Nearest neighbour techniques
amputees
kNN was firstly proposed for the parameter The basic principle of kNN consists of comparing new arriving data (instances) with all instances that were captured as reference data in an initial step and considering a subset of them (number of reference instances The comparison of instances refers to the comparison of distances between a new instance and the training instances by using a specified distance measure.After calculating the distance, kNN selects a number In place of directly majority voting after a set of neighbouring instances has been selected (uniform weights), distance-based weighting factors can be calculated for all instances of the selection. For each class, these weightings are summed up, so that the prediction is determined as the class with the maximum sum. This distance weighting is typically introduced to avoid that a majority of class labels from instances which are farther away (but still within the neighbourhood) influences the prediction at the expense of instances which are closer but in minority.kNN has been applied for EMG-based pattern recognition a variety of times. In 1983, a nearest neighbour classifier (Since then, nearest-neighbour-based methods have been evaluated in various EMG-based gesture detection studies rather unsystematically, mainly for comparison to other classifiers, both for able-bodied subjects and amputees (Table Usually, kNN also exposed similarly good detection accuracy as QDA, SVMs, Gaussian as well as Bayesian methods, and performed comparably or better than LDA (see Table Besides these comparisons of different classifiers without external factors, a study of noise influence on kNN exposed a high stability of detection accuracy even for reduced signal-to-noise ratios if The mid-term performance for discovering the influence of electrode shift on kNN showed a basically constant average performance from one day to another [Finally, kNN has shown to be comparably insensitive to the reduction of recording sample rate. In an experiment, kNN achieved higher accuracies than SVM and LDA at all sampling rates, and the performance reduction for frequency reduction was not as steep as for other classifiers [