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Human Gait Monitoring Using Body-Worn Inertial Sensors and Kinematic Modelling


Amin Ahmadi, Francois Destelle, David Monaghan, Kieran Moran, Noel O'Connor, Luis Unzueta, Maria Teresa Linaza

Publication Type: 
Refereed Conference Meeting Proceeding
In this paper, we present a low-cost computationally efficient method to accurately assess Gait by monitoring the 3D trajectory of the lower limb (i.e. 3 segments - foot, tibia and thigh, and 2 joints - ankle and knee). Our method utilises a network of miniaturized wireless inertial sensors, coupled with a suite of sophisticated real-time analysis algorithms and can operate in any unconstrained environment. Firstly, we adopt a modified computationally-efficient, highly accurate and real- time gradient descent algorithm to obtain the 3D orientation of each of the 3 segments. Secondly, by utilising the foot sensor, we successfully detect the stance phase of the human gait cycle, which allows us to obtain drift-free velocity and the 3D position of the foot during functional phases of a gait cycle (i.e. heel strike to heel strike). Thirdly, by setting the foot segment as the root node we calculate the 3D orientation and position of the other 2 segments as well as the ankle and knee joints. Finally, we employ a customised kinematic model adjustment technique to ensure that the motion is coherent with human biomechanical behaviour of the leg. Our method is low-cost, is robust to measurement drift and can accurately monitor human gait outside the lab in any unconstrained environment.
Conference Name: 
IEEE Sensors 2015
IEEE Sensors 2015
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
Korea, South (Republic of Korea)
Research Group: 
Dublin City University (DCU)
Open access repository: