Description of a new hand/palm-held computerized 3D force measuring system. The system is built for interface (direct) measurement of 3D manual contact force with real-time data presentation. Static calibration was performed of the 3D force sensor with variable preloads to study their effect as well of the prototype system adapted for clinical manual examination and treatment. The new system enables, for the first time, recording and presenting of 3D manual contact forces at the patient-practitioner interface. 3D direct manual contact force measures have the potential to give a more complete and differentiated characterization of patient and practitioner forces than 1D forces. Clinical validity of the prototype system will have to be investigated, and for studying specific clinical manual handling techniques, obvious limitations require further development.
Literature highlights the need for research on changes in lumbar movement patterns, as potential mechanisms underlying the persistence of low-back pain. Variability and local dynamic stability are frequently used to characterize movement patterns. In view of a lack of information on reliability of these measures, we determined their within- and between-session reliability in repeated seated reaching. Thirty-six participants (21 healthy, 15 LBP) executed three trials of repeated seated reaching on two days. An optical motion capture system recorded positions of cluster markers, located on the spinous processes of S1 and T8. Movement patterns were characterized by the spatial variability (meanSD) of the lumbar Euler angles: flexion–extension, lateral bending, axial rotation, temporal variability (CyclSD) and local dynamic stability (LDE). Reliability was evaluated using intraclass correlation coefficients (ICC), coefficients of variation (CV) and Bland-Altman plots. Sufficient reliability was defined as an ICC ≥ 0.5 and a CV < 20%. To determine the effect of number of repetitions on reliability, analyses were performed for the first 10, 20, 30, and 40 repetitions of each time series. MeanSD, CyclSD, and the LDE had moderate within-session reliability; meanSD: ICC = 0.60–0.73 (CV = 14–17%); CyclSD: ICC = 0.68 (CV = 17%); LDE: ICC = 0.62 (CV = 5%). Between-session reliability was somewhat lower; meanSD: ICC = 0.44–0.73 (CV = 17–19%); CyclSD: ICC = 0.45–0.56 (CV = 19–22%); LDE: ICC = 0.25–0.54 (CV = 5–6%). MeanSD, CyclSD and the LDE are sufficiently reliable to assess lumbar movement patterns in single-session experiments, and at best sufficiently reliable in multi-session experiments. Within-session, a plateau in reliability appears to be reached at 40 repetitions for meanSD (flexion–extension), meanSD (axial-rotation) and CyclSD.
MULTIFILE
Inertial measurement units (IMUs) allow for measurements of kinematic movements outside the laboratory, persevering the athlete-environment relationship. To use IMUs in a sport-specific setting, it is necessary to validate sport-specific movements. The aim of this study was to assess the concurrent validity of the Xsens IMU system by comparing it to the Vicon optoelectronic motion system for lower-limb joint angle measurements during jump-landing and change-of-direction tasks. Ten recreational athletes performed four tasks; single-leg hop and landing, running double-leg vertical jump landing, single-leg deceleration and push off, and sidestep cut, while kinematics were recorded by 17 IMUs (Xsens Technologies B.V.) and eight motion capture cameras (Vicon Motion Systems, Ltd). Validity of lower-body joint kinematics was assessed using measures of agreement (cross-correlation: XCORR) and error (root mean square deviation and amplitude difference). Excellent agreement was found in the sagittal plane for all joints and tasks (XCORR > 0.92). Highly variable agreement was found for knee and ankle in transverse and frontal plane. Relatively high error rates were found in all joints. In conclusion, this study shows that the Xsens IMU system provides highly comparable waveforms of sagittal lower-body joint kinematics in sport-specific movements. Caution is advised interpreting frontal and transverse plane kinematics as between-system agreement highly varied.
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.
