1 ) : 1 ) michelangelo hand prosthesis with a dry active surface emg electrode ( otto bock healthcare gmbh , vienna , at ) , 2 ) multichannel electrotactile stimulation system ( maxsens , tecnalia , san sebastian , es ) , and 3 ) a laptop pc ( intel(r ) core(tm ) i5 - 4210u cpu at 1.70ghz , 6 gb ram ) . the michelangelo hand is a two degree - of - freedom myoelectric prosthesis capable of producing two types of grasps : palmar ( used in this study ) and lateral . the prosthesis is equipped with an embedded sensor measuring grasping force ( maximum ~100 n ) . one active electrode was used for recording and preprocessing of a bipolar emg signal from the wrist flexor muscle , which was subsequently low - pass filtered at 1.5 hz . the stimulation system was a fully - programmable and integrated electrotactile interface comprising a stimulation unit and a flexible array electrode . the stimulation unit generates biphasic , current - controlled pulses with adjustable stimulation parameters ( 50  1000 s pulse width with 10 s step , 1  400 hz pulse rate with 1 hz step , and 0.1  5 ma amplitude with 0.1 ma step ) . using the multiplexing unit , pulses generated by a single stimulation channel were distributed to 16 independent pads of the array electrode . the stimulation array electrode , which consisted of 16 circular cathodes and a single adjacent anode , was 200 mm long and designed to be placed circumferentially around the forearm ( fig . the size of the electrode was chosen in accordance with an average forearm circumference and the inter - pad distance satisfies the two - point discrimination threshold for electrical stimulation on the forearm . it was made of a polyester layer , and ag / agcl conductive layer , and an insulation coating covering the conductive leads . in order to improve the electrode - skin contact , the pads were covered with conductive hydrogel pads ( ag702 , axelgaard , dk ) . the control loop with sensory feedback was implemented at the laptop pc using a flexible test bench for the evaluation of the human manual control systems , developed using matlab simulink and real - time windows target toolbox . both the prosthesis and the stimulation system were connected to the laptop pc via bluetooth . sensor data ( emg signal and normalized grasping force ) was received from the prosthesis at 100 hz . the normalized linear envelope of the emg signal was used as a control input for the prosthesis , proportionately setting closing velocity and grasping force . these two variables are directly dependent  the force applied by the prosthesis at the moment of the initial contact with an object is proportional to the hand closing velocity . normalized grasping force was the feedback variable transmitted to the stimulation interface , thus closing the loop . the frequency was set to 20 hz and the pulse width to 220 s , while the amplitude for each pad was individually calibrated for each subject to obtain clear and comfortable sensations . the full range of grasping force ( 0  100% ) was divided in 6 subranges ( force levels ) . due to nonlinearity of the prosthesis response , which results in an offset force even when closed at the minimum velocity , the first subrange covered 0  25% , and the remaining force range of 25 2 . five groups of 2 neighboring pads were selected for coding the first 5 levels of the feedback variable : level 1  pads 1 and 2 , level 2  pads 5 and 6 , level 3  pads 8 and 9 , level 4  pads 11 and 12 , and level 5  15 and 16 . the last subrange ( 85  100% ) was coded by the simultaneous activation of 6 pads spreading around the forearm ( pads 1 , 5 , 8 , 9 , 12 and 16 ) . therefore , an increase in the feedback variable was perceived by the subject as electrotactile stimuli moving along the forearm in mediolateral direction , to be concluded with the stimuli spreading around the full forearm circumference , when the force exceeded 85% . three transradial amputees ( 2 males , 1 female , 48  9 yrs . ) participated in the experiment . the experimental protocol was approved by the ethics committee of the specialized hospital for rehabilitation and orthopedic prosthetics belgrade , serbia . the prosthesis and a solid object , set to be grasped by michelangelo hand , as well as the laptop pc , were positioned on the table in front of the subjects . the emg electrode was placed on the wrist flexor muscle of the subject s residual limb , and the stimulation electrode was positioned on the forearm of the intact arm . each subject was asked to perform wrist flexion using maximum voluntary contraction ( mvc ) , and the gain of the emg electrode was set so that the generated signal can reach the saturation level . the range from 10% to 60% of mvc was mapped as the normalized prosthesis input ( 0  100 % ) . since the subjects had no prior experience in using a myoelectric prosthesis or receiving electrotactile stimulation , they were presented with an explanation of working principles of the prosthesis and the stimulation . in the same manner as in 13 , the subjects first went through a short training of myoelectric control . the control of the grasping force was evaluated using the routine grasping task . in this , the subjects were asked to activate the muscles to an appropriate contraction level ( feedforward command ) so that the prosthesis closes and grasps the object with the indicated desired grasping force . they were instructed not to steer the force after contact but rather to use the information on the generated force ( electrotactile feedback ) to adjust the feedforward command in the next trial . out of the six force levels coded through the feedback , the four levels ( levels 2 - 5 ) were used as the target forces . the levels 1 and 6 were left out since these forces could be achieved trivially by performing minimal or maximal contraction , respectively . the experimental session was organized in four blocks of grasping trials and the feedback training with psychometric evaluation . the first and last block of grasping trials were performed in open loop , and the second and third block in closed loop with electrotactile feedback . each block comprised 60 grasping trials , with target forces ( levels 2 - 5 ) pseudorandomized so that each of the target forces appeared 15 times in total . in summary , the first block evaluated the baseline performance of force control , i.e. , the precision and accuracy that the subjects could achieve with no explicit feedback on the generated force . the second and third block aimed at assessing the impact of feedback and in addition served as the training . in these blocks , as explained before , the subjects could exploit feedback in order to adjust their feedforward myoelectric commands across trials . the last block ( no feedback ) evaluated the extent of acute learning , i.e. , the potential retention of the learned feedforward control . feedback training during which the subjects were trained to correctly interpret the stimuli , followed by the psychometric evaluation , was conducted before the start of the second block of grasping trials ( force control with feedback ) . all six electrotactile levels were first presented to the subject from the lowest to the highest . the six stimulation codes were randomly delivered , the subjects were asked to identify the code ( i.e. , corresponding force range ) , and the experimenter provided the correct answer . finally , the psychometric evaluation included 60 trials of randomly ordered electrotactile codes with no feedback about the correct answer . the outcome measure in the psychometric test was success rate ( sr ) of correctly identified stimulation codes ( feedback levels ) . in the routine grasping task , the mean absolute error ( mae ) and the standard deviation of the absolute force error ( stdae ) were calculated for each subject and each session to assess the accuracy and precision of the force control , respectively . the error was computed for each grasping trial as a difference between the normalized target and applied force ( both expressed as a percentage of maximum force ) . the latter was determined as the maximal force reached during the first 200 ms after the touch onset . 1 ) : 1 ) michelangelo hand prosthesis with a dry active surface emg electrode ( otto bock healthcare gmbh , vienna , at ) , 2 ) multichannel electrotactile stimulation system ( maxsens , tecnalia , san sebastian , es ) , and 3 ) a laptop pc ( intel(r ) core(tm ) i5 - 4210u cpu at 1.70ghz , 6 gb ram ) . the michelangelo hand is a two degree - of - freedom myoelectric prosthesis capable of producing two types of grasps : palmar ( used in this study ) and lateral . the prosthesis is equipped with an embedded sensor measuring grasping force ( maximum ~100 n ) . one active electrode was used for recording and preprocessing of a bipolar emg signal from the wrist flexor muscle , which was subsequently low - pass filtered at 1.5 hz . the stimulation system was a fully - programmable and integrated electrotactile interface comprising a stimulation unit and a flexible array electrode . the stimulation unit generates biphasic , current - controlled pulses with adjustable stimulation parameters ( 50  1000 s pulse width with 10 s step , 1  400 hz pulse rate with 1 hz step , and 0.1  5 ma amplitude with 0.1 ma step ) . using the multiplexing unit , pulses generated by a single stimulation channel were distributed to 16 independent pads of the array electrode . the stimulation array electrode , which consisted of 16 circular cathodes and a single adjacent anode , was 200 mm long and designed to be placed circumferentially around the forearm ( fig . the size of the electrode was chosen in accordance with an average forearm circumference and the inter - pad distance satisfies the two - point discrimination threshold for electrical stimulation on the forearm . it was made of a polyester layer , and ag / agcl conductive layer , and an insulation coating covering the conductive leads . in order to improve the electrode - skin contact , the pads were covered with conductive hydrogel pads ( ag702 , axelgaard , dk ) . the control loop with sensory feedback was implemented at the laptop pc using a flexible test bench for the evaluation of the human manual control systems , developed using matlab simulink and real - time windows target toolbox . both the prosthesis and the stimulation system were connected to the laptop pc via bluetooth . sensor data ( emg signal and normalized grasping force ) was received from the prosthesis at 100 hz . the normalized linear envelope of the emg signal was used as a control input for the prosthesis , proportionately setting closing velocity and grasping force . these two variables are directly dependent  the force applied by the prosthesis at the moment of the initial contact with an object is proportional to the hand closing velocity . normalized grasping force was the feedback variable transmitted to the stimulation interface , thus closing the loop . the frequency was set to 20 hz and the pulse width to 220 s , while the amplitude for each pad was individually calibrated for each subject to obtain clear and comfortable sensations . the full range of grasping force ( 0  100% ) was divided in 6 subranges ( force levels ) . due to nonlinearity of the prosthesis response , which results in an offset force even when closed at the minimum velocity , the first subrange covered 0  25% , and