Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations
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The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p < 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.
Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application.Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running.Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team.Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.
Gebouwautomatiseringssystemen voor de utiliteitssector zoals kantoren, scholen, ziekenhuizen vereisen steeds meer functionaliteit om tegemoet te komen aan nieuwe eisen en wensen van gebouwbeheer en eindgebruikers op gebied van o.a. comfort, bezetting, onderhoud interieur, afvalbeheer, energie en dergelijke. De recente technologische ontwikkelingen maken het mogelijk om de gebouwbeheersystemen in te zetten voor innovatieve toepassingen. Maar door lastige toegankelijkheid van bestaande systemen kunnen gebouwbeheerders onvoldoende gebruik maken van deze vernieuwingen. Fabrikanten van gebouwbeheersystemen (GBS) hebben hun producten (vaak op basis van BACnet) veelal zo ingericht dat onderlinge competitie en vrije marktwerking voor verschillende vernieuwende elementen op gebied van digitalisering van beheer- en onderhoudstaken moeilijk is. Recente ontwikkelingen maken het mogelijk binnen de field layer van BACnet dat nieuwe devices aan het bestaande gebouwbeheersysteem gekoppeld kunnen worden en reeds bestaande devices kunnen worden aangestuurd. Nieuwe open source data-mining applicaties (bijv. van Rapid Miner, IBM, Oracle) bieden daarbij de mogelijkheid nieuwe gegevens te genereren om het beheer van gebouwen verder te optimaliseren. Deze ontwikkelingen maken de weg vrij voor verdere toepassingen en innovaties en bieden kansen voor betrokken bedrijven in deze sector. Echter, gebouwbeheerders en installateurs zijn nog onwetend of onzeker van de mogelijkheden m.b.t. prestaties, robuustheid, integreerbaarheid en ondersteuning terwijl de behoefte tot nieuwe diensten groeit. In dit KIEM project wordt met een consortium van een sensor/ICT-ontwikkelbedrijf (Octo), een totaal installateur (E+W) (Lomans Amersfoort), een gebouwbeheerder (HU bedrijfsvoering) en drie onderzoekers uit verschillende lectoraten van de hogeschool Utrecht verkend welke open source datamining tools en innovatieve sensorsystemen van belang kunnen zijn voor de huidige gebouwautomatisering. Er wordt verkend waar de knelpunten zijn en waar de kansen liggen tot integratie. Daarbij kan gedacht worden aan diensten op basis van gebouwbeheer zoals gegarandeerd comfortabel binnenklimaat, efficiënte bezettingsgraad van ruimtes, vernieuwend afvalbeheer en optimale energiehuishouding. Maar ook andere potentiële diensten zullen verder worden onderzocht samen met ketenpartners en ICT/sensorsysteem-innovators. Deze verkenningen worden vertaald naar een programma voor vervolgonderzoek.
