Signal processing and nearest-neighbour-based methods
action/gesture
REGRESSION
In general, a kNN-based classification approach will be given priority over kNN regression, as the latter exposed a high extent of instability in preliminary experiments.To keep the computational demands as low as possible for an embedded prosthetic control system, we aimed at utilizing time-domain features due to their lower complexity. Specifically, the linear envelope of the signal will be used as input feature. It can be shown that the majority of the discriminatory effect in the widely used Hudgins EMG feature set stems from the mean absolute value [The gestures chosen to evaluate the performance are selected among rest state (In order to evaluate the static performance of the algorithm and specifically to match requirement R1, the cross-validation accuracy on a variety of The parameters which can be altered in kNN for static cross-validation are the number of neighbours to be considered (A problem with kNN classification is the fundamental characteristic that no intermediate states can be predicted. Therefore, kNN classification will be extended by proportionality scaling schemes to provide proportional control.The following concepts are applicable for kNN classification based on majority-voting regarding the occurrences of individual class labels.It is assumed that the intensity of an exerted action/gesture is proportional to the amplitude of the EMG signal’s linear envelope [Furthermore, a threshold for the rest action, i.e. the state where no gesture is exerted, will be introduced (rest magnitude thresholding). The motivation for this is that, if samples are closer to the rest state than to the specific real gesture, it would be classified as rest, until the transition point in the signal amplitude is reached. The rest state usually resides at around zero signal amplitude, unless distinct postures are considered where this might differ due to the limb position effect.The concept of the rest magnitude thresholding consists of measuring the average rest activity and basing a threshold of signal amplitude on this value, possibly altered by further parameters. If this threshold amplitude is exceeded when executing the prediction on a new sample, the classification takes place and a class label of the available ones except rest is assigned. Otherwise, the new sample is considered as rest.Requirement R3 will be evaluated by means of target achievement tests. First, the presented concepts will be evaluated in pilot experiments without being statistically representative. The tendencies obtained are used as a baseline for a user study with multiple subjects following afterwards. For both versions of experiments, several gestures will be tested on different signal intensity levels (for instance exerting just one third of a full wrist flexion), after training solely took place on full intensity level. The single gesture has to be reached and held for a certain period of time without deviating too much within some error range in order to consider the task as successful. For this purpose, the subject will see a visual stimulus in the form of a hand model to be followed, as well as another hand model visualizing the current gesture prediction (as in Fig. The final user study will be conducted in a double-blind manner in order to provide comparability of the algorithms. Therefore, the selection of gestures and intensity levels during one experiment will be randomized. For the purpose of not favouring a single method over another (if there should be a time-dependency of success), the occurrences of methods and levels will be equally distributed across the available time slots.Requirement R4 is met inherently by the standard kNN approach since in every prediction step, each instance of the training set is compared with the sample to be predicted for obtaining the particular distance. This means if there are new samples to be stored in the training set, they are directly taken into account during prediction, thus leading to incrementality.