System Development for Monitoring Physiological Parameters in Living Environment

This chapter contributes in system development for physiological parameters monitoring in living environment. It describes the integration of different technologies in terms of collecting physiological data, algorithms for processing collected data and software application for visualization.

The main problem that this research highlights is the creation of a system that could response on the problems that health caring institutions face nowadays during their primary tasks. The goal is to create a system that could decrease the risk of losing personnel or suffering damage through checking and monitoring of the physiological status. The technological approach for solving a medical problem and assessing a patient’s status in ambient living environment is introduced in a more efficient and accurate manner. It means that certain medical algorithms used for initial assessment are researched in the creation of models for assessing (i.e. triage) the medical status. It is based on a Machine Learning (ML) algorithm.

There are many articles that illustrate the use of machine learning algorithms in decision making while assessing the medical status, but the contemporary technological development in terms of hardware, software, operating systems, computer networks, and processing power influence on their extended use in the fields (i.e. initial medical assessment) where they are not sufficiently exploited.

Although ML algorithms are used in numerous medical applications for supporting physicians’ decisions, the implementation in medical emergency has not been enough yet. In addition, automated prediction for the patient’s status in real-time based on ML algorithm and patient vital signs monitoring supports medical centers preparations for the level of treating patients after pre-hospital triage.

The paper is structured as follows. The second section presents related work in the field. The third section talks about the equipment related to measuring psychophysiological parameters, problems with reports of conducted statistical analysis. The fourth section shows the architecture of the whole system that has being developed. Fifth section presents proof from the field experiment. Sixth section presents the approaches in terms of conducted multiple linear regression analysis and correlation over created dataset. At the end there are future research plan and conclusion.

The paper has been inspired by the needs to have developed system that can collect data from living environment including live streaming monitoring [1]. During the research it has been found that some similar works exists [2]. Hailstone et al. [2] were testing the sensor Zephyr BioHarness™ and they have done a research with study case and experimental proof. The accuracy of their research motivated the presented implementation in living environment.

On the other hand, the authors in [3] have compared the accuracy of the BioHarness™ and Vmax metabolic cart as well as the K4 b² portable metabolic measurement system in health environment. The present chapter shows a collection of data in different conditions like sport, military exercises, everyday activity. Devices such as the Bioharness™ are being used in a variety of situations including physical activity, exercise monitoring, and emergency professions. The authors in [4] were testing the dates from the Bioharness in abnormal Heart Rate (HR) conditions which is a one of the expected scenarios in living environment. There are also several authors who are talking about the same topic but they are using some different sensors and methods, like in [5, 6, 7]. New generation smart watches have been used to measure the physiological parameters as HR, Galvanic Skin Resistance (GSR), temperature and EQ02 in a multi parameter body-worn system [7]. There are capable to log and transmit the physiological data describing a wearer’s cardiorespiratory and thermal status. About the monitoring there are works that describe different techniques actually known to monitor HR. In the [8, 9] the authors are focusing in particular on the so-called photo plethysmography (PPG) technique.

An assessment of the reliability of five Bioharness™ variables using a treadmill based protocol is studied in [10]. The authors also describe the correlation coefficient that are used between the variables.

In [11], an application is developed for improving male/female sports condition dependable on their body profile with the e-health platform. There are some authors that compare different sensors for measuring the physiological parameters on older people. They have found that the creation of an acceptable monitoring device for older persons requires from designers and developers to consider the special demands and abilities of the target group [12, 13, 14, 15, 16].

In [17] the authors validate cardiovascular alarms in critically ill patients in an experimental setting by generating a database of physiological data and clinical alarm annotations. They report the current rate of alarms and their clinical validity having collected physiological data at one second intervals while monitoring alarms. It was reported during 982 h of observation to have 5934 alarms, i.e. six alarms per hour. They find out that about 40% of all alarms did not correctly describe the patient condition and were classified as technically false; while 68% of those were caused by manipulation. Only 885 (15%) of all alarms were considered clinically relevant. Most of the generated alarms were threshold alarms (70%) and were related to arterial blood pressure (45%).

