Characteristics of muscle activation patterns at the ankle in stroke patients during walking.

Stroke causes impairment of the sensory and motor systems; this can lead to difficulties in walking and participation in society. For effective rehabilitation it is important to measure the essential characteristics of impairment and associate these with the nature of disability. Efficient gait requires a complex interplay of muscles. Surface electromyography(sEMG) can be used to measure muscle activity and to observe disruption to this interplay after stroke. Yet, classification of this disruption in stroke patients has not been achieved. It is hypothesised that features identified from the sEMG signal can be used to classify underlying impairments. A clinically viable gait analysis system has been developed, integrating an in-house wireless sEMG system synchronised with bilateral video and inertial orientation sensors. Signal processing techniques have been extended and implemented, appropriate for use with sEMG. These techniques have focussed on frequency domain features using wavelet analysis and muscle activation patterns using principal component analysis. The system has been used to measure gait from stroke patients and un-impaired subjects. Characteristic patterns of activity from the ankle musculature were defined using principal component analysis of the linear envelope. Patients with common patterns of tibialis anterior activity did not necessarily share common patterns of gastrocnemius or soleus activity. Patients with similar linear envelope patterns did not always present with the same kinematic profiles. The relationship between observable impairments, kinematics and sEMG is seen to be complex and there is therefore a need for a multidimensional view of gait data in relation to stroke impairment. The analysis of instantaneous mean frequency and time-frequency has revealed additional periods of activity not obvious in the linear or raw signal representation. Furthermore, characteristic calf activity was identified that may relate to abnormal reflex activity. This has provided additional information with which to group characteristic muscle activity. An evaluation of the co-activation of gastrocnemius and tibialis anterior muscles using a sub-band filtering technique revealed three groups; those with distinct co-activation, those with little co-activation and those with continuous activity in the antagonistic pair across the stride. Signal features have been identified in sEMG recordings from stroke patients whilst walking extending current signal processing techniques. Common features of the sEMG and movement have been grouped creating a decision matrix. These results have contributed to the field of clinical measurement and diagnosis because interpretation of this decision matrix is related to underlying impairment. This has provided a framework from which subsequent studies can classify characteristic patterns of impairment within the stroke population; and thus assist in the provision of rehabilitative interventions

A Silhouette Based Human Motion Tracking System

This paper proposes a system for model based human motion estimation. We start with a human model generation system, which uses a set of input images to automatically generate a free-form surface model of a human upper torso. We subsequently determine joint locations automatically and generate a texture for the surface mesh. Following this, we present morphing and joint transformation techniques to gain more realistic human upper torso models. An advanced model such as this is used in a system for silhouette based human motion estimation. The presented motion estimation system contains silhouette extraction based on level set functions, a correspondence module, which relates image data to model data and a pose estimation module. This system is used for a variety of experiments: Different camera setups (between one to four cameras) are used for the experiments and we estimate the pose configurations of a human upper torso model with 21 degrees of freedom at two frames per second. We also discuss degenerated cases for silhouette based human motion estimation. Next, a comparison of the motion estimation system with a commercial marker based tracking system is performed to gain a quantitative error analysis. The results show the applicability of the system for marker-less human movement analysis. Finally we present experimental results on tracking leg models and show the robustness of our algorithms even for corrupted image data. 1

A System for Marker-Less Human Motion Estimation

Abstract. In this contribution we present a silhouette based human motion estimation system. The system components contain silhouette extraction based on level sets, a correspondence module, which relates image data to model data and a pose estimation module. Experiments are done in a four camera setup and we estimate the model components with 21 degrees of freedom in two frames per second. Finally, we perform a comparison of the motion estimation system with a marker based tracking system to perform a quantitative error analysis. The results show the applicability of the system for marker-less sports movement analysis.