Multi-sensor integration and data fusion for enhancing gait assessment In and Out of the laboratory

Introduction Recently, there has been a growing interest in methods for monitoring individual motor performance during daily-life activities. To this end, inertial miniaturized units (IMU) turned out to be the most relevant technological solution. From direct measures of angular velocity and proper acceleration of sensed body segments, a broad set of spatio-temporal gait variables can be derived through signal morphology analysis, biomechanical models and machine learning techniques. However, the validity of IMU-based methods depends on several factors, including motor impairment severity, environmental context, IMU location. Accurate displacement estimations can be particularly critical. Full acceptance of IMU-based methods for «real world» mobility assessment in clinical programmes needs a rigorous validation and this, in turn, advocates for the development of suitable gold standards. This work deals with the design of a wearable multi-sensor system (INDIP) that, by integrating different sensing technologies, aims at providing the best possible reference for digital gait assessment in real world scenarios. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein