Among various approaches, gait analysis is commonly used to help identify the biometric features of PD.
There are two approaches to gait recognition: the model-based approach [11 13], where human body structure is explicitly modeled, and the appearance-based approach [5, 6, 10, 14 18], where gait is treated as a sequence of holistic binary patterns (silhouettes).
As we approached these new gait challenges, the team brainstormed how, where, and why we might be able to assist.
Atkinson, T. & Smith, M. Oligonucleotide Synthesis: A Practical Approach (ed. Gait, M. J.) 35 81 (IRL, Oxford, 1984).
This work describes a new approach for gait analysis and balance measurement.
The majority of previous approaches to gait recognition have used subspace learning methods which have several shortcomings that we avoid.
To provide an overview of, and evaluate the current evidence on, locomotor training approaches for gait rehabilitation in individuals with incomplete spinal cord injury to identify the most effective therapies.
We design and develop a shoe-integrated sensing system for objective bio-information collection, utilize signal processing algorithms for feature estimation and leverage machine learning as well as statistical analysis approaches for gait pattern examination.
In Section 2, we review the different approaches to gait recognition.
In addition, we evaluate various approaches to gait recognition which are robust against view angles.
In general, there are two main approaches for gait analysis, termed model-based and model-free.
