# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees  
[PERSON]  
1 TECNALIA, Basque Research and Technology Alliance (BRTA), Arteaga Auzoa 25, 48160 Derio, Spain;  
[EMAIL_ADDRESS] (M.P. );  
[EMAIL_ADDRESS] (A.R. );  
[EMAIL_ADDRESS] (J.M. );  
1  
[PERSON]  
1 TECNALIA, Basque Research and Technology Alliance (BRTA), Arteaga Auzoa 25, 48160 Derio, Spain;  
[EMAIL_ADDRESS] (M.P. );  
[EMAIL_ADDRESS] (A.R. );  
[EMAIL_ADDRESS] (J.M. );  
2  
[PERSON]  
3 Aether Biomedical, Krolowjedwigi 43, 61-871 Poznan, Poland; [EMAIL_ADDRESS] (V.B. );  
[EMAIL_ADDRESS] (D.A.) 3  
[PERSON]  
3 Aether Biomedical, Krolowjedwigi 43, 61-871 Poznan, Poland; [EMAIL_ADDRESS] (V.B. );  
[EMAIL_ADDRESS] (D.A.) 3  
[PERSON]  
1 TECNALIA, Basque Research and Technology Alliance (BRTA), Arteaga Auzoa 25, 48160 Derio, Spain;  
[EMAIL_ADDRESS] (M.P. );  
[EMAIL_ADDRESS] (A.R. );  
joseph.mcintyre@tecnalia. To overcome these adversities, prosthetic devices can be an effective solution. But as with any other assistive device, natural interaction between the human and the machine is essential. An ideal prosthetic hand should be intuitive to control and should quickly and reliably detect the intended gesture of the user [3]. But even this is not enough. Imagine carrying an egg in one hand and a glass bottle in the other. If we apply the same force on the egg and on the bottle, one of them will break. A reliable prosthetic hand must not only recognize intended hand posture, it must also successfully decode the user's intended _force_ when grasping objects. An attractive approach to controlling prosthetic devices is to command the device through the activation of remaining muscles, but this is a challenging endeavour. As the remaining muscles of the user's forearm differ considerably among users, the generalization of any decoding algorithm based on measured activation in remnant muscles is not trivial. Machine learning techniques can be seen as the tool that will translate and adapt each individual's remaining muscle response into a concrete command [4; 5]. Indeed, feeding a time series of raw EMG directly to a classifier can be tempting, considering the Deep Learning algorithms that are currently trending in the literature [6], but these techniques require a large number of inputs to train a model. We therefore take the more traditional route: sEMG _signals_ arrive in windows or batches, _features_ are extracted from those windows and those features are used to train a conventional _classifier_. As our goal is to control a self-contained prosthetic device, we have chosen to focus on time-domain features, from amongst the wide variety features that have been proposed [7], because the computational cost is lower than frequency domain features. We also consider that good results in experiments with able-bodied subjects may nevertheless yield poor results when transferred to amputees. Thus, we refer to the work of [PERSON] and colleagues [8] who found that classic features (mean absolute value, root mean square, zero crossing rate, slope sign changes and waveform length) and time-dependent power spectrum descriptors proposed in [9; 10] (TD-PSD) remain functionally coherent when shifting from able-bodied participants to amputees. Thus, in our analysis we prioritize these features over others proposed in [7]. Analogous to the best feature subset identification, there are also many works in the literature comparing classifiers (see e.g., [11; 12; 13]), but few present a systematic quantitative comparison of features [14] and only in [9; 10] were the EMG signals obtained from amputees. More importantly, detecting intended applied force has received little attention; with most studies relying on subjects with intact limbs. We therefore set out to extend the state-of-the-art by examining what machine-learning methodologies might be used to control grip force.