Search

Electromyogram (EMG) pattern recognition is an advanced signal-processing technique that has long been investigated as a method of improving the control of powered upper-limb prostheses. Several factors have recently been identified that affect the robustness of EMG pattern recognition, such as changes in limb position and performing dynamic activities. Some researchers have suggested that integrating accelerometry (ACC) data with EMG signals may provide the classifier with additional information about the limb position and therefore reduce the limb position effect. In these studies, however, it was assumed that the classifier was trained with data from all possible limb positions. This work investigates the suitability of combining ACC and EMG data when the training occurs only in a subset of tested positions. It is shown that, unless the classifier is trained in most of the possible positions, the integration of accelerometry data with EMG data can significantly degrade performance compared with EMG alone. In addition to the possible degradation of performance, because of the excessive training time, training the system in many positions is not clinically practical. Consequently, a dynamic training method that moves the residual limb through the regions of interest is introduced. This approach is shown to minimize training time while improving performance when combining ACC and EMG data. [ABSTRACT FROM AUTHOR]

Background: An increasingly aging society and consequently rising number of patients with poststroke-related neurological dysfunctions are forcing the rehabilitation field to adapt to ever-growing demands. Although clinical reasoning within rehabilitation is dependent on patient movement performance analysis, current strategies for monitoring rehabilitation progress are based on subjective time-consuming assessment scales, not often applied. Therefore, a need exists for efficient nonsubjective monitoring methods. Wearable monitoring devices are rapidly becoming a recognized option in rehabilitation for quantitative measures. Developments in sensors, embedded technology, and smart textile are driving rehabilitation to adopt an objective, seamless, efficient, and cost-effective delivery system. This study aims to assist physiotherapists' clinical reasoning process through the incorporation of accelerometers as part of an electronic data acquisition system. Methods: A simple, low-cost, wearable device for poststroke rehabilitation progress monitoring was developed based on commercially available inertial sensors. Accelerometry data acquisition was performed for 4 first-time poststroke patients during a reach-press-return task. Results: Preliminary studies revealed acceleration profiles of stroke patients through which it is possible to quantitatively assess the functional movement, identify compensatory strategies, and help define proper movement. Conclusion: An inertial data acquisition system was designed and developed as a low-cost option for monitoring rehabilitation. The device seeks to ease the data-gathering process by physiotherapists to complement current practices with accelerometry profiles and aid the development of quantifiable methodologies and protocols. [ABSTRACT FROM AUTHOR]