MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY OF MECHANICAL ENGINEERING GRADUATION PROJECT ROBOTIC AND ARTIFICIAL INTELLIGENCE DEVELOPMENT OF A WEARABLE AI-BASED DEVICE FOR WRIST PULSE DIAGNOSIS ADVISOR: PhD. BUI HA DUC STUDENT: DAO THANH QUAN LE QUOC TUAN SKL010837 Ho Chi Minh city, July 2023 MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY OF MECHANICAL ENGINEERING GRADUATION THESIS DEVELOPMENT OF A WEARABLE AI-BASED DEVICE FOR WRIST PULSE DIAGNOSIS Supervisor: BUI HA DUC, PhD Student: DAO THANH QUAN Student ID: 19134081 Student: LE QUOC TUAN Student ID: 19134091 Year Of Admission: 2019 - 2023 Ho Chi Minh City, July 2023 HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY OF MECHANICAL ENGINEERING DEPARTMENT OF MECHATRONICS GRADUATION THESIS DEVELOPMENT OF A WEARABLE AI-BASED DEVICE FOR WRIST PULSE DIAGNOSIS Supervisor: BUI HA DUC, PhD Student: DAO THANH QUAN Student ID: 19134081 Class: 19134 Student: LE QUOC TUAN Student ID: 19134091 Class: 19134 Year Of Admission: 2019 - 2023 Ho Chi Minh City, July 2023 TRƯỜNG ĐẠI HỌC SƯ PHẠM KỸ THUẬT TP. HCM CỘNG HOÀ XÃ HỘI CHỦ NGHĨA VIỆT NAM KHOA CƠ KHÍ CHẾ TẠO MÁY Độc lập - Tự do – Hạnh phúc NHIỆM VỤ ĐỒ ÁN TỐT NGHIỆP Học kỳ II/ năm học 2022-2023 Giảng viên hướng dẫn: TS. Bùi Hà Đức…………….……… Sinh viên thực hiện: Lê Quốc Tuấn………………MSSV: 19134091…Điện thoại 0935920245 Đào Thanh Quân……………MSSV: 19134081…Điện thoại 0962600379 1.
Mã số đề tài: 22223DT95 – Tên đề tài: Development of a Wearable AI-based Device for Wrist Pulse Diagnosis 2. Các số liệu, tài liệu ban đầu: ……………. Nội dung chính của đồ án: Xây dựng thiết bị đo và phần mềm xử lý tín hiệu mạch đập ở cổ tay, qua đó chẩn đoán tình trạng sức khỏe của một người ……………. Các sản phẩm dự kiến Thiết bị đo mạch đập cổ tay Phần mềm phân tích chẩn đoán tình trạng sức khỏe người dựa trên tín hiệu mạch đập cổ tay …………….
Ngôn ngữ trình bày: Bản báo cáo: Tiếng Anh Tiếng Việt Trình bày bảo vệ: Tiếng Anh Tiếng Việt TRƯỞNG KHOA TRƯỞNG BỘ MÔN GIẢNG VIÊN HƯỚNG DẪN (Ký, ghi rõ họ tên) (Ký, ghi rõ họ tên) (Ký, ghi rõ họ tên) i COMMITMENT • Project: Development of a Wearable AI-based Device for Wrist Pulse Diagnosis • Lecturer: Bui Ha Duc, Ph.D • Student: Dao Thanh Quan • Student ID: 19134081 - Class: 19134 • Adress: Phuoc Long B Ward, Thu Duc City, Ho Chi Minh City • Phone number: 0962600379 • Email: quandaoforwork@gmail.com • Student: Le Quoc Tuan • Student ID: 19134091 - Class: 19134 • Adress: Linh Chieu Ward, Thu Duc City, Ho Chi Minh City • Phone number: 0935920345 • Email: lequoctuan.com • Graduation thesis submission date: • Commitment: “I affirm that the graduation thesis presented here is the result of my research and efforts. I have not replicated any content from published articles without appropriate citations. Should any breach be identified, I acknowledge full accountability for the consequences.” Ho Chi Minh City, … July 2023 ii ACKNOWLEDGMENT First and foremost, on behalf of my team, I would like to extend my heartfelt thanks to our supervisor, Bui Ha Duc Ph. Your unwavering commitment, expertise, and guidance have been instrumental in shaping my research and steering me toward success.
Your mentorship, patience, and constant encouragement have played a significant role in shaping my academic journey. Your profound knowledge and invaluable insights have helped me overcome challenges and broaden my intellectual horizons. I am truly grateful for your dedication and support. I would also like to extend my sincere appreciation to Ho Chi Minh City University of Technology and Education for providing me with an exceptional learning environment and resources.
