G Papaioannou

Dynamic muscle fatigue detection using self-organizing maps

Wavelets are used for the processing of signals that are non-stationary and time varying. The electromyogram (EMG) contains transient signals related to muscle activity. Wavelet coefficients are proposed as features for identifying muscle fatigue. By observing the approximation coefficients it is shown that their amplitude follows closely the muscle fatigue development. The proposed method for detecting fatigue is automated by using neural networks. The self-organizing map (SOM) has been used to visualize the variation of the approximation wavelet coefficients and aid the detection of muscle fatigue. The results show that a 2D SOM separates EMG signatures from fresh and fatigued muscles, thus providing a visualization of the onset of fatigue over time. The map is able to detect if muscles have recovered temporarily. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.

Assessment of Internal and External Prosthesis Kinematics during Strenuous Activities Using Dynamic Roentgen Stereophotogrammetric Analysis

Optimal performance of artificial limbs is still largely dependent on the accurate evaluation of their biomechanical behavior. The accumulated expertise of the prosthetist and indirect measurements of socket and joint kinematics are currently used in a trial and error format for prosthesis-socket performance maximization. The accurate direct unobtrusive assessment of residual limb-skin slippage within the socket during dynamic high-speed activities remains an unresolved challenge till date. This assessment is further complicated in the case of transtibial amputees who have previously undergone joint arthroplasty surgery. This study uses a new method of assessment of the combination of three-dimensional (3D) total knee prosthesis kinematics and socket-residual limb kinematics/slippage during high-speed strenuous activities using Biplane Dynamic Roentgen Stereogrammetric Analysis (DRSA) instrumentation. Marker-based assessment of dynamic socket-residual limb and residual bone telescoping motion with as much as 0.03-mm translational and 1.3 degrees rotational accuracy was demonstrated. The in-vivo dynamic accuracy for the model-based (markerless) tracking (MBT) method to track the joint prosthesis was further improved from that reported previously. Quantitatively, measurement bias between DRSA and the MBT methods ranged from −0.012 to −0.11 mm (depending on coordinate axis) for the femoral prosthesis and from 0.004 to 0.048 mm for the socket. The results from this transtibial case study indicated that maximum 3D slippage for some socket-skin-marker pairs reached values of up to 16 mm for the fast-stop task and up to 8 mm for the step-down task. Maximum “deformation” of up to 12.5% is observed for the fast-stop trials and step-down trials between skin-to-skin marker pairs. The respective deformation between skin-to-socket marker pairs reached maximum values of almost 22%. The deformation between femur/tibia edges and skin/socket marker pairs reached maximum values of almost 100%. Relative skin strain calculated from skin-marker pairs reached values that range between 0.01 and 0.1 for step-down and fast-stop trials, respectively. The relative engineering shear (γ) between selected skin-marker clusters that form orthonormal meshes ranged between 81.5 and 129 degrees. This in-vivo, patient-specific, unobtrusive dynamic information is highly accurate and allows socket-residual limb interactions to be presented using 3D visualization tools that were until recently unavailable to the clinician prosthetist. These methods can significantly impact the iterative cycle of socket fitting and evaluation.