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.
MULTIFILE
This paper describes the work that is done by a group of I3 students at Philips CFT in Eindhoven, Netherlands. I3 is an initiative of Fontys University of Professional Education also located in Eindhoven. The work focuses on the use of computer vision in motion control. Experiments are done with several techniques for object recognition and tracking, and with the guidance of a robot movement by means of computer vision. These experiments involve detection of coloured objects, object detection based on specific features, template matching with automatically generated templates, and interaction of a robot with a physical object that is viewed by a camera mounted on the robot.
DOCUMENT
In wheelchair sports, there is an increasing need to monitor mechanical power in the field. When rolling resistance is known, inertial measurement units (IMUs) can be used to determine mechanical power. However, upper body (i.e., trunk) motion affects the mass distribution between the small front and large rear wheels, thus affecting rolling resistance. Therefore, drag tests – which are commonly used to estimate rolling resistance – may not be valid. The aim of this study was to investigate the influence of trunk motion on mechanical power estimates in hand-rim wheelchair propulsion by comparing instantaneous resistance-based power loss with drag test-based power loss. Experiments were performed with no, moderate and full trunk motion during wheelchair propulsion. During these experiments, power loss was determined based on 1) the instantaneous rolling resistance and 2) based on the rolling resistance determined from drag tests (thus neglecting the effects of trunk motion). Results showed that power loss values of the two methods were similar when no trunk motion was present (mean difference [MD] of 0.6 1.6 %). However, drag test-based power loss was underestimated up to −3.3 2.3 % MD when the extent of trunk motion increased (r = 0.85). To conclude, during wheelchair propulsion with active trunk motion, neglecting the effects of trunk motion leads to an underestimated mechanical power of 1 to 6 % when it is estimated with drag test values. Depending on the required accuracy and the amount of trunk motion in the target group, the influence of trunk motion on power estimates should be corrected for.
DOCUMENT
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.
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.
