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dc.contributor.authorBoudali, Ahmed Mounir
dc.date.accessioned2019-04-29
dc.date.available2019-04-29
dc.date.issued2018-09-30
dc.identifier.urihttp://hdl.handle.net/2123/20326
dc.description.abstractThe ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints.en_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectHuman locomotionen_AU
dc.subjectExoskeletonen_AU
dc.subjectRehabilitationen_AU
dc.subjectSystem identificationen_AU
dc.subjectData analysisen_AU
dc.subjectGait pattern generatoren_AU
dc.titleSystem Identification of Bipedal Locomotion in Robots and Humansen_AU
dc.typeThesisen_AU
dc.type.thesisDoctor of Philosophyen_AU
usyd.facultyFaculty of Engineering and Information Technologies, School of Aerospace, Mechanical and Mechatronic Engineeringen_AU
usyd.degreeDoctor of Philosophy Ph.D.en_AU
usyd.awardinginstThe University of Sydneyen_AU


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