Table 1 .

Mann & Reimers (1970)

[55]

1 amputee

Myoelectric elbow prosthesis (Boston arm)

Elbow angle

Vibrotactile cutaneous display—Upper-arm socket

Improved accuracy (radial error) in matching tasks: comparable to standard mechanical prosthesis

Hasson & Manczurowsky (2015)

[54]

27 able-bodied

Myoelectric virtual arm

Arm’s position and velocity

Vibrotactile (linear actuator)—Wrist of dominant arm

No improvements in end-point error, movement time and composite skill. In some cases, detrimental

Guémann et al. (2022)

[87]

16 able-bodied and 7 amputees

Myoelectric virtual arm

Elbow angle

Vibrotactile array—1st third of upper arm/stump

Better performances in reach accuracy compared to no feedback. No further improvement with vision available but preferred according to NASA-TLX (workload evaluation questionnaire)

Marinelli et al. (2023)

[88]

15 able-bodied and 1 congenital limb deficiency

Virtual wrist, manually controlled (keyboard keys)

Wrist angle (prono-supination)

Vibrotactile array (+ Gaussian interpolation spatial encoding)—Forearm

The novel method for continuous information encoding was demonstrated to be effective in providing the subjects with meaningful stimuli about prono-supination of the virtual wrist (average error < 10%). The approach described can be flexible customized in terms of sensation quality

Vargas et al. (2021)

[89]

7 able-bodied

Myoelectrical finger

Finger MCP joint angle

Vibrotactile array—Upper arm

Subjects were able to control a myoelectric finger to the desired angle, both with a position- and velocity-based control, when provided with artificial proprioceptive feedback

Witteveen et al. (2012)

[90]

15 able-bodied and 3 amputees

Virtual hand opening controlled by a scroll wheel

Hand opening (+ touch)

Vibrotactile

vs Electrotactile—Forearm/Stump

In general, vibrotactile feedback performed better in grasping task considering duration, mean absolute error and % correct openings. Electrotactile feedback increases the duration. Addition of touch feedback further improves results

Garenfeld et al. (2020)

[86]

13 able-bodied

Pattern classification-based Myoelectric control of virtual hand multiple DoFs

Wrist rotation and hand aperture

Electrotactile displays (spatial vs amplitude encoding)—Forearm (contralateral)

2 different feedback configurations delivered good performances in terms of completion rate, time to the target, distance error and path efficiency

Han et al. (2023)

[91]

5 able-bodied and 2 amputees

No control [Passive discrimination]

Flexion–Extension of prosthetic wrist

Electrotactile (static and dynamic coding for position and movement senses)—Forearm/Stump

Subjects were able to discriminate among 5 different degrees of flexion–extension of the prosthetic wrist, as well as the transition between one position and another (temporal combination of multiple position sensations). Lower success rates were reported for movement recognition

Patel et al. (2016)

[67]

11 able-bodied

Myoelectric control of virtual and multi-articulated hand

Hand configuration

Electrotactile—Forearm (contralateral to control muscles)

Feedback improved performances in matching task, compared to no feedback condition. Results when visual feedback is available are always better

Earley et al. (2021)

[80]

16 able-bodied

Hybrid positional-myoelectric control of a 2 DoF prosthesis

Joint speed

Frequency-modulated audio feedback

Joint speed feedback reduced reaching errors in ballistic reaching task, only when the control was perturbed by doubling the EMG gain

Bark et al. (2008)

[61]

10 able-bodied

Hand-held force sensor-controlled cursor on screen (virtual arm)

Cursor position

Vibrotactile/Skin stretch—Forearm

Both feedback strategies improved position error results. Overall, skin stretch performed better

Wheeler et al. (2010)

[74]

15 able-bodied

Myoelectric virtual arm

Elbow angle

Skin stretch (rotational)—Upper arm

Lower targeting errors with feedback, compared to no feedback. However, proprioceptive feedback from the contralateral limb obtained the best results

Kayhan et al. (2018)

[82]

11 able-bodied

No control

[Passive discrimination]

Wrist multiple DoFs-related information

Skin stretch—Forearm

Feasibility of the approach demonstrated by means of confusion matrices for discrimination accuracy

Shehata et al. (2019)

[99]

1 amputee (lower limb)

No control

[Passive discrimination]

Ankle movement

Skin stretch + TVI—Stump

Combination of feedback strategies increased the consistency of the illusion by 30–40%, depending on the site where the stretch was applied, with respect to TVI alone. Skin stretch also increases range and speed of the illusory movement

Akhtar et al. (2014)

[100]

5 able-bodied

Myoelectric virtual fingers

Finger joint angle/hand configuration

Skin stretch/Vibrotactile—Forearm

Subjects were able to discriminate 6 different grips with 88% accuracy. In 1 DoF virtual targeting task, performances were similar between skin stretch and vibrotactile feedback

Battaglia et al. (2017)

[96]

18 able-bodied

Myoelectric prosthetic hand

Hand aperture (prosthesis DC motor encoder)

Skin stretch (linear)—Upper arm

The feedback device was successfully integrated with the myoelectric prosthesis. Subjects’ discrimination accuracy improved with respect to no feedback condition

Battaglia et al. (2019)

[103]

44 able-bodied and 1 amputee

Myoelectric prosthetic hand

Hand aperture (prosthesis DC motor encoder)

