Traditionally, research education is performed within the universities, and the PhD students are working within a research group. However, technical development and also research is performed within companies, and the need to keep up with the latest findings in research and to strengthen the competence within the private business sector is increasing. At Mälardalen University, we have experience from working in several Industrial Graduate Schools. The collaboration with the companies gets intensified and deepened trough such programmes, and the university tends to keep the good contact with previous PhD students and their companies also many years after their graduation. The Graduate Schools also give the companies good insight in the university world. Presently, we are involved in two Graduate Schools, and several of the PhD projects are focusing within Biomedical Engineering. Further, one of the graduate schools is linked to the research profile Embedded Sensor Systems for Health, which is supported from the same financier. Companies are involved also in the research profile, and through these activities, the Industrial PhD students form a critical mass and can exchange both experience and knowledge with other companies and with university researchers.

Currently we are witnessing fast development of patch ECG devices, some of which have already been extensively evaluated and shown to be useful for detecting arrhythmias. The research about using the patch ECG devices for purposes other than arrhythmia detection has been scarce. The efficiency of patch electrocardiography for a specific purpose can depend on the devices location on the body surface. It is still an open question where to position the ECG patch devices, and should the position depend on the specific purpose and perhaps even be personalized. We present the lead theory of differential leads (ECG leads obtained by patch ECG devices) and discuss its implications on the patch ECG devices positioning on the body surface.

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk. 

A heterogeneous Internet of Things (IoT) architecture for remote health monitoring (RHM) is proposed, that employs Bluetooth and IEEE 802.15.4 wireless connectivity. The RHM system encompasses Shimmer physiological sensors with Bluetooth radio, and OpenMote environmental sensors with IEEE 802.15.4 radio. This system architecture collects measurements in a relational database in a local server to implement a Fog node for fast data analysis as well as in a remote server in the Cloud. 

High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, detection, and treatment of hypertension and related diseases. Pulse transit time (PTT) has attracted interest as an index of BP changes for cuffless BP measurement techniques. Currently, PPT-based BP measurement approaches have improved and are able to relieve the discomfort associated with an inflated cuff such as that used in auscultatory and oscillometric BP measurement techniques. However, PTT can only track the BP variation in high frequency (HF) which limits the true representation of BP changes. This paper presents a continuous and cuffless BP monitoring method based on multiparameter fusion. We used photoplethysmogram (PPG) and a two-lead electrocardiogram (ECG) and employed an algorithm based on PTT and the PPG intensity ratio (PIR) to continuously track BP in both high and low frequencies and estimate systolic and diastolic BP. 

The demography is changing towards older people, and the challenge to provide an appropriate care is well known. Sensor systems, combined with IT solutions are recognized as one of the major tools to handle this situation. Embedded Sensor Systems for Health (ESS-H) is a research profile at Mälardalen University in Sweden, focusing on embedded sensor systems for health technology applications. The research addresses several important issues: To provide sensor systems for health monitoring at home, to provide sensor systems for health monitoring at work, to provide safe and secure infrastructure and software testing methods for physiological data management. The user perspective is important in order to solve real problems and to develop systems that are easy and intuitive to use. One of the overall aims is to enable health trend monitoring in home environments, thus being able to detect early deterioration of a patient. Sensor systems, signal processing algorithms, and decision support algorithms have been developed. Work on development of safe and secure infrastructure and software testing methods are important for an embedded sensor system aimed for health monitoring, both in home and in work applications. Patient data must be sent and received in a safe and secure manner, also fulfilling the integrity criteria.

Remote health monitoring can be leveraged by the IoT paradigm. We tested OpenMotes, state-of-the-art IoT devices featuring IEEE 802.15.4 protocol, with Contiki-NG and OpenWSN, two of the most popular IoT operating systems, for the purpose of obtaining data from the OpenMote's sensors. The procedure is not strait forward and requires additional programing of the operating systems. All the steps necessary to make the OpenMote work with Contiki-NG are presented in detail. We also describe how to use Copper Firefox add-on to access the sensors data over CoAP, as well as how to design a functional web interface in Node-RED for the sensors data. 

This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.

This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care@home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care@home.