Introduction and motivation
amputees
REGRESSION
The development of prosthetics has been continuously improving since the twentieth century. After being solely a cosmetic replacement for amputees, prostheses evolved to body-driven functional devices and, especially beginning in the 1940s, to powered myoelectric systems ([From the existing myographic methods to collect data from muscular activity, this paper focuses on surface electromyography (sEMG), which comprises the capturing, processing and analysis of electromyographic signals, i.e. the changes over time in electric potential originating from skeletal muscles (cf. [Prosthetic control describes the general concept behind the process from capturing signal data (sensor side) via processing and analyzing it to forwarding the data interpretation to a prosthetic device (actuator side)—with potential feedback loops. The (closed-loop) motor-control of the prosthetic actuators themselves, i.e. the single joints of the prosthetic device by, e.g. direct force control, impedance or admittance control will not be considered in the scope of this work.There has been a variety of control schemes presented for EMG-based prosthetic devices in the area of open-loop myocontrol [Pattern-recognition-based myoelectric control schemes utilize machine learning methods to correctly detect gestures by means of classification or regression. Specific features can be calculated from the raw or filtered signal in time-, frequency- and time-frequency domain [This work examines and extends the Commonly applied pattern recognition methods in myocontrol often show disadvantages in terms of generalizability, intuitive control and robustness regarding “electrodes shift, varying force levels” [Referring to the results of kNN in the context of EMG-based prosthetics in Sect. kNN is shown to be relatively insensitive to noise [Its low complexity is a main advantage of kNN specifically in the context of implementation for embedded systems. As for all instance-based machine learning techniques, it does not require an explicit model training. However, it has to be coped with high computational demands during the prediction. Thus, this paper introduces mechanisms for dataset reduction, combines them with kNN and finally analyzes a selected method to be called DSM-kNN.The applicability to embedded, wearable systems is specifically important since “users of modern prosthetics are now given access to applications that can run on external devices capable of fine tuning and setting up gestures or gesture patterns [...] [to] allow[] high-level customization” [Precise sensor placement on specific muscles is usually considered essential for achieving high detection performance from sEMG signals [Although kNN has been applied in several EMG studies, there was no in-depth examination of the strategy’s parameters nor has it been particularly combined with data reduction techniques as in our work.