BackgroundGait analysis has been used for decades to quantify knee function in patients with knee osteoarthritis; however, it is unknown whether and to what extent inter-laboratory differences affect the comparison of gait data between studies. Therefore, the aim of this study was to perform an inter-laboratory comparison of knee biomechanics and muscle activation patterns during gait of patients with knee osteoarthritis.MethodsKnee biomechanics and muscle activation patterns from patients with knee osteoarthritis were analyzed, previously collected at Dalhousie University (DAL: n = 55) and Amsterdam UMC, VU medical center (VUmc: n = 39), using their in-house protocols. Additionally, one healthy male was measured at both locations. Both direct comparisons and after harmonization of components of the protocols were made. Inter-laboratory comparisons were quantified using statistical parametric mapping analysis and discrete gait parameters.ResultsThe inter-laboratory comparison showed offsets in the sagittal plane angles, moments and frontal plane angles, and phase shifts in the muscle activation patterns. Filter characteristics, initial contact identification and thigh anatomical frame definitions were harmonized between the laboratories. After this first step in protocol harmonization, the offsets in knee angles and sagittal plane moments remained, but the inter-laboratory comparison of the muscle activation patterns improved.ConclusionsInter-laboratory differences obstruct valid comparisons of gait datasets from patients with knee osteoarthritis between gait laboratories. A first step in harmonization of gait analysis protocols improved the inter-laboratory comparison. Further protocol harmonization is recommended to enable valid comparisons between labs, data-sharing and multicenter trials to investigate knee function in patients with knee osteoarthritis.
MULTIFILE
The pervasiveness of wearable technology has opened the market for products that analyse running biomechanics and provide feedback to the user. To improve running technique feedback should target specific running biomechanical key points and promote an external focus. Aim for this study was to define and empirically test tailored feedback requirements for optimal motor learning in four consumer available running wearables. First, based on desk research and observations of coaches, a screening protocol was developed. Second, four wearables were tested according to the protocol. Third, results were reviewed, and four experts identified future requirements. Testing and reviewing the selected wearables with the protocol revealed that only two less relevant running biomechanical key points were measured. Provided feedback promotes an external focus of the user. Tailoring was absent in all wearables. These findings indicate that consumer available running wearables have a potential for optimal motor learning but need improvements as well.
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Within this study the aim is to measure running workload and relevant running technique key points on varying cadence in recreational runners using a custom build sensor system ‘Nodes’. Seven participants ran on a treadmill at a self-chosen comfortable speed. Cadence was randomly guided by a metronome using 92%, 96%, 100%, 104%, and 108% of the preferred cadence in 2-min trials. Workload was measured by collecting the heart rate and the rating of perceived exertion (RPE 1 to 10) scores. Heart rate data shows that the 100% cadence trial was most economical with a relative heart rate of 99.2%. The 108% cadence trial had the lowest relative RPE score with 96.2%. The sample rate of the Nodes system during this experiment was too low to analyze the key points. Three requirements are proposed for the further engineering of a wearable running system, (i) sampling frequency of minimal 50 Hz, (ii) step-by-step analysis, and (iii) collecting workload in the heart rate and RPE.
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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.
