# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## 1 Introduction  
In spite of the continuous improvement in the amputation rehabilitation and prosthetic restoration [1], living without a limb limits the daily life activities leading to a lower quality of life. Many people in the world suffer from the loss of a limb. As an example, [PERSON] et al. estimated that 1.6 million people in the USA were suffering this situation in 2005 and predicted to double this number by 2050 [2]. Upper limb amputation is the removal of any part of the arm or forearm. Computations at or above the wrist are considered as a major one, being the most common case the amputation at the transradial level. In the same study [2], 540,000 people living in the USA were estimated to have an upper limb loss, of which around 40,000 correspond to amputations above the wrist. In the last years the upper limb prostheses control has become a growing research field [3]. The prosthetic industry has been developing new prostheses to mitigate the effects of losing a limb. However, despite of all the research and efforts carried out, there is a gap between the newer developments in the academia and the available commercial prostheses. The main problems the research faced have been a low user satisfaction and high prosthesis rejection due to a non-functionaluse [4; 5; 6; 7; 8]. Because of that, nowadays a primary goal in this field is to find prostheses control protocols robust enough to be used in real life. Different biosignals can be used as inputs for the control system. Electromyography (EMG), the measurement of superficial electric potentials during muscle contractions, is the most established input source for predicting the user's intent. Its easy access and processing makes EMG a better option than other input sources such as electroencephalogram (EEG). EMG signals allow to infer the neuromuscular activity information sent to perform a specific movement. Multiple studies show the good performance of EMG signals to predict the user's intent [9; 10; 11; 12; 13; 14; 15; 16; 17]. The control system extracts the most important information from the data and maps it into a feature space. The extracted features are used to train an algorithm that learns to predict the user's intent. This output is presented to the user through some feedback channel, and the whole system is tested in unknown situations. During the last years, there have been developments in all processes of the control loop: new features and feature extraction methods [18; 19], new algorithms based on classification [20; 21; 22] and regression models [23; 24], new feedback procedures [25; 26; 27; 28; 29; 30; 31] and new prostheses technology [32]. Nevertheless, most of these improvements have never been implemented in real life devices. One of the reasons is the lack of usability in real world scenarios. Thus, although these new systems have a high performance in controlled environments, they exhibited a lack of robustness when tested in daily life situations on realistic environments [3; 9; 33]. Several factors could generate non-stationarities in the EMG signals that fed the system limiting a robust performance. The most common problems are: limitations of EMG signal acquisition process [34; 35; 36; 37], arm positioning [38; 39], electrode shifting [39; 40; 41], skin conditions [25], fatigue [42] or time degradation [43]. These factors affected the reliability of modern prosthesis control methods over time and conditions of use. A lot of work is being done in this direction in order to obtain controllers that behave in a more natural and robust way [44; 45; 46; 47; 48]. We have previously proposed an adaptive auto-regressive proportional myoelectric control system [49], which showed a much effective performance in controlled environments, such as laboratory experiments, than today's state of the art regression models.