Background: A new tool (OpenGo, Moticon GmbH) was introduced to continuously measure kinetic and temporospatial gait parameters independently through an insole over up to 4 weeks. The goal of this study was to investigate the validity and reliability of this new insole system in a group of healthy individuals.Methods: Gait data were collected from 12 healthy individuals on a treadmill at two different speeds. In total, six trials of three minutes each were performed by every participant. Validation was performed with the FDM‐S System (Zebris). Complete sensor data were used for a within test reliability analysis of over 10000 steps. Intraclass correlation was calculated for different gait parameters and analysis of variance performed.Results: Intraclass correlation for the validation was >0.796 for temporospatial and kinetic gait parameters. No statistical difference was seen between the insole and force plate measurements (difference between means: 36.3 ± 27.19 N; p = 0.19 and 0.027 ± 0.028 s; p = 0.36). Intraclass correlation for the reliability was >0.994 for all parameters measured.Conclusion: The system is feasible for clinical trials that require step by step as well as grouped analysis of gait over a long period of time. Comparable validity and reliability to a stationary analysis tool has been shown.

The individual assessment of the postoperative healing situation contributes significantly to detecting healing disorders, ensuring the mechanical stability of implants, and planning revision surgeries. Our established workflow consists of the following steps: (1) Monitoring of the patients during their treatment course with a motion capturing system as kinematic and sensor insoles for the kinetic gait analysis, (2) transfer of the motion data into the musculoskeletal simulation system AnyBody™ to achieve the corresponding individual muscle and joint forces. (3) Clinical imaging of the patients via postoperative computed tomography scans, ideally combined with a six-rod bone density calibration phantom. (4) Segmentation of the CT images and generation of the corresponding adaptive finite element meshes of the bone-implant systems, including the material parameters based on Hounsfield units and calibration phantom via the software ScanIP™. (5) Based on the patient-specific model, all information from the musculoskeletal simulation and gait analysis is transferred to our biomechanical simulation process as patient-specific constraints. This workflow allows us to simulate individual patient models based on their respective actual motion data over their treatment course. Thus, pathological processes that may lead to non-healing fractures can be detected early after surgery and prevented by adapting the postoperative treatment protocol. Furthermore, it is possible to understand the forces that affect the fracture and its healing process permanently in more detail. The findings demonstrate that the individual motion parameters and fracture morphology influence the local healing parameters and create individual weight-bearing recommendations.