This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of dataset reduction are evaluated considering real-time determinism to allow for the reliable integration into battery-powered portable devices. The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband. Besides offline cross-validation accuracy, success rates in real-time pilot experiments (online target achievement tests) are determined. Based on the assessment of specific dataset reduction techniques' adequacy for embedded control applications regarding accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself promising when applying kNN on the reduced set. A randomized, double-blind user study was conducted to evaluate the respective methods (kNN and kNN with DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier Features (RR-RFF). The kNN-based methods performed significantly better (p<0.0005) than the regression techniques. Between DSM-kNN and kNN, there was no statistically significant difference (significance level 0.05). This is remarkable in consideration of only one sample per class in the reduced set, thus yielding a reduction rate of over 99% while preserving success rate. The same behaviour could be confirmed in an extended user study. With k=1, which turned out to be an excellent choice, the runtime complexity of both kNN (in every prediction step) as well as DSM-kNN (in the training phase) becomes linear concerning the number of original samples, favouring dependable wearable prosthesis applications.

翻译：本文介绍了基于kNN方案的假肢控制手势检测学习技术的设计、实现与验证。为应对实例预测中的高计算需求，考虑实时确定性因素评估了数据集约简方法，以实现其在电池供电便携设备中的可靠集成。通过使用八通道表面肌电臂带，分析了参数化及不同比例方案的影响。除了离线交叉验证精度外，还确定了实时先导实验（在线目标达成测试）的成功率。基于对特定数据集约简技术面向嵌入式控制应用在精度与时间行为方面的充分性评估，决策表面映射在将kNN应用于约简集时展现出潜力。我们开展了一项随机双盲用户研究，将相应方法（kNN及经DSM约简的kNN）与岭回归及采用随机傅里叶特征的RR进行对比。基于kNN的方法性能显著优于回归技术（p<0.0005）。DSM-kNN与kNN之间无统计学显著差异（显著性水平0.05）。考虑到约简集中每类仅保留一个样本——即实现超过99%的约简率同时保持了成功率——这一结果尤为显著。在扩展用户研究中同样证实了这一特性。当k=1时（这被证明是极佳选择），kNN（在每次预测步骤中）与DSM-kNN（在训练阶段中）的运行时复杂度均与原始样本数量呈线性关系，这有利于实现可靠的穿戴式假肢应用。