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.
Multi-sensor integration and data fusion for enhancing gait assessment In and Out of the laboratory
2019-09-30
Gait & Posture 74 34
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
NTRS | 1976
A Data Fusion Algorithm for Multi-Sensor Microburst Hazard Assessment
British Library Conference Proceedings | 1994
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