Human gait identification has become an active area of research due to increased security requirements. Human gait identification is a potential new tool for identifying individuals beyond traditional methods. The emergence of motion capture techniques provided a chance of high accuracy in identification because completely recorded gait information can be recorded compared with security cameras. The aim of this research was to build a practical method of gait identification and investigate the individual characteristics of gait. For this purpose, a gait identification approach was proposed, identification results were compared by different methods, and several studies about the individual characteristics of gait were performed. This research included the following: (1) a novel, effective set of gait features were proposed; (2) gait signatures were extracted by three different methods: statistical method, principal component analysis, and Fourier expansion method; (3) gait identification results were compared by these different methods; (4) two indicators were proposed to evaluate gait features for identification; (5) novel and clear definitions of gait phases and gait cycle were proposed; (6) gait features were investigated by gait phases; (7) principal component analysis and the fixing root method were used to elucidate which features were used to represent gait and why; (8) gait similarity was investigated; (9) gait attractiveness was investigated. This research proposed an efficient framework for identifying individuals from gait via a novel feature set based on 3D motion capture data. A novel evaluating method of gait signatures for identification was proposed. Three different gait signature extraction methods were applied and compared. The average identification rate was over 93%, with the best result close to 100%. This research also proposed a novel dividing method of gait phases, and the different appearances of gait features in eight gait phases were investigated. This research identified the similarities and asymmetric appearances between left body movement and right body movement in gait based on the proposed gait phase dividing method. This research also initiated an analysing method for gait features extraction by the fixing root method. A prediction model of gait attractiveness was built with reasonable accuracy by principal component analysis and linear regression of natural logarithm of parameters. A systematic relationship was observed between the motions of individual markers and the attractiveness ratings. The lower legs and feet were extracted as features of attractiveness by the fixing root method. As an extension of gait research, human seated motion was also investigated.

Gait study plays an important role in the walking robot, because it is the foundation of walking robots. The robot must first determine the walking pattern and rules, thus we can evolve further design, control, analysis or study. This research focus on hexapod and quadruped walking robots, and establishes a mathematical model which can fully describe natural and artificial gaits, and systematically plan and express them. Another point of this research is planning walk trajectory of robot. Here we purpose a new concept of foot trajectory planning, and establish S-V-A-J models for feet motion. We try to make robots move forward with constant velocity, as a goal, by using piecewise function of cam design theory. Therefore robot can walk with constant velocity and maintain the continuity of acceleration.

Thesis (Ph. D.)--Drexel University, 2004. / Includes abstract and vita. Includes bibliographical references (leaves 73-79).

Currently normalcy indices have not been used to evaluate the gait of normal healthy adults. The purpose of this study was to assess the appropriateness of using a normalcy index in assessing gait patterns by investigating normalcy indices of different compositions created from an adult population. Thirty-seven normal and healthy subjects between the ages of nineteen and sixty-six underwent a clinical gait analysis. Ten trials from the left and right sides of the body were averaged. From the averaged data, sixteen kinematic variables were chosen for further analysis. Principal component analysis was applied to the variables and four separate normalcy indices were formed. They consisted of a right side, left side, left and right combined, and average of both sides indices. For the left side index, 23.41% of the variance was accounted for in the principle component. In the right side index, 25.35% of the variance was accounted for. 25.45% and 23.95% of the variance was accounted by the PC in the average of both sides and the left and right combined indices. The mean index scores of the left and right indices are 6.48 and 8.38. Index score for the average of both sides and left-right combined indices is 6.80. A paired samples t-test showed that the left and right indices were significantly different at the .01 level. The mean of the index from both sides and the left-right combined were not significantly different with an alpha of 0.969. The significant difference between the left and right side indices suggests that with further refinement, normalcy indices may possibly be used to assess the symmetry of gait. / School of Physical Education, Sport, and Exercise Science

DISSERTATION ABSTRACT Li Jin Doctor of Philosophy Department of Human Physiology March 2018 Title: Kinematic and Kinetic Analysis of Walking and Running across Speeds and Transitions between Locomotion States Walking and running are general locomotion activities for human beings. Basic gait patterns and whole body center of mass (COM) dynamic patterns are distinctly different between them. Lower extremity joint mechanics patterns could reflect musculoskeletal coordination characteristics. Change of locomotion tasks and speeds can affect lower extremity joint kinematic and kinetic characteristics, and progression of age may also affect these characteristics. Little is known about change of locomotion tasks and speeds effects on lower extremity joint level kinetic characteristics, and whether there is a connection between COM system and lower extremity system. To address this, twenty healthy subjects were recruited to participate in a series of treadmill tests, including walking (0.8 – 2.0 m/s, with 0.2 m/s intervals), running (1.8 – 3.8 m/s, with 0.4 m/s intervals) and gait mode transition from walking to running, and from running to walking (between 1.8 – 2.4 m/s, 0.1 m/s2). Three-dimensional kinematic and kinetic data were collected in all locomotion tests and used to calculate and analyze outcome variables for lower extremity joints and the COM system across different conditions. Results indicate that change of locomotion speeds significantly affect joint level kinetic characteristics within both walking and running locomotion states. Different locomotion task demands (walking vs. running) require fundamental alteration of lower extremity joint level kinetic patterns, even at the same locomotion speed. Progression of age also affects lower extremity joint level kinematic and kinetic patterns in walking and running across speeds. Additionally, stance phase an energy generation and transfer phenomenon occurred between the distal and proximal joints of the lower extremity in both walk-to-run and run-to-walk transitions. Lastly, a connection exists between whole body COM oscillation patterns and lower extremity joint level kinetic characteristics in running. These findings serve to further clarify the mechanisms involved in change of locomotion tasks and speeds effects on lower extremity joint kinetic patterns, and further establish a connection between the COM system and the lower extremity system. These findings may be beneficial for future foot-ankle assistive device development, potential optimization of gait efficiency and performance enhancement. This dissertation includes previously published and unpublished coauthored material.

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