the remaining force range of 25 2 . five groups of 2 neighboring pads were selected for coding the first 5 levels of the feedback variable : level 1  pads 1 and 2 , level 2  pads 5 and 6 , level 3  pads 8 and 9 , level 4  pads 11 and 12 , and level 5  15 and 16 . the last subrange ( 85  100% ) was coded by the simultaneous activation of 6 pads spreading around the forearm ( pads 1 , 5 , 8 , 9 , 12 and 16 ) . therefore , an increase in the feedback variable was perceived by the subject as electrotactile stimuli moving along the forearm in mediolateral direction , to be concluded with the stimuli spreading around the full forearm circumference , when the force exceeded 85% . three transradial amputees ( 2 males , 1 female , 48  9 yrs . ) participated in the experiment . the experimental protocol was approved by the ethics committee of the specialized hospital for rehabilitation and orthopedic prosthetics belgrade , serbia . the prosthesis and a solid object , set to be grasped by michelangelo hand , as well as the laptop pc , were positioned on the table in front of the subjects . the emg electrode was placed on the wrist flexor muscle of the subject s residual limb , and the stimulation electrode was positioned on the forearm of the intact arm . each subject was asked to perform wrist flexion using maximum voluntary contraction ( mvc ) , and the gain of the emg electrode was set so that the generated signal can reach the saturation level . the range from 10% to 60% of mvc was mapped as the normalized prosthesis input ( 0  100 % ) . since the subjects had no prior experience in using a myoelectric prosthesis or receiving electrotactile stimulation , they were presented with an explanation of working principles of the prosthesis and the stimulation . in the same manner as in 13 , the subjects first went through a short training of myoelectric control . the control of the grasping force was evaluated using the routine grasping task . in this , the subjects were asked to activate the muscles to an appropriate contraction level ( feedforward command ) so that the prosthesis closes and grasps the object with the indicated desired grasping force . they were instructed not to steer the force after contact but rather to use the information on the generated force ( electrotactile feedback ) to adjust the feedforward command in the next trial . out of the six force levels coded through the feedback , the four levels ( levels 2 - 5 ) were used as the target forces . the levels 1 and 6 were left out since these forces could be achieved trivially by performing minimal or maximal contraction , respectively . the experimental session was organized in four blocks of grasping trials and the feedback training with psychometric evaluation . the first and last block of grasping trials were performed in open loop , and the second and third block in closed loop with electrotactile feedback . each block comprised 60 grasping trials , with target forces ( levels 2 - 5 ) pseudorandomized so that each of the target forces appeared 15 times in total . in summary , the first block evaluated the baseline performance of force control , i.e. , the precision and accuracy that the subjects could achieve with no explicit feedback on the generated force . the second and third block aimed at assessing the impact of feedback and in addition served as the training . in these blocks , as explained before , the subjects could exploit feedback in order to adjust their feedforward myoelectric commands across trials . the last block ( no feedback ) evaluated the extent of acute learning , i.e. , the potential retention of the learned feedforward control . feedback training during which the subjects were trained to correctly interpret the stimuli , followed by the psychometric evaluation , was conducted before the start of the second block of grasping trials ( force control with feedback ) . all six electrotactile levels were first presented to the subject from the lowest to the highest . the six stimulation codes were randomly delivered , the subjects were asked to identify the code ( i.e. , corresponding force range ) , and the experimenter provided the correct answer . finally , the psychometric evaluation included 60 trials of randomly ordered electrotactile codes with no feedback about the correct answer . the outcome measure in the psychometric test was success rate ( sr ) of correctly identified stimulation codes ( feedback levels ) . in the routine grasping task , the mean absolute error ( mae ) and the standard deviation of the absolute force error ( stdae ) were calculated for each subject and each session to assess the accuracy and precision of the force control , respectively . the error was computed for each grasping trial as a difference between the normalized target and applied force ( both expressed as a percentage of maximum force ) . the latter was determined as the maximal force reached during the first 200 ms after the touch onset . individual and averaged sr for all three subjects are presented for each force level and the whole evaluation session . the subjects were able to perceive and identify six electrotactile codes with an average sr of 94  4% . while the inner four levels ( levels 2  5 ) were occasionally misclassified , all subjects were able to recognize levels 1 and 6 with the sr of 100% . figure 3 depicts mae ( left ) and stdae ( right ) for the four blocks of the grasping trials , averaged across the three subjects . as expected , mae was lower when the subjects were provided with the electrotactile feedback  12.9% and 12.6% for blocks 2 and 3 , respectively , compared to 24.4% in the first block moreover , when the feedback was abolished in the fourth block , the mae increased but still remained lower than in the first block with no feedback ( 15.6% vs. 24.4% ) . the trend was similar for the precision of the grasping force control , since the stdae decreased from 14.0% in block 1 to 9.3% in block 2 and 9.7% in block 3 . after the feedback was removed in block 4 , the stdae remained similar ( 9.6% ) to that in the blocks with feedback . the ability of the subjects to understand multichannel electrotactile feedback and exploit it in the closed loop myoelectric control during the routine grasping task was evaluated in three amputated subjects . psychometric tests demonstrated that the subjects were able to identify a set of six electrotactile spatial codes with a high success rate ( 94  4% ) . in the previous study conducted in 10 able - bodied subjects with the same electrode , the sr in recognizing six equally spaced electrotactile stimulation channels was 88 the results indicate that the novel method of spatial coding can improve the recognition of the electrotactile feedback . instead of six levels with equally distributed pads , feedback was coded with five groups of two neighboring pads and one distinct level , associated with the maximal subrange of force , which relied on simultaneous activation of multiple stimulation sites . in this manner , however , it is important to emphasize that the feedback mapping was chosen due to its simplicity , in order to ensure the high recognition rate and understanding of stimuli in psychometric tests . different mapping , which would rely on physiological modeling and the intuitivism of the feedback , could possibly lead to a similar performance in closed loop force control , independent of the results of psychometric test . in routine grasping task , mae and stdae decreased in the blocks with feedback with respect to the first block ( baseline open loop performance ) . therefore , the provision of feedback improved the quality of force control in both accuracy ( mae ) and precision ( stdae ) . importantly , contrary to previous studies , the present experiment included a realistic task , in which the subjects needed to generate four levels of force in a random order ( as when grasping a sequence of objects in real life ) . at the same time , the feedback was transmitted using a practically relevant method , i.e. , a multichannel electrotactile interface implementing a low - resolution feedback ( 6 discrete levels ) . this is an encouraging result implying the benefits of closing the loop in myoelectric prosthetics . in addition , at all times , the subjects had access to indirect feedback sources ( e.g. , motor sound and visual assessment of hand closing velocity ) . although these alternative forms of feedback could partially contribute to faster learning of force control , noticeable difference in performance between block 1 ( baseline ) and block 2 ( in which the electrotactile feedback was introduced ) suggests that electrotactile feedback had a major influence . furthermore , the results demonstrated that the short training of force control with electrotactile feedback ( 120 trials of blocks 2 and 3 ) was enough to improve the performance of open loop grasping ( block 1 vs. block 4 , both without feedback ) . the subjects used the feedback to adjust and learn the myoelectric commands required for producing the four force levels , and they were able to retain and reproduce those feedforward commands once the feedback was removed . this finding suggests that electrotactile feedback can be exploited for learning and training of myoelectric control . we believe that extensive training with electrotactile feedback could result in further improvement in accuracy and precision , eventually leading to an equal performance with and without feedback . however , this is a hypothesis that needs to be tested in the future experiments . in conclusion , the presented study demonstrated that the control of myoelectric prosthesis , both in terms of precision and accuracy , improved when the subjects were provided with electrotactile force feedback . furthermore , the study revealed the learning process , since all the subjects performed better in the final block without feedback compared to the first one . this is an encouraging outcome , suggesting that electrotactile feedback not only improves the performance in myoelectric control , but can also allow short - term learning . the next step is the evaluation of the results in longitudinal study to explore the long - term effects of the closed loop control .
aim of this study was to investigate the feasibility of electrotactile feedback in closed loop training of force control during the routine grasping task . 
 the feedback was provided using an array electrode and a simple six - level spatial coding , and the experiment was conducted in three amputee subjects . 
 the psychometric tests confirmed that the subjects could perceive and interpret the electrotactile feedback with a high success rate . 
 the subjects performed the routine grasping task comprising 4 blocks of 60 grasping trials . in each trial , the subjects employed feedforward control to close the hand and produce the desired grasping force ( four levels ) . 
 first ( baseline ) and the last ( validation ) session were performed in open loop , while the second and the third session ( training ) included electrotactile feedback . 
 the obtained results confirmed that using the feedback improved the accuracy and precision of the force control . 
 in addition , the subjects performed significantly better in the validation vs. baseline session , therefore suggesting that electrotactile feedback can be used for learning and training of myoelectric control .