Development and Real World Use of a Vibratory Haptic Feedback System for Upper-Limb Prosthetic Users

Restoration of sensory information via bionic hands

Individuals who have lost the use of their hands because of amputation or spinal cord injury can use prosthetic hands to restore their independence. A dexterous prosthesis requires the acquisition of control signals that drive the movements of the robotic hand, and the transmission of sensory signals to convey information to the user about the consequences of these movements. In this Review, we describe non-invasive and invasive technologies for conveying artificial sensory feedback through bionic hands, and evaluate the technologies' long-term prospects.

Haptic shared control improves neural efficiency during myoelectric prosthesis use

Clinical myoelectric prostheses lack the sensory feedback and sufficient dexterity required to complete activities of daily living efficiently and accurately. Providing haptic feedback of relevant environmental cues to the user or imbuing the prosthesis with autonomous control authority have been separately shown to improve prosthesis utility. Few studies, however, have investigated the effect of combining these two approaches in a shared control paradigm, and none have evaluated such an approach from the perspective of neural efficiency (the relationship between task performance and mental effort measured directly from the brain). In this work, we analyzed the neural efficiency of 30 non-amputee participants in a grasp-and-lift task of a brittle object. Here, a myoelectric prosthesis featuring vibrotactile feedback of grip force and autonomous control of grasping was compared with a standard myoelectric prosthesis with and without vibrotactile feedback. As a measure of mental effort, we captured the prefrontal cortex activity changes using functional near infrared spectroscopy during the experiment. It was expected that the prosthesis with haptic shared control would improve both task performance and mental effort compared to the standard prosthesis. Results showed that only the haptic shared control system enabled users to achieve high neural efficiency, and that vibrotactile feedback was important for grasping with the appropriate grip force. These results indicate that the haptic shared control system synergistically combines the benefits of haptic feedback and autonomous controllers, and is well-poised to inform such hybrid advancements in myoelectric prosthesis technology.

Correlating Vibration Patterns to Perception of Tactile Information for Long-Term Prosthetic Limb Use and Continued Rehabilitation of Neuropathic Pain

Prosthetic limbs (and orthotic devices) have been used as a paradigm for the treatment and rehabilitation of neuropathic pain, such as phantom limb pain. Long-term adoption of the devices for the continued use in rehabilitation remains low in part due to reduced embodiment and the high cognitive load associated with controlling the device. Previous research has shown that incorporating sensory feedback in prostheses can provide proprioceptive information, increase control and manipulation of objects, and improve embodiment. However, feedback experienced by the user varies daily and requires constant parameter adjustments to maintain accurate and intuitive sensory perception, further preventing long term adoption. Work therefore needs to be explored that correlate feedback modalities to perception of tactile information, such as texture and pressure. The work presented in this paper begins to explore this by utilizing a deep-learning algorithm to classify the dissipation of vibration artefacts found in the EMG signals of able-bodied individuals to specific texture patterns. Four texture patterns were applied to 7 participants using two vibration motors and repeated 3 times. In post processing, a RNN network identified the artefact features along equidistantly spaced EMG electrodes and correctly classified unseen data from each participant.

A Haptic Sleeve as a Method of Mechanotactile Feedback Restoration for Myoelectric Hand Prosthesis Users

Current myoelectric upper limb prostheses do not restore sensory feedback, impairing fine motor control. Mechanotactile feedback restoration with a haptic sleeve may rectify this problem. This randomised crossover within-participant controlled study aimed to assess a prototype haptic sleeve's effect on routine grasping tasks performed by eight able-bodied participants. Each participant completed 15 repetitions of the three tasks: Task 1—normal grasp, Task 2—strong grasp and Task 3—weak grasp, using visual, haptic, or combined feedback All data were collected in April 2021 in the Scottish Microelectronics Centre, Edinburgh, UK. Combined feedback correlated with significantly higher grasp success rates compared to the vision alone in Task 1 (p < 0.0001), Task 2 (p = 0.0057), and Task 3 (p = 0.0170). Similarly, haptic feedback was associated with significantly higher grasp success rates compared to vision in Task 1 (p < 0.0001) and Task 2 (p = 0.0015). Combined feedback correlated with significantly lower energy expenditure compared to visual feedback in Task 1 (p < 0.0001) and Task 3 (p = 0.0003). Likewise, haptic feedback was associated with significantly lower energy expenditure compared to the visual feedback in Task 1 (p < 0.0001), Task 2 (p < 0.0001), and Task 3 (p < 0.0001). These results suggest that mechanotactile feedback provided by the haptic sleeve effectively augments grasping and reduces its energy expenditure.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Sensory Feