A novel type of application for the exploration of enclosed or otherwise difficult to access environments requires large quantities of miniaturized sensor nodes to perform measurements while they traverse the environment in a “go with the flow” approach. Examples of these are the exploration of underground cavities and the inspection of industrial pipelines or mixing tanks, all of which have in common that the environments are difficult to access and do not allow position determination using e.g. GPS or similar techniques. The sensor nodes need to be scaled down towards the millimetre range in order to physically fit through the narrowest of parts in the environments and should measure distances between each other in order to enable the reconstruction of their positions relative to each other in offline analysis. Reaching those levels of miniaturization and enabling reconstruction functionality requires: 1) novel reconstruction algorithms that can deal with the specific measurement limitations and imperfections of millimetre-sized nodes, and 2) improved understanding of the relation between the highly constraint hardware design space of the sensor nodes and the reconstruction algorithms. To this end, this work provides a novel and highly robust sensor swarm reconstruction algorithm and studies the effect of hardware design trade-offs on its performance. Our findings based on extensive simulations, which push the reconstruction algorithm to its breaking point, provide important guidelines for the future development of millimetre-sized sensor nodes.
Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
Wireless sensor networks are becoming popular in the field of ambient assisted living. In this paper we report our study on the relationship between a functional health metric and features derived from the sensor data. Sensor systems are installed in the houses of nine people who are also quarterly visited by an occupational therapist for functional health assessments. Different features are extracted and these are correlated with a metric of functional health (the AMPS). Though the sample is small, the results indicate that some features are better in describing the functional health in the population, but individual differences should also be taken into account when developing a sensor system for functional health assessment.
Size measurement plays an essential role for micro-/nanoparticle characterization and property evaluation. Due to high costs, complex operation or resolution limit, conventional characterization techniques cannot satisfy the growing demand of routine size measurements in various industry sectors and research departments, e.g., pharmaceuticals, nanomaterials and food industry etc. Together with start-up SeeNano and other partners, we will develop a portable compact device to measure particle size based on particle-impact electrochemical sensing technology. The main task in this project is to extend the measurement range for particles with diameters ranging from 20 nm to 20 um and to validate this technology with realistic samples from various application areas. In this project a new electrode chip will be designed and fabricated. It will result in a workable prototype including new UMEs (ultra-micro electrode), showing that particle sizing can be achieved on a compact portable device with full measuring range. Following experimental testing with calibrated particles, a reliable calibration model will be built up for full range measurement. In a further step, samples from partners or potential customers will be tested on the device to evaluate the application feasibility. The results will be validated by high-resolution and mainstream sizing techniques such as scanning electron microscopy (SEM), dynamic light scattering (DLS) and Coulter counter.
De technische en economische levensduur van auto’s verschilt. Een goed onderhouden auto met dieselmotor uit het bouwjaar 2000 kan technisch perfect functioneren. De economische levensduur van diezelfde auto is echter beperkt bij introductie van strenge milieuzones. Bij de introductie en verplichtstelling van geavanceerde rijtaakondersteunende systemen (ADAS) zien we iets soortgelijks. Hoewel de auto technisch gezien goed functioneert kunnen verouderde software, algorithmes en sensoren leiden tot een beperkte levensduur van de gehele auto. Voorbeelden: - Jeep gehackt: verouderde veiligheidsprotocollen in de software en hardware beperkten de economische levensduur. - Actieve Cruise Control: sensoren/radars van verouderde systemen leiden tot beperkte functionaliteit en gebruikersacceptatie. - Tesla: bij bestaande auto’s worden verouderde sensoren uitgeschakeld waardoor functies uitvallen. In 2019 heeft de EU een verplichting opgelegd aan automobielfabrikanten om 20 nieuwe ADAS in te bouwen in nieuw te ontwikkelen auto’s, ongeacht prijsklasse. De mate waarin deze ADAS de economische levensduur van de auto beperkt is echter nog onvoldoende onderzocht. In deze KIEM wordt dit onderzocht en wordt tevens de parallel getrokken met de mobiele telefonie; beide maken gebruik van moderne sensoren en software. We vergelijken ontwerpeisen van telefoons (levensduur van gemiddeld 2,5 jaar) met de eisen aan moderne ADAS met dezelfde sensoren (levensduur tot 20 jaar). De centrale vraag luidt daarom: Wat is de mogelijke impact van veroudering van ADAS op de economische levensduur van voertuigen en welke lessen kunnen we leren uit de onderliggende ontwerpprincipes van ADAS en Smartphones? De vraag wordt beantwoord door (i) literatuuronderzoek naar de veroudering van ADAS (ii) Interviews met ontwerpers van ADAS, leveranciers van retro-fit systemen en ontwerpers van mobiele telefoons en (iii) vergelijkend rij-onderzoek naar het functioneren van ADAS in auto’s van verschillende leeftijd en prijsklassen.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
