This paper argues for a Problem Based Learning (PBL) design that promotes digital tool usage in entrepreneurship and innovation management education, in order to develop students’ innovative behavior and entrepreneurial orientation. Survey data were collected from 89 students in Germany, the Netherlands, and Poland. The results of the study show that PBL activities positively impact students’ digital tool usage, innovative behavior, and entrepreneurial orientation. The results also provide support for the full mediating role of students’ innovative behavior in the relationship between PBL activities and students’ entrepreneurial orientation. Therefore, based on this research we encourage Higher Education Institutions to integrate effective skill sets into innovation and entrepreneurship education by integrating the usage of digital tools into PBL open-source educational resources.
In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
The present study aims to contribute to the body of knowledge on HRD in small businesses by providing a detailed investigation of the role that owner-managers play in enabling social learning and performance in small firms. The investigation focusses particularly on the specific relationships of the social-interdependence orientation and social competence of owner-managers with their social learning behaviour, as well as with the performance of their smallbusinesses within the pig-production sector in the Republic of Korea. A survey was conducted amongst nearly 200 Korean ownermanagers of pig farms. The results indicate that social interdependence orientations and social competencies have a significant relationship with social learning behaviour. Self-promotion and a cooperative orientation are especially important, with selfpromotion taking precedence for social learning behaviour of a more ‘internal’ nature, and a cooperative attitude being more important social learning behaviour of a more ‘external’ nature. Social competence and social interdependence did not have a significant relationship with performance, but social learning behaviour did. The results further highlight the importance of individual social characteristics to social learning behaviour occurring outside highly structured educational settings, in addition to demonstrating that the competence and attitudes required are determined by the type of interaction partner.
MULTIFILE
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.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
Dit project maakt deel uit van een langer lopend onderzoek naar de vaak onbewuste waardenoriëntatie van studenten met betrekking tot hun professionele handelen. Bewustwording en explicitering van een persoonlijke waardenoriëntatie draagt bij aan een groter aanpassingsvermogen van professionals, zeker in complexe vraagstukken.Doel Dit project beoogt een werkvormenboek die reflectie over persoonlijke, professionele en maatschappelijke waarden op toegankelijke wijze op gang wil brengen. Het zal bijdragen aan perspectiefwisseling met de centrale vraag ‘kan het ook anders?’ Resultaten Een boek met een toegankelijke theoretische inleiding over het begrip normatieve of waardenbewuste professionalisering, gevolgd door een twintigtal werkvormen die bijdragen aan perspectiefwisseling. Looptijd 01 december 2018 - 01 april 2021 Aanpak De onderzoeker zal samen met experts uit verschillende vakgebieden werkvormen ontwikkelen en deze testen in studentenleerteams van de HUpabo. Relevantie/impact De HUpabo is gestart met het vormgeven van leerteamleren en waardenbewust, ofwel normatief professionaliseren. Studenten maken gebruik van het narratief zelfportret dat ontleend is aan het promotieonderzoek Life Orientation for Professionals (2018). Voor het leerteamleren is er dringend behoefte aan integratie van die portret door middel van uitdagende reflectie-instrumenten.
