We present a method for measuring gait velocity of older adults using data from existing ambient sensor networks. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations, our method imposes no constraints on the elderly. We studied different probabilistic models for the modeling of the duration and the distance of the indoor walking paths. Experiments are carried out on 27 months of sensor data and include repeated assessments from an occupational therapist. We showed that gait velocities can be measured with low variance and correlate with most assessments. The advantage of our monitoring system is that because of the continuous measurements, clearer trends can be extracted than from incidental assessments of the occupational therapist.
From the article: Abstract—By using agent technology, a versatile and modular monitoring system can be built. In this paper, such a multiagentbased monitoring system will be described. The system can be trained to detect several conditions in combination and react accordingly. Because of the distributed nature of the system, the concept can be used in many situations, especially when combinations of different sensor inputs are used. Another advantage of the approach presented in this paper is the fact that every monitoring system can be adapted to specific situations. As a case-study, a health monitoring system will be presented.
In pursuit of competitive advantage in an increasingly globalized and complex environment, organizations are turning to continuous improvement and digitalization to achieve operational excellence. Viewed through the lens of Dynamic Capabilities Theory, the similarities complementarities, and synergies of continuous improvement capability and data analytic capability are examined. Bridging the gap between theory and practice, continuous improvement routines and practices that can be harnessed to accelerate the implementation of data analytical capability are identified. These include Hoshin Kanri to link digitalization projects to organizational strategic, training to develop organizational knowledge of digitalization, problem solving teams to break knowledge silos, and the use of PDCA-type processes for adopting and monitoring the performance of digital technologies.
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CILOLAB contributes to the transition of the UFT-system towards zero emission city logistics in 2025 by examining, developing and enabling alternatives for urban logistics activities. Specifically, CILOLAB focuses on the transferability and scaling-up of successful logistics initiatives; i.e. concepts that facilitate decoupling between transport towards and in cities. CILOLAB is an action-driven partnership where cities cooperate with transport operators, interest groups, research institutes and societal partners and collaboratively develop new approaches for urban logistical solutions. Through continuous monitoring and impact assessment these solutions are evaluated and further developed within this experimentation environment, all contributing to the CILOLAB ambition.
CILOLAB contributes to the transition of the UFT-system towards zero emission city logistics in 2025 by examining, developing and enabling alternatives for urban logistics activities. Specifically, CILOLAB focuses on the transferability and scaling-up of successful logistics initiatives; i.e. concepts that facilitate decoupling between transport towards and in cities. CILOLAB is an action-driven partnership where cities cooperate with transport operators, interest groups, research institutes and societal partners and collaboratively develop new approaches for urban logistical solutions. Through continuous monitoring and impact assessment these solutions are evaluated and further developed within this experimentation environment, all contributing to the CILOLAB ambition.
Cell-based production processes in bioreactors and fermenters need to be carefully monitored due to the complexity of the biological systems and the growth processes of the cells. Critical parameters are identified and monitored over time to guarantee product quality and consistency and to minimize over-processing and batch rejections. Sensors are already available for monitoring parameters such as temperature, glucose, pH, and CO2, but not yet for low-concentration substances like proteins and nucleic acids (DNA). An interesting critical parameter to monitor is host cell DNA (HCD), as it is considered an impurity in the final product (downstream process) and its concentration indicates the cell status (upstream process). The Molecular Biosensing group at the Eindhoven University of Technology and Helia Biomonitoring are developing a sensor for continuous biomarker monitoring, based on Biosensing by Particle Motion. With this consortium, we want to explore whether the sensor is suitable for the continuous measurement of HCD. Therefore, we need to set-up a joint laboratory infrastructure to develop HCD assays. Knowledge of how cells respond to environmental changes and how this is reflected in the DNA concentration profile in the cell medium needs to be explored. This KIEM study will enable us to set the first steps towards continuous HCD sensing from cell culture conditions controlling cell production processes. It eventually generates input for machine learning to be able to automate processes in bioreactors and fermenters e.g. for the production of biopharmaceuticals. The project entails collaboration with new partners and will set a strong basis for subsequent research projects leading to scientific and economic growth, and will also contribute to the human capital agenda.
