wave
In Traditional Chinese Medicine, there are four diagnostic methods used to examine human health conditions namely inspection, auscultation, olfaction and interrogation, and palpation. Each method is based on different information such as external conditions, family history, diets, living states, etc. of the patients to examine their wellness [10]. In this study, we mainly focus on wrist pulse wave diagnosis which belongs to palpation methods. The wrist pulse diagnosis is an important method to assess a person’s overall health conditions and identify imbalances within the human body. It is commonly believed by TCM practitioners that wrist pulses can reflect the flow of qi, which is a vital energy or life force, and everything in the world is made up of qi. Qi circulates throughout the human body as well as every organ inside it through meridian channels. This flow is then reflected in many acupuncture points in the body including cun, guan, chi positions. Hence, wrist pulse diagnosis is a way to capture the information of qi through acupuncture points. According to TCM theory, the stability and balance of the flow of qi are the most important concepts used to judge the wellness of the human body. By assessing the flow of qi through the wrist pulse, TCM practitioners can grasp the whole information about the internal organs of a person [11], [12]. Moreover, many recent researches have given scientific proof of this kind of diagnosis. In detail, during the respiratory process, the heart creates an oscillation that is distributed throughout the arterial system of the human body. Side-branch organs then react to the oscillation, generate harmonic forces with maximum amplitudes near their own natural frequencies, and then distribute back to the arterial system. Based on this
“frequency match” theory, it is recorded that the frequencies at several acupuncture points match the natural frequencies of several organs [13], [14].
To assess the wrist pulse, TCM practitioners simultaneously place their index, middle and ring fingers on the patient’s radial artery, which correspond to cun, guan, and chi, respectively. These positions are commonly believed to represent the states of internal organs of the human body. For example, cun, guan, and chi positions on the left hand correspond to heart, liver, kidney while those on the right hand correspond to lung, stomach and kidney, respectively.
Because the pulse varies according to applied static forces, practitioners then respectively apply light, medium, heavy forces to feel for various qualities, including pulse rate, trend, strength, length, width, etc. Based on these qualities, practitioners with experiences can tell the imbalance and overall health state of a person [9], [15], [16].
B. Piezoelectric sensor
Piezoelectric sensors are electronic devices that can transform mechanical or thermal energy into electrical signals, utilizing the principle of electromechanical coupling.
This is based on the phenomenon of piezoelectricity, whereby certain materials generate an electrical voltage when exposed to mechanical stress, and conversely, produce mechanical stress when subjected to an electrical voltage. This type of sensor has several specific advantages over other types of pressure sensors that make it well-suited for TCM. Firstly, piezoelectric sensors are highly sensitive and can detect the pulse wave which is a very small pressure wave generated by
a heartbeat. Piezoelectric sensors also have a fast response time, which is important for accurately capturing the pulse wave. The pulse wave is a dynamic signal that changes rapidly, and a fast response time is needed to capture these changes. Photoplethysmography sensor also has sensitive characteristic and fast response time, but this type of sensor cannot precisely reflect the nature of TCM techniques which depend on the forces applied to the vessels at the wrist.
Because of the adjacent of cun - guan - chi position, the sensor's size must be comparable to the size of a typical person's finger. In this study, we use piezoelectric sensors (ID: DT1-052K) developed by TE Connectivity Inc. with size 16 mm x 41 mm (width x height) and the total thickness is 40 μm.
C. Hardware design
The hardware architecture of the proposed system was shown in Fig. 2. It was divided into three parts: a device including three Piezoelectric sensors, ADC and Raspberry Pi for data acquisition and analysis, computer for data visualization and storage. When utilizing a piezoelectric sensor to measure the pulse signal of the wrist, the primary challenge is the presence of electrostatics on the body. The direct contact of the sensor with the skin causes the electrostatic to interfere with the signal, resulting in signal inaccuracy and significant noise. As a result, the received signal is not reliable.
The final design of the system is shown in Fig. 3. In order to avoid electrostatic interference and false signals when the sensor is in direct contact with the skin, an isolator element is incorporated into the sensor design, enabling indirect measurement without direct skin contact. The isolator (Fig. 3b) elements can rotate along the axis, allowing us to apply different forces on them. The sensor tips represent the fingers that make contact with the wrist and transmit the vibrations to the piezo sensor. It is free to vibrate thanks to the hollow space in the isolator.
Fig. 1. Piezoelectric sensor
Fig. 2. The hardware architecture of the proposed system
Proceedings of 2023 International Conference on System Science and Engineering (ICSSE)
3 According to [15], the distance between Guan and Cun is about 10 mm, and Chi is about 10-15mm proximal from Cun.
Hence, the distance between each sensor tip is 10 mm. A bracelet was designed to fit the wrist size of an adult. Three M6 bolts were placed on the upper part of the bracelet to apply force to the isolators.
D. Data acquisition
Based on the nature of piezoelectric sensors which generate voltage with respect to the applied pressure, an additional analog-to-digital converter (ADC) is needed to digitize the generated voltage signal. Additionally, the human wrist can only generate a very small pressure, and it was tested that the voltage generated by piezoelectric sensors is quite small. Hence, a high-resolution ADC must be used to record even the slightest changes in piezoelectric signal. In this study, a 32-bit ADC named ADS1263 was chosen to satisfy the aforementioned requirements. To read data from this ADC, an additional Raspberry Pi Zero which was directly connected to the ADS1263 was used. Furthermore, for the sake of evaluation, an ECG sensor, which is a 3-wire ECG named SEN0213 (DFRobot), was recorded simultaneously.
However, the ECG sensor was recorded separately by an Arduino Uno microcontroller board instead of a Raspberry Pi Zero. An additional circuit was built to make sure that two types of sensors are recorded simultaneously. In this circuit, a button was used to decide when to start recording. After the button is clicked, Raspberry Pi Zero sends a signal to Arduino Uno through an optocoupler and then two boards start to record the signal simultaneously.
E. Data preprocessing
As mentioned before, the recorded data is contaminated by several types of noises such as 50 Hz frequency noise created by the power supply, or baseline wander. Besides, because of the nature of piezoelectric sensors, it must be exposed to the human wrist to record the pressure signal.
Hence, the recorded signal is directly affected by electrostatic noise as well as muscular noise happening on human skin [17]. As a result, the validation process cannot be done without filtering these noises. However, because of the objective of this study, only high-frequency noises are focused. Specifically, the recorded data is put through a low- pass filter with a cutoff frequency of 20 Hz to partially remove high-frequency data. Furthermore, in order to decrease the delay offered by the filter, a Butterworth filter is proposed in this study.
III. RESULT A. Hardware
The system comprises a bracelet and a control box, as shown in Fig. 5. Both of which are fabricated using 3D printing technology and PLA material with a LayerHeight of 0.2 mm and Infill of 100%. This process facilitates precision machining at a low cost.
The control box, with dimensions of 150 mm x 100 mm x 50 mm, is compact and can be wired electrically. It also features a cooling fan that aids in dissipating heat generated
Fig. 4. The electrical circuit used for validation process
Fig. 3. (Left) Proposed design of the system. (Right) Vertical half-view of the isolator
Fig. 5. Actual image of the system during operation