The university has been a constant source of inspiration, fostering an atmosphere of growth, innovation, and academic excellence. The diverse faculty and staff have been instrumental in shaping my academic and personal development, and I am indebted to them for their commitment to nurturing future scholars and leaders. My deepest gratitude goes to my family for their unwavering love, encouragement, and understanding. Your support has been the foundation of my journey, and I am forever grateful for your sacrifices, belief in me, and the countless ways you have cheered me on.
Your unwavering support has provided me with the strength and motivation to overcome challenges and strive for excellence. Lastly, I would like to express my gratitude to all those who have contributed to my growth and development, directly or indirectly. Your encouragement, advice, and belief in my abilities have been invaluable throughout this challenging yet rewarding journey. Sincerely, Dao Thanh Quan iii ABSTRACT Pulse diagnosis, an integral part of traditional medicine (TM), is a crucial diagnostic method alongside looking, listening, and asking.
It involves the practitioner placing their three fingers on the patient's radial artery at the wrist to analyze their health condition. This method has been used for thousands of years in TM and continues to be highly regarded for its convenience, affordability, and non-invasive nature. Pulse diagnosis remains a strong contender for disease diagnosis even in modern times. Recent studies have highlighted the significance of the wrist pulse signal as a bloodstream signal that can provide valuable insights for disease analysis.
However, traditional pulse diagnosis (TPD) heavily relies on the expertise of practitioners. The measurement and interpretation involved in TPD typically demand years of training for practitioners. Additionally, there is a lack of standardized communication and sharing of pulse signal experiences among different practitioners. These factors present challenges in the further development and widespread application of TPD in modern clinical practice.
Advancements in sensor technologies, signal processing, and pattern recognition have paved the way for significant advancements in the computational analysis of pulse signals. These developments have led to the creation of three types of sensors for pulse signal acquisition: pressure sensors, photoelectric sensors, and ultrasonic sensors. These sensors enable the simulation of pulse signal analysis, resembling the expertise of practitioners. Signal processing and pattern recognition methods have been devised to interpret and analyze pulse signals.
As a result, pulse signals have been extensively investigated for various applications, including pulse waveform classification, prediction, and the diagnosis of numerous diseases such as cholecystitis, nephrotic syndrome, diabetes, and more. The objectives of this study are to research, design, and fabricate a measuring device equipped with a piezo sensor to capture wrist pulse signals, and apply artificial intelligence to analyze the recorded signal. In addition, we have gathered data on wrist pulse (using our developed device) and blood glucose levels (from a commercially available device) from a group of individuals over several days, with measurements taken at various times throughout the day. By applying digital signal processing techniques, we have effectively eliminated noise sources such as high-frequency noise, electrostatic noise, and baseline wander from the collected data.
Our ultimate goal is to leverage this processed data to create an AI model capable of predicting pulse signals and blood glucose levels. Our research findings have been accepted and approved at the 2023 International Conference on System Science and Engineering (ICSSE). iv TABLE OF CONTENTS NHIỆM VỤ ĐỒ ÁN TỐT NGHIỆP. iv TABLE OF CONTENTS.
v LIST OF TABLES. viii LIST OF FIGURES. ix LIST OF ACRONYMS. Scientific and practical significances.
Structure of the report. Introduction to Traditional Medicine. Introduction to the use of sensors in collecting pulse wave signals. Capacitive pressure sensors.
Introduction to noise-removing technique used for pulse signal. Finite Impulse Response (FIR) filter. Infinite Impulse Response (IIR) filter. Wavelet-based filter.
Introduction to metrics used in time series. Introduction to signal-to-noise ratio. Introduction to mean squared error. Introduction to Machine Learning in Traditional Medicine.
Application of machine learning. Introduction to statistical model used in time series forecasting. Introduction to deep learning model used in time series forecasting .DESIGN MECHANICAL SYSTEM. Design the bracelet.
Design the electrical box .DESIGN ELECTRONICS – CONTROL SYSTEM. Electronics – control system’s objectives. Cetral control block. Power supply block.
Signal processing block. Experiment set up .DESIGN PULSE WAVE FORECASTING ALGORITHM. Patient recruitment and protocol. Applying wavelet transform in filtering signal.
Baseline wander removal. Time series forecasting model. Feature selection network. Interpretable multi-head attention .BLOOD GLUCOSE MEASUREMENT.