Skin stretch (linear)—Upper arm

Feedback was effective even in conditions of higher cognitive load (distraction task). Improved performances were reported in a functional test (AM-ULA) and passive discrimination test with the amputee user

Rossi et al. (2019)

[73]

43 able-bodied and 1 amputee

Myoelectric prosthetic hand

Hand aperture

Skin stretch—Forearm

Improved discrimination accuracy both in passive (hand resting on table) and active (actively moving the hand) settings. 75% accuracy was reported, against 33% in no feedback condition

Colella et al. (2019)

[72]

10 able-bodied and 1 agenesia

Myoelectric prosthetic hand

Hand aperture

Skin stretch (“unidirectional” and “pinch”—Forearm/Stump

Similar discrimination accuracies with different configurations of skin stretch were reported

Pylatiuk et al. (2006)

[106]

5 amputees

Myoelectric prosthetic hand

Grip force

Vibrotactile—Stump

Improved ability in regulating the grasping force, with a reduction of 37–54% reported, compared to a vision-only condition

Stepp & Matsuoka (2010)

[107]

8 able-bodied

Phantom Premium 1.0 robotic device (Sensable Technology) for 3D monitoring of the index fingertip

Applied Force

Vibrotactile/Haptic—Upper arm

Addition of vibrotactile feedback resulted in increased virtual box displacement and decreased difficulty ratings compared to vision-only condition, but performances were poorer compared to the ones obtained with the direct haptic feedback provided by the robot. Increased task times were reported with vibrotactile feedback

Witteveen et al. (2014)

[57]

10 able-bodied and 7 amputees

Virtual hand controlled by computer mouse scroll wheel

Hand aperture and grip force

Vibrotactile (position + amplitude modulation)—Different configurations: Forearm and entire upper limb/ Stump

Subjects provided with opening information alone or in combination with force feedback were able to discriminate 4 stiffness levels in 60% of the cases. Performances were significantly better than those obtained without feedback. Force feedback alone was not sufficient to discriminate stiffness. No statistical differences between hand opening info alone and in combination with force

Witteveen et al. (2015)

[58]

10 amputees

Virtual hand controlled by computer mouse scroll wheel

Hand aperture and grip force

Vibrotactile (position + amplitude modulation)—Stump

Performances were similar to those reported in the able-bodied group in a previous study

Ninu et al. (2014)

[105]

9 able-bodied and 4 amputees

Myoelectric prosthetic hand

Hand closing velocity and grip force

Vibrotactile—Forearm/Stump

Vibrotactile feedback was effective in replacing visual feedback. Force feedback was not essential for the control of grip, given that subjects were able to do so predictively by means of closing velocity

Jorgovanovic et al. (2014)

[110]

10 able-bodied

Virtual 1 DoF hand prosthesis controlled through a single-axis contactless joystick

Grasping force

Electrotactile—Forearm

The results showed that subjects were able to learn and scale the force based on electrotactile feedback, resulting in a 72% success rate in grasping objects of different weights. The closed-loop control demonstrated robustness and improved performance compared to feedforward control

Chai et al. (2019)

[104]

15 able-bodied

Single DoF myoelectric hand prosthesis

Grasping angle and force

Electrotactile—Upper arm (ipsilateral to control muscles)

The feedback allowed subjects to discriminate among 4 types of grasped object sizes (87.5%), 3 kinds of stiffness (94%) and 4 levels of grasping forces (73.8%)

Štrbac et al. (2016)

[69]

10 able-bodied and 6 amputees

No control

[Passive discrimination]

Grasping angle and force, wrist rotation and flexion

Electrotactile (spatial + frequency coding)—Forearm/Stump

Subjects were able to discriminate stimulation levels with more than 90% success rate. Amputee subjects demonstrated lower rates than able-bodied

Dosen et al. (2017)

[64]

10 + 10 able bodied

Myoelectric + Virtual hand prosthesis

Grasping force

Electrotactile (spatial/spatial + frequency coding)—Forearm (contralateral to control muscles)

The study suggests that mixed (spatial + frequency) coding is a reliable method for transmitting high-resolution information and offers advantages in terms of compactness compared to other coding schemes. Despite psychometric differences however, the performance in closed-loop control tasks was similar for both coding schemes

Clemente et al. (2017)

[81]

8 able-bodied

Data glove-controlled robotic hand

Grip aperture and force

Augmented reality

AR feedback was successfully integrated into subjects’ sensorimotor control loop. Participants were also able to decouple the two types of information provided. AR feedback allowed the subjects to execute the task more consistently, compared to no feedback condition

Dosen et al. (2015)

[111]

10 able-bodied and 2 amputees

Myoelectric prosthetic hand

EMG + Force/Force

Visual interface

The addition of EMG information reduced twofold force dispersion. More accurate and stable tracking of force was also reported. According to authors, force was controlled predictively (given the anticipatory nature of EMG signal) and with a finer resolution

Schweisfurth et al. (2016)

[68]

11 able-bodied and 1 amputee

Myoelectric prosthetic hand

sEMG envelope vs Force (prosthesis input vs output)

Electrotactile (spatial + frequency coding)—Forearm (contralateral to control muscles)

EMG allows for predictive control (improved feedforward control) and improved precision of myoelectric command and force control