Bioimedical pilot projects e.g., telemedicine, homecare, animal and human trials usually involve several physiological measurements. Technical development of these projects is time consuming and in particular costly. A versatile but affordable biosignal measurement platform can help to reduce time and risk while keeping the focus on the important goal and making an efficient use of resources. In this work, an affordable and open source platform for development of physiological signals is proposed. As a first step an 8–12 leads electrocardiogram (ECG) and respiration monitoring system is developed. Chips based on iCoupler technology have been used to achieve electrical isolation as required by IEC 60601 for patient safety. The result shows the potential of this platform as a base for prototyping compact, affordable, and medically safe measurement systems. Further work involves both hardware and software development to develop modules. These modules may require development of front-ends for other biosignals or just collect data wirelessly from different devices e.g., blood pressure, weight, bioimpedance spectrum, blood glucose, e.g., through Bluetooth. All design and development documents, files and source codes will be available for non-commercial use through project website, BiosignalPI.org.

Impedance Cardiography (ICG) is a non-invasive method for monitoring cardiac dynamics using Electrical Bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper present a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full 3-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recording were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents, will be available on www.BiosignalPI.com, for open access under a Non Commercial-Share A like 4.0 International License.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications. 

Background and objective: Reduced heart rate variability (HRV) is an indicatorof a malfunctioning autonomic nervous system. Resonant frequencybreathing is a potential non-invasive means of intervention for improvingthe balance of the autonomic nervous system and increasing HRV. However,such breathing exercises are regarded as boring and monotonous tasks.The use of gaming elements (gamification) or a full gaming experience is awell-recognised method for achieving higher motivation and engagement invarious tasks. However, there is limited documented knowledge on how todesign a game for breathing exercises. In particular, the influence of additionalinteractive elements on the main course of training has not yet beenexplored. In this study, we evaluated the satisfaction levels achieved usingdifferent game elements and how disruptive they were to the main task, i.e.,paced breathing training.

Methods: An Android flight game was developed with three game modes thatdiffer in the degrees of multitasking they require. Design, development and evaluation were conducted using a user-centred approach, including contextanalysis, the design of game principle mock-ups, the selection of game principlesthrough a survey, the design of the game mechanics and GUI mock-up,icon testing and the performance of a summative study through user questionnairesand interviews. A summative evaluation of the developed gamewas performed with 11 healthy participants (ages 40-67) in a controlled setting.Results: The results confirm the potential of video games for motivatingplayers to engage in HRV biofeedback training. The highest training performanceon the first try was achieved through pure visualisation rather thanin a multitasking mode. Players had higher motivation to play the morechallenging game and were more interested in long-term engagement.Conclusion: A framework for gamified HRV biofeedback research is presented.It has been shown that multitasking has considerable influence onHRV biofeedback and should be used with an adaptive challenge level.