Blood glucose measuring model. Training procedure: Blood glucose measuring model. Experimental result: Blood glucose measuring model. 77 CONCLUSION AND DISCUSSION.
I vii LIST OF TABLES Table 3.1: Common 3D printer filaments comparison .1: High-Precision AD HAT specifications .2: High-Precision AD HAT Pinouts .1: The comparison between forecasting models. 70 viii LIST OF FIGURES Figure 1.1 Wrist pulse diagnosis in traditional medicine (Source: https://beta.com/healthplus/article/tcm-misconceptions) .1: Traditional Medicine methods .2: Tongue describes organ system map on tongue (Source: https://www.com/blog/traditional-chinese-medicine-tongue-diagnosis) .3: The positions of Cun – Guan – Chi and their equivalent organs .4: PPG sensors placements .5: PPG reflection type working principle .6: Connected circuit between Piezoresistive sensor and Wheatstone Bridge (Source: https://www.com/wps/portal/abacus/solutions/technologies/sensors/pressure-sensors/core- technologies/piezoresistive-strain-gauge/) .7: Capacitive pressure sensor structure (Source: https://www.com/wps/portal/abacus/solutions/technologies/sensors/pressure-sensors/core- technologies/capacitive/) .8: Piezoelectric pressure sensor construction .9: Bi-Sensing Pulse Diagnosis Instrument and holder of pulse-taking posture (Source: [13]) 16 Figure 2.10: (a) Proposed system, (b) Real photograph of the system with real-time UI.11: Signal with power line interference .12: Signal with baseline wander .13: A direct form discrete-time FIR filter of order N (Source: Wikipedia) .14: Wavelet Families of discrete wavelets and continuous wavelets (Source: [4]) .15: Non-stationary and stationary series example (Source: https://otexts.com/fpp2/arima.16: Visualization of LSTM Architecture (Source: https://thorirmar.com/post/insight_into_lstm/) .1: Wrist pulse wave diagnosis method .2: (a) Penetrative method, (b) None-penetrative method (Source: https://www.com/datasheets/Sensors/Flex/MSI-techman.3: CAM mechanism description and design .4: Visualization of the response signals on Labview .5: The first design option .6: The second design option .7: The modified isolator element .8: (a) The bracelet’s frame, (b) Vertical half-view of the isolator element .9: The proposed assembly design of the system .10: Printing process on Elegoo Neptune Plus 3 3D Printer Machine .11: The completed bracelet .12: The proposed design of the electrical box .13: The completed electrical box .1: Block diagram of electronics – control system.2: Power supply adaptor for Raspberry Pi (Source: Internet) .4: Raspberry Pi Zero (source: internet).5: Onboard High-Precision AD HAT (source: https://www.6: Serial Interface Timing Requirements (Source: https://www.com/document- viewer/ads1263/datasheet ) .7: Serial Interface Switching Characteristics (Source: https://www.com/document- viewer/ads1263/datasheet) .8: Data red directly by ADC1(Source: https://www.com/document- viewer/ads1263/datasheet) .9: Electrocardiogram sensor SEN0213 (Source: digikey.10: Block diagram of an additional circuit for device validation .11: 5-second filtered signal of ECG and the proposed device on subject 1.12: 5-second filtered signal of ECG and the proposed device on subject 2 .13: 5-second filtered signal of ECG and the proposed device on subject 3 .14: 5-second filtered signal of ECG and the proposed device on subject 1 .1: Important features inside 2 consecutive pulse wave .2: The boxplots showing distribution of heart rate variability, pulse period, diastolic peak height, systolic peak height on 4 different times including Before Breakfast (BB), After Breakfast (AB), Before Lunch (BL), After Lunch (AL) .3: Data acquisition demonstration .4: Frequency bands division of wavelet transform .5: ‘Sym5’ wavelet used in the proposed algorithm .6: The comparisons between raw and filtered data on 5 different subjects with each row belongs to a subject .7: The positions of pulse’s onset and systolic peak inside pulse wave .8: Pulse’s onset detection algorithm .9: Baseline wander removal algorithm .10: Block diagram of the forecasting model .11: Block diagram of the gated residual network .12: Block diagram of gated linear unit .13: Block diagram of feature selection network .14: Results of iterative inference on 4 different subjects .1: The global number of people with diabetes in 2017 and 2045 (Source: IDF Diabetes Atlas 8th edition) .2: Visualization of fluid compartments (Source: https://courses.com/suny- ap2/chapter/body-fluids-and-fluid-compartments-no-content/) .3: Block diagram of blood glucose measuring model .