Identifikasi Sinyal Suara Jantung (PCG) dengan Metode Energi Shannon dan Implementasinya pada IoT (Internet of Things)

Survei pada beberapa negara menunjukkan bahwa penderita penyakit jantung semakin meningkat. Hal ini dipengaruhi oleh beberapa hal, antara lain: gaya hidup, kurang berolahraga, tingkat stress dan makanan yang tidak sehat. Pada saat ini pemeriksaan gejala penyakit jantung dilakukan dengan cara manual misalnya menggunakan stetoskop, pemeriksaan ECG dan Echocardiograf. Akan tetapi pemeriksaan jantung dengan menggunakan stetoskop secara manual ini sangat dipengaruhi oleh kondisi lingkungan, subjektivitas dan pengalaman seorang ahli penyakit jantung atau dokter. Karena itu diperlukan sebuah metode dengan kompleksitas rendah namun mampu diterapkan untuk deteksi dini penyakit jantung. Dalam penelitian ini, algoritma identifikasi sinyal suara jantung dengan memanfaatkan energi Shannon diterapkan pada sebuah mini PC untuk mengetahui posisi dan jarak waktu sinyal S1 dan S2 dalam domain waktu. Selanjutnya, hasil pengolahan tersebut dikirimkan melalui media komunikasi Internet dan ditampilkan pada sebuah aplikasi mobile. Berdasarkan hasil uji coba didapatkan bahwa nilai rata-rata interval S1-S1 sebesar 0.7517 s dan S1-S2 sebesar 0.3202 s. Penelitian ini juga menghitung lama pemrosesan energi Shannon pada mini PC dengan rata- rata waktu yang dibutuhkan selama 0.0441 s. Seluruh data yang telah diolah dan dikirim ke cloud, memiliki rata-rata waktu tunda selama 1.3792 s

Generative Adversarial Network for Photoplethysmography Reconstruction

Photoplethysmography (PPG) is an optical measurement method for blood pulse wave monitoring. The method has been widely applied in both clinical and wearable devices to collect physiological parameters, such as heart rate (HR) and heart rate variability (HRV). Unfortunately, the PPG signals are very vulnerable to motion artifacts, caused by inevitable movements of human users. To obtain reliable results from PPG-based monitoring, methods to denoise the PPG signals are necessary. Methods proposed in the literature, including signal decomposition, time-series analysis, and deep-learning based methods, reduce the effect of noise in PPG signals. However, their performance is insufficient for low signal-to-noise ratio PPG signals, or limited to noise from certain types of activities. Therefore, the aim of this study is to develop a method to remove the motion artifacts and reconstruct noisy PPG signals without any prior knowledge about the noise. In this thesis, a deep convolutional generative adversarial network (DC-GAN) based method is proposed to reconstruct the PPG signals corrupted by real-world motion artifacts. The proposed method leverages the temporal information from the distorted signal and its preceding data points to obtain the clean PPG signal. A GAN-based model is trained to generate succeeding clean PPG signals by previous data points. A sliding window moving at a fixed step on the noisy signal is used to select and update the input for the trained model by the information within the noisy signal. A PPG dataset collected by smartwatches in a health monitoring study is used to train, validate, and test the method in this study. A noisy dataset generated with real-world motion artifacts of different noise levels and lengths is used to evaluate the proposed and baseline methods. Three state-of-the-art PPG reconstruction methods are compared with our method. Two metrics, including maximum peak-to-peak error and RMSSD error, are extracted from the original and reconstructed signals to estimate the reconstruction error for HR and HRV. Our method outperforms state-of-the-art methods with the lowest values of the two evaluation matrices at all noise levels and lengths. The proposed method achieves 0.689, 1.352 and 1.821 seconds of maximum peak-to-peak errors for 5-second, 10-second, and 15-second noise at the highest noise level, respectively, and achieves 0.021, 0.048 and 0.067 seconds of RMSSD errors for the same noise cases. Consequently, our method performs the best in reconstructing distorted PPG signals and provides reliable estimation for both HR and HRV

Fall Detection Using Channel State Information from WiFi Devices

Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

WiFi Sensing at the Edge Towards Scalable On-Device Wireless Sensing Systems

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing research assumes costly offline computing resources and hardware for training machine learning models and for performing model inference. To improve the scalability of WiFi sensing systems, this dissertation introduces techniques for improving machine learning at the edge by thoroughly surveying and evaluating signal preprocessing and edge machine learning techniques. Additionally, we introduce the use of federated learning for collaboratively training machine learning models with WiFi data only available on edge devices. We then consider privacy and security concerns of WiFi sensing by demonstrating possible adversarial surveillance attacks. To combat these attacks, we propose a method for leveraging spatially distributed antennas to prevent eavesdroppers from performing adversarial surveillance while still enabling and even improving the sensing capabilities of allowed WiFi sensing devices within our environments. The overall goal throughout this work is to demonstrate that WiFi sensing can become a ubiquitous and secure sensing option through the use of on-device computation on low-cost edge devices

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

A HARDWARE-SOFTWARE CO-DESIGNED WEARABLE FOR REAL-TIME PHYSIOLOGICAL DATA COLLECTION AND SIGNAL QUALITY ASSESSMENT

In the future, Smart and Connected Communities (S&CC) will use distributed wireless sensors and embedded computing platforms to produce meaningful data that can help individuals, and communities. Here, we presented a scanner, a data reliability estimation algorithm and Electrocardiogram (ECG) beat classification algorithm which contributes to the S&CC framework .In part 1, we report the design, prototyping, and functional validation of a low-power, small, and portable signal acquisition device for these sensors. The scanner was fully tested, characterized, and validated in the lab, as well as through deployment to users homes. As a test case, we show results of the scanner measuring WRAP temperature sensors with relative error within the 0.01% range. The scanner measurement shows distinguish temperature of 1F difference and excellent linear dependence between actual and measured resistance (R2 = 0.998). This device hasdemonstrated the possibility of a small, low-power portable scanner for WRAP sensors.Additionally, we explored the statistical data reliability metric (DReM) to explain the quality of bio-signal quantitatively on a scale between 0.0 -1.0. As proof of concept, we analyzed the ECG signal. Our DReM prediction algorithm measures the reliability of the ECG signals effectively with low Root mean square error = 0.010 and Mean absolute error = 0.008 and coefficient of determination R2 value of 0.990. Finally, we tested our model against the opinions of three independent judges and presented R2 value to determine the agreement between judgments vs our prediction model.We concluded our contribution to the S&CC framework by analyzing ECG beat classification with a pipeline of classifiers that focuses on improving the models performance on identifying minority classes (ventricular ectopic beat, supraventricular ectopic beat). Moreover, we intended to minimize morphological distortion introduced due to indiscriminate use of filtering techniques on ECG signals. Our approach shows an average positive predictive value 95.21%, sensitivity of95.28%, and F-1 score 95.76% respectively