In [18] the authors describe the development of a computer system for long-term large-scale recording and storage of multichannel physiological signals that was built using commercial solutions (software and hardware) and existing hospital IT infrastructure. Both numeric (1 Hz) and waveform (62.5–500 Hz) data were captured from 24 Surgical Intensive Care Unit (SICU) bedside monitors simultaneously and stored in a file-based Vital Sign Data Bank (VSDB) during one-year period. The database size is 4.21 TB. Vital signs were recorded from 1175 critically ill patients and up to six Electro Cardiographs (ECG). All type of monitored waveforms and all monitored numeric data were recorded in most of the cases.

Factors and predictors that influence nurses’ intention to use the Electronic Intensive Care Unit (eICU) are presented in [19]. The aim is to analyse the applicability of the Technology Acceptance Model in healthcare settings, and to provide psychometric evidence of the measurement scales used in the study. The study involved 117 participants from two healthcare systems. The acceptance survey on the use of eICU was conducted as a studying instrument. The results show that perceived usefulness is the most influential factor. The merge between tele–intensive care units, informatics, telecommunication technology, tele nursing, and telemedicine is used in [20] to provide expert, evidence-based, and cutting-edge services to critically ill patients.

The book on enhanced living environments [21] presents the theory and the practice in the field using the up-to-date technological solutions. The main aspects of assistive care systems and applications relevant to the highly positive and dynamic scientific domain have attracted much interest in the world of Internet technology in the last decade.

The purpose of this section is to show the data collection from different multisensory platform, which collect bio signals in terms of ECG, Heart Rate, Respiratory Rate. The architecture is shown in Fig. 1.

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The data floating is “wireless” and it is a mobile system with fast data transfer. The use of Bluetooth technology introduced acceptable latency for the measured signals. The data is sent from the user (from the sensor in Fig. 2a) via Bluetooth connection to the smartphone. Also, it can be connected directly to the tablet, where the data is processing by the algorithm that has been used to create the BioHarness App. It could be seen in Fig. 2b and c.

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Processed data via 3G/4G have been transferred to the switch and multicasted to the monitoring center with medical personnel and the cloud.

However, abovementioned sensors were not appropriate for ambient living environment. The most convenient biosensor on the market was BioHarness™ 3 (Fig. 2a). It also could be used in different activities (sport, military exercises, everyday activity). The BioHarness™ 3 captures and transmits comprehensive physiological data from the wearer to mobile and fixed data networks enabling genuine remote monitoring of human performance and condition in the real world.

Concerning experiment in the lab, the following physiological data can be measured: heart rate, respiratory rate, interbeat (R-R) interval, breathing rate, ECG, posture, activity level, peak acceleration. In terms of communication, the connection between BioHarness™ 3 and DellLatitude 3340 Notebook Laptop Computer is established via Bluetooth communication protocol. Also, the communication between Laptop Computer and Samsung Galaxy S7 is established by Samsung OEM Micro USB 2.0 Charging Cable.

Next, Zephyr API for Android operating system is provided by the manufacturer for collecting physiological data (Fig. 2b). Also, regarding Android based API it does not include support for the ECG in the interface.

Communication protocol for transferring physiological data is Bluetooth 2.0 which supports data transfer of up to 3 Mbit/sec.

The above-mentioned laboratory setup is standardized for Android-based application development.

Because there is no function for storing data from sensor the API is upgraded with additional functionalities such as storing data in CSV file. The physiological data are collected in the following order: heart rate, respiratory rate and peak acceleration. Next, the interface is inflated with three additional buttons: “Save Data”, “Weight Chart” and “Read Data” (Fig. 2c).

The “Save Data” button is used to store the data in CSV file (Fig. 2d). It should be stressed that additional methods and variables are added in the class MainActivity.java. Also, AndroidManifest.xml file from API is upgraded with permission for writing data by the following line:

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Another two buttons are mock-up buttons and are planned to gain functionalities in further phase of the system development.

The obtained results about correlation of psychophysiological parameters are very strong. It means that changes in heart beats are linearly followed by peak acceleration and respiratory rate. The tests need to be performed also by different groups of older adults looking for the changes in correlation results related to the age and disease.

The system will be extended with two new modules in the developed Android application adding a fuzzy logic algorithm as a decision instrument to raise an alert. In addition, the collected data is going to be visualized using the CSV file, monitoring center, live streaming module and cloud services.