The dataset contains the results of the simulations performed to estimate in vivo total knee replacement (TKR) kinematics and loads presented in the paper “C. Curreli, F. Di Puccio, G. Davico, L. Modenese, and M. Viceconti, Using Musculoskeletal Models to Estimate in vivo Total Knee Replacement Kinematics and Loads: Effect of Differences Between Models, Frontiers in Bioengineering and Biotechnology, vol. 9, p. 611, 2021, doi: 10.3389/fbioe.2021.703508”. Specifically, the dataset contains a comparison of the kinematic and dynamic results obtained with three different musculoskeletal models developed using the OpenSim software. A comparison between the predicted joint reaction forces and the in vivo loads measured by the instrumented knee implant is also reported. Experimental data used for the simulations were obtained from the fifth edition of the “Grand Challenge Competitions to Predict in vivo Knee Loads” (https://simtk.org/projects/kneeloads).

http://amsacta.unibo.it/6756/

Because loss of mobility is an important feature of many health conditions, there is a need for regulatory accepted walking-related digital mobility outcomes (DMOs) as clinical trial endpoint measures in a variety of disease states. To achieve this, the consortium has elaborated a roadmap that is published (A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach). Part of the roadmap is the technical validation study. The consortium has started to recruit the 120 participants for this study (healthy older adults, Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, proximal femoral fracture) across sites in 3 countries: Germany, United Kingdom and Israel. The conduct of study is challenging due to the COVID-19 pandemic and recruitment is slower than anticipated. This study will identify the best algorithms to quantify real-world walking speed and other relevant characteristics to describe the way we walk using a variety of advanced technology that will then be taken for further clinical validation.

https://www.karger.com/Article/FullText/512513

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online.

https://www.mdpi.com/1424-8220/21/18/6307/htm