A multi-modal approach for hand motion classification using surface EMG and accelerometers.

For decades, electromyography (EMG) has been used for diagnostics, upper-limb prosthesis control, and recently even for more general human-machine interfaces. Current commercial upper limb prostheses usually have only two electrode sites due to cost and space limitations, while researchers often experiment with multiple sites. Micro-machined inertial sensors are gaining popularity in many commercial and research applications where knowledge of the postures and movements of the body is desired. In the present study, we have investigated whether accelerometers, which are relatively cheap, small, robust to noise, and easily integrated in a prosthetic socket; can reduce the need for adding more electrode sites to the prosthesis control system. This was done by adding accelerometers to a multifunction system and also to a simplified system more similar to current commercially available prosthesis controllers, and assessing the resulting changes in classification accuracy. The accelerometer does not provide information on muscle force like EMG electrodes, but the results show that it provides useful supplementary information. Specifically, if one wants to improve a two-site EMG system, one should add an accelerometer affixed to the forearm rather than a third electrode.

Examining the adverse effects of limb position on pattern recognition based myoelectric control.

Pattern recognition of myoelectric signals for the control of prosthetic devices has been widely reported and debated. A large portion of the literature focuses on offline classification accuracy of pre-recorded signals. Historically, however, there has been a semantic gap between research findings and a clinically viable implementation. Recently, renewed focus on prosthetics research has pushed the field to provide more clinically relevant outcomes. One way to work towards this goal is to examine the differences between research and clinical results. The constrained nature in which offline training and test data is often collected compared to the dynamic nature of prosthetic use is just one example. In this work, we demonstrate that variations in limb position after training can have a substantial impact on the robustness of myoelectric pattern recognition.

Ergonomic design criteria for a novel laparoscopic tool handle with tactile feedback.

Laparoscopic surgery has many ergonomic disadvantages often not considered in the design of instruments. The poorly designed surgical tools produce inconveniences in both functional and cognitive aspects; including tactile sensation and visual-motor space coordination. The aim of this article is to find out how laparoscopic handle design can be improved by combining classical ergonomic guidelines with tactile feedback related to handle design. The article briefly discusses how the human hand and hand-held tools are used to perform tasks. An ergonomic handle for laparoscopic grasping, with a built-in tactile sensation display, is presented. Our review of laparoscopic instruments reveals important aspects for handle design. It is concluded that there is a need for greater awareness of ergonomic guidelines for users' sensory requirements when designing and manufacturing laparoscopic instruments.