Bernhard J.F. van Beijnum

A framework for the comparison of mobile patient monitoring systems

A mobile patient monitoring system makes use of mobile computing and wireless communication technologies for continuous or periodic measurement and analysis of biosignals of a mobile patient. In a number of trials these systems have demonstrated their user-friendliness, convenience and effectiveness for both patients and healthcare professionals. In this paper we propose a generic architecture, associated terminology and a classificatory framework for comparing mobile patient monitoring systems. We then apply this comparison framework to classify six mobile patient monitoring systems selected according to the following criteria: use of diverse mobile communication techniques, evidence of practical trials and availability of sufficient published scientific information. We also show how to use this framework to determine feature sets of prospective real-time mobile patient monitoring systems using the example of epilepsy monitoring. This paper is aimed at both healthcare professionals and computer professionals. For healthcare professionals, this paper provides a general understanding of technical aspects of the mobile patient monitoring systems and highlights a number of issues implied by the use of these systems. The proposed framework for comparing mobile patient monitoring systems can be used by healthcare professionals to determine feature sets of prospective mobile patient monitoring systems to address particular healthcare related needs. Computer professionals are expected to benefit by gaining an understanding of the latest developments in the important emerging application area of mobile patient monitoring systems.

Automatic identification of inertial sensor placement on human body segments during walking

BackgroundCurrent inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically.MethodsWalking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis).Results and conclusionsAfter testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method.

Performance evaluation of the context-aware handover mechanism for the nomadic mobile services in remote patient monitoring

Owing to the recent advances in the mobile middleware technologies, hardware technologies and association with the human user, handheld mobile devices are evolving into data producers and in turn acting as nomadic mobile service providers. For the nomadic mobile service hosted on a multi-homed handheld mobile device, context-awareness provides a capability of selecting the suitable network interface for the data transfer. This paper conducts a performance evaluation of the context-handover mechanism for the nomadic mobile services applied in the remote patient monitoring domain and hosted on a multi-homed handheld mobile device. The experimentation analyzes the suitability of a particular network for the data transfer, the effect of multi-homing on the remote patient monitoring application and the resource utilization on the mobile device. The performance analysis provides us useful insights, which are currently being exploited in the extended middleware architecture for the vertical handover support to the nomadic mobile services.

A Customer Service Management Architecture for the Internet

Managing services on the Internet is becoming more and more complex and time consuming for service providers since services are increasing both in number and complexity. Also the number of users per service is going up. A solution to this problem is to allow the service users themselves to partly manage the services they are using. This is called Customer Service Management, and it will both increase the perceived value of the services to the users as well as lower the operational costs of service management for the service provider. This paper presents an architecture for Customer Service Management in the Internet.

A System for Monitoring Stroke Patients in a Home Environment

Currently, the changes of functional capacity and performance of stroke patients after returning home from a rehabilitation hospital is unknown for a physician, having no objective information about the intensity and quality of a patients daily-life activities. Therefore, there is a need to develop and validate an unobtrusive and modular system for objectively monitoring the stroke patients upper and lower extremity motor function in daily-life activities and in home training. This is the main goal of the European FP7 project named âx80x9cINTERACTIONâx80x9d. A complete sensing system is developed, whereby Inertial Measurement Units (IMU), Knitted Piezoresistive Fabric (KPF) goniometers, KPF strain sensors, EMG electrodes and force sensors are integrated into a modular sensor suit designed for stroke patients. In this paper, we describe the systems architecture. Data from the sensors are captured wirelessly and stored in a remote secure database for later access and processing via portal technology. In collaboration with clinicians and engineers, clinical outcome measures were defined and the question of how to present the data on the web portal was addressed. The first implementation of the complete system includes a basic version of all components and is currently being extended to include all sensors within the INTERACTION system.

Modeling the vagus nerve system with the Unified Modeling Language

Traditionally, the means of describing anatomical and physiological structures of the autonomic nervous system is natural language, drawings and images as represented in the scientific literature. In behavioral studies of this system, mathematical and electrical models and computer simulation tools are in use. In this article, we propose the use of the Unified Modeling Language to describe and specify the anatomical and physiological structures and indicate how these can be enriched to capture the behavioral view as well. Using the metamodel facilities of the language, we propose a domain specific language that captures the domain concepts, their relationships and constraints. Application of the language is demonstrated by modeling the vagus nerve in part.

US and Dutch nurse experiences with fall prevention technology within nursing home environment and workflow: a qualitative study

Abstract Falls remain a major geriatric problem, and the search for new solutions continues. We investigated how existing fall prevention technology was experienced within nursing home nurses environment and workflow. Our NIH‐funded study in an American nursing home was followed by a cultural learning exchange with a Dutch nursing home. We constructed two case reports from interview and observational data and compared the magnitude of falls, safety cultures, and technology characteristics and effectiveness. Falls were a high‐magnitude problem at the US site, with a collectively vigilant safety culture attending to non‐directional audible alarms; falls were a low‐magnitude problem at the NL site which employed customizable, infrared sensors that directed text alerts to assigned staff members mobile devices in patient‐centered care culture. Across cases, 1) a coordinated communication system was essential in facilitating effective fall prevention alert response, and 2) nursing home safety culture is tightly associated with the chosen technological system.

Physical Activity Support Community TogetherActive

Reducing sedentary lifestyle and physical inactivity is getting an increased attention of researchers and health organizations due to its significant benefits on health. In the same direction we are proposing a virtual community system, TogetherActive, which supports people in their daily physical activity. The community is connected to physical activity sensors and provide social support (informational, emotional, instrumental and appraisal supports). In order to increase motivation, individual and group goals, comparison, competition and cooperation are the key concepts considered in the system. This paper presents the design, implementation and usability evaluation of the TogetherActive system.

Formal Modeling of Service Session Management

This paper proposes a concept to apply modeling tools to Multi-Provider Telematics Service Management. The service architecture is based on the framework called Open Service Components which serves as building blocks to compose end-to-end telematics services in terms of service components offered by different service providers. Our work presented in this paper contributes to the abstract way of modeling end-to-end Service Management using Architectural Description Language and an underlying Formal Description Language.

Estimation of vertical ground reaction forces and sagittal knee kinematics during running using three inertial sensors

Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ>0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE <5°. Ground reaction forces are estimated with a mean RMSE < 0.27 BW. Additionaly, peak vertical ground reaction force, loading rate and maximal knee flexion during stance were compared, however, no significant differences were found. With multiple subject training the accuracy of estimating discrete and continuous outcomes decreases, however, good agreement (ρ > 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects.