Preface

Data acquisition is a process of sampling real-world phenomena to collect data that can be manipulated by a computer and software. Data acquisition systems include sensors that convert physical parameters to an electrical signal and utilize computer network technologies for data transfers. Recent advances in biomedical data acquisition systems have led to innovative applications in medical diagnosis, healthcare, and assisted living. The emerging solutions enable the non-invasive and continuous acquisition of valuable biomedical data. Moreover, the collected data can be exchanged using networked systems and analyzed in real time at remote locations.

This book provides insight into the recent advances and applications of biomedical data acquisition technology. It consists of six chapters, each focusing on a specific innovative application.

Chapter 1 concisely reviews several solutions that utilize network-connected sensors for patient activity recognition and monitoring of physiological parameters in healthcare. The considered technological solutions include smart sensors, wireless body sensor networks, and visual sensor networks. The chapter discusses the operation of these networked systems to demonstrate their advantages from the perspective of biomedical data acquisition for representative application examples.

Chapter 2 is devoted to ambient assisted-living systems that can support people in their daily routines by using different types of sensors, mobile devices, computers, communication networks, and software applications. The authors present a data analysis framework that monitors complex patient situations in real time using a web application and a set of sensors. The implemented sensors measure heart rate and breathing rate, analyze gait, and determine the temperature, humidity, and volatile organic compounds of air in the room. Moreover, the proposed system controls an active prosthetic foot to adapt it to the floor covering automatically. The chapter demonstrates that the components of the ambient assisted-living system can communicate with low latency via a heterogeneous network that integrates WiFi, Bluetooth, Gigabit LAN, and 4G+ communication.

Chapter 3 considers acoustic sensor applications for monitoring the joints of the human body. The authors review the use of acoustic emission measurements and vibroarthrography in osteoarthritis diagnosis. These methods' main advantages include the possibility of non-invasive and radiation-free monitoring of variations in joint structure and evaluation of osteoarthritis progression. It is suggested that the acoustic approach could be competitive to state-of-the-art radiographic and magnetic resonance imaging techniques.

Chapter 4 presents a method for controlling advanced prosthetic devices, with the use of an aneural–machine interface. In this method, the introduced data acquisition system translates the human brain's neural activity into control commands for prostheses. Specifically, functional near-infrared spectroscopy was used to acquire data that enable the generation of the control commands for a three degrees-offreedom prosthetic arm. The experimental results show that popular classification

algorithms (artificial neural network and linear discriminant analysis) can be used to predict various arm motions based on the functional near-infrared spectroscopy signals. It is demonstrated that this approach enables the design of improved prostheses for amputees.

Chapter 5 proposes a method to extract informative parameters from the electrocardiogram signal for diagnosing the state of the cardiovascular system. The authors use flicker-noise spectroscopy to analyze correlation relationships in sequences of electrocardiogram irregularities. On this basis, they obtain signal parameters for the normal state of the cardiovascular system and several arrhythmias (ventricular tachycardia, atrial fibrillation, atrial arrhythmia). It is shown that the extracted parameters are useful as input data of an artificial neural network that recognizes pathologies of the cardiovascular system.

Chapter 6 includes an overview of the embedded systems that can be applied for data acquisition in telemedicine, epidemic surveillance, and patient monitoring. The author characterizes popular software and hardware solutions that establish a platform for developing embedded systems. It is also suggested that the embedded systems could contribute to increasing the safety of healthcare workers and patients during the pandemic.

> **Bartłomiej Płaczek** Institute of Computer Science, University of Silesia, Sosnowiec, Poland

> > **1**

**Figure 1**.

**Chapter 1**

Acquisition

*Bartłomiej Płaczek*

**1. Introduction**

important area.

Introductory Chapter: Data

New biomedical technologies can support faster development of disease treatments, prevention, and diagnostic procedures. They are expected to make significant contributions to the quality of life, improve patient healthcare, and reduce the related costs. Advancement of data acquisition techniques is a key prerequisite for the development in biomedical engineering. Recent advances in data acquisition systems, sensor design, and sensor networks allow collection of large volumes of detailed biomedical data. For instance, body area networks with wireless sensors can be used to non-invasively and continuously monitor several physiological parameters and recognize human activities [1]. Other examples are visual sensor networks for supervision of patients during rehabilitation and Internet of Things (IoT) systems with medical devices connected to the internet that can collect valuable data, enable detailed analysis of symptoms and facilitate remote healthcare. Valuable biomedical data can be also acquired using image processing methods for micrographs analysis [2]. This book intends to provide the reader with an insight into the current state-of-the-art in biomedical data acquisition and focuses on the most important developments in this highly

Few examples of the aforementioned data acquisition techniques are discussed in the introductory chapter. In particular, this chapter concisely reviews the selected

Different types of sensors can be connected by a wireless body area network (WBAN) in order to monitor various body functions. Usually, the sensors in

WBANs are placed on the body. Another approach is to implant small sensors inside the human body. Such approach reduces the impact of WBAN on normal activities of the monitored person. Operations performed by the WBAN sensors include collecting data readings of physical body parameters as well as preprocessing and transmitting the data. The preprocessing operations can be implemented to aggregate, compress or denoise raw sensor readings. A wireless communication is used to transmit the preprocessed data from sensors to a remote destination for further processing or storing. The general concept of WBAN operation is illustrated in

The WBAN platforms enable development of ubiquitous medical records in the cloud and on-line healthcare services with disease-alert systems. This technology can contribute to early diagnosis and personalized treatment of patients. It allows the

approaches that utilize network-connected sensors.

**2. Wireless body sensor networks**
