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The electromyographic signal is the electrical manifestation of the neuromuscular activation associated with a contracting muscle. The surface electromyographic (SEMG) signal represents the current generated by ionic flow across the membrane of the muscle fibers that propagates through the intervening tissues to reach the detection surface of an electrode located over skin (De Luca (2006)). The SEMG signal provides a non-invasive tool for investigating the properties of skeletal muscles (Sommerich et al. (2000)). The main challenge in implementing controlled motion for prosthesis is correctly predicting the user’s motion intention. SEMG signals have been used in an effective way in prosthesis control systems (Merletti & Parker (2004); Parker et al. (2006)). The SEMG signal is very convenient for prosthesis control, because it is intrinsically related to the user’s intention (Hudgins et al. (1993)). Amyoelectric control algorithm should be capable of learning themuscular activation patterns that are used in natural form for typical movements. It also needs robustness against variations in conditions during the operation, and the response time cannot create delays that are noticeable to the user (Fukuda et al. (2003)). Pattern recognition of the SEMG signal allows discriminating amongst the desired classes of limb motion and plays a key role in advanced control of powered prostheses for amputees and for individuals with congenital deficiency in the upper or lower limbs. The success of a myoelectric control scheme depends greatly on the classification accuracy. Electronic knees can be designed for providing different levels of damping during swing, and for adjusting to different walking speeds, assuming they have the appropriate sensors and control algorithms for estimating the knee joint angle and the walking speed. With the appropriate control algorithm, it is possible to program the prosthesis to allow the knee to flex and extend while bearing a subject’s weight (stance flexion). This feature of normal walking is not possible with conventional prostheses. Electronic knees use some form of computational intelligence to control the resistive torque about the knee. Several research groups have been involved in designing prototype knee controllers. Grimes et al. (1977) developed an echo control scheme for gait control, in which a modified knee trajectory from the sound leg is played back on the contralateral side. Popovic et al. (1995) presented a battery-powered active knee joint actuated by direct-current motors, together with a finite state knee controller 22

Myoelectric Knee Angle Estimation Algorithms for Control of Active Transfemoral Leg Prostheses