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.

Smart textiles offer ways to integrate sensing and actuating abilities into textile structures found in garments, furniture and other applications such as filters, reinforcements, disposable products and others. A large part of the research being done on smart textiles concerns the possibilities for monitoring human health and wellbeing. In recent years, the research community has shown an increasing interest in measuring pressure using smart textiles. Observations in previous work on electrically conductive fabrics had shown that the conductivity in these fabrics was not always isotropic and the assumption was that the contact resistance between the conductive elements (often yarns) was the source of this anisotropy. The work done in connection to this thesis investigates two questions regarding smart textiles: first electrical interconnections and second electrical sensing. An algorithm and a device for measuring the contact resistance in woven samples were developed. Results from that work showed that the contact resistance of woven samples can be measured and that in the case of metallized yarns the contact resistance does not pose a problem for interconnection. For the sensing part two explanatory models for the capacitance of a functionalized spacer-fabric under compression were developed and tested on measured data. The results indicate that both models provide reasonable agreement with the data up to ca 50% compression.

The rise of interest in wearable sensing of bioelectrical signals conducted via smart textile systems over the past decades has resulted in many investigations on how to develop and evaluate such systems. All measurements of bioelectrical signals are done by way of electrodes. The most critical parameter for an electrode is the skin-electrode impedance. A common method for measuring skin-electrode impedance is the two-lead method, but it has limitations because it relies on assumptions of symmetries of the body impedance in different parts of the body as well as of the skin-electrode impedances. To address this, in this paper we present an easy-to-use and reliable three-lead in vivo method as a more accurate alternative. We aim to show that the in vivo three-lead method overcomes all such limitations. We aim at raising the awareness regarding the possibility to characterize textile electrodes using a correct, accurate and robust method rather than limited and sometimes inadequate and uninformative methods. The three-lead in vivo method eliminates the effect of body impedance as well as all other contact impedances during measurements. The method is direct and measures only the skin-electrode impedance. This method is suitable for characterization of skin-electrode interface of textile electrodes intended for both bioelectrical signals as well as for electrostimulation of the human body. We foresee that the utilization of the three-lead in vivo method has the potential to impact the further development of wearable sensing by enabling more accurate and reliable measurement of bioelectrical signals. 

This paper presents a smartphone-based platform for large-scale, low-cost, long-term naturalistic data collection aimed at vulnerable road users (VRUs). The approach taken is to collect naturalistic movement data from VRUs based on information from the embedded sensors in high-end smartphones. The Smartphone application, LogYard, developed in the current study, allows the recording of high quality data (tri-axial acceleration and rotation at 100 Hz plus GPS position and velocity each second). This way, large data quantities from ATV drivers’ movements during daily use in different use cases, can be transferred from a large number of users and accumulated in a cloud-based server for off-line analysis.

Apart from the description on how data is recorded and managed in the smartphone-based platform, also a procedure on how to include participants to studies and how private integrity issues and informed consent can be handled from a distance is presented.

By means of the presented smartphone based platform, large number of participants taking part in several parallel on-going studies can be easily administered. This makes the platform a powerful tool to use in large-scale, low-cost, long-term studies providing data from large groups of study participants.

The information made available this way can be used to develop automatic crash notification (ACN) systems directed to VRUs based on identifying movements outside what is “normal” for bicyclists, mopedists, motorcyclists and ATV users.

Bioimpedance modeling with equivalent electrical circuits has an important role in various biomedical applications, as it facilitates understanding of underlying physical and electrochemical processes in applications such as body composition measurements and assessment of clinical conditions. However, the estimation of model parameter values is not a straightforward task, especially when complex circuits with fractional-order components [e.g., constant phase elements (CPEs)] are used. In this article, we propose a low-complexity method for parameter estimation of the Cole-impedance model suitable for low-cost embedded hardware (e.g., 8-bit microcontrollers). Our approach uses only the measured real and imaginary impedance, without any specific software package/toolbox, or initial values provided by the user. The proposed method was validated with synthetic (noiseless and noisy) data and experimental right-side, hand-to-foot bioimpedance data from a healthy adult participant. Moreover, the proposed method was compared in terms of accuracy with the recently published relevant work and commercial Electrical Impedance Spectroscopy software (Bioimp 2.3.4). The performance evaluation in terms of complexity (suitable for deployment for the microcontroller-based platform with 256 kB of RAM and 16 MHz clock speed), execution time (18 s for the dataset with 256 points), and cost (< 25) confirms the proposed method in regards to reliable bioimpedance processing using embedded hardware.