In this paper, we analyze the micro-Doppler signatures of elderly gait patterns in the presence of walking aids using radars. The signatures are based on real data experiments conducted in a laboratory environment using human subjects walking with a walking cane and a walker. Short-time Fourier transform is used to provide the local signal behavior over frequency and to detail the changes in th...

We propose a scheme for gait recognition using cubic higher-order local auto-correlation (CHLAC), discriminant analysis, and k-NN decision rules. CHLAC is based on three-way (x-, y-, and time-dimensional) auto-correlations of pixels in motion images, and it effectively extracts motion features. The method has several properties preferable for recognition: shift-invariance (rendering the method ...

In this work we introduce a new model-based approach towards the 3-D tracking and extraction of gait patterns in human motion. We suggest the use of a hierarchical, structural model of the human body with a novel derivation of system dynamics from hard and soft kinematic constraints. The hard constraints place physical limitations on possible model configurations while the soft constraints repr...

Recognition of a person from gait is a biometric of increasing interest. This paper presents a new approach on silhouette representation to extract gait patterns for human recognition. Silhouette shape of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silho...

The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine In...

This paper deals with background modeling and moving object classification for gait recognition. Current image-based human recognition methods such as fingerprints, face, iris biometric modalities, generally require a cooperative subject views. These methods cannot reliably recognize non cooperating individuals at a distance in the real world under changing environmental conditions. In such con...

Human gait recognition is an important indicator and are extensively studied research area especially with the aging population and rehabilitation applications. Application of gait analysis ranges from diagnosis, monitoring and early detection of potential hazards such as human fall. There are various types of approaches used in gait analysis including wearable, ambient and vision based devices...

1 The human body is a complex structure, and its structural health can be monitored using sensors. A system using wireless inertial measurement units for data acquisition and the monitoring of human gait is described. Gait analysis is used in the medical community to diagnose and evaluate patients with Parkinson’s disease. Currently, such analysis is done subjectively. The system described quan...

The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...

Human locomotion dynamics has been a topic of interest since the past few decades. In many areas such as as humanoid robot design, gait rehabilitation, diagnosis of pathologies, prosthesis design, sport analysis, and motion cognition, the human locomotion is needed to be properly modelled. Dynamics of human locomotion, and the capability of implementing an accurate human gait simulation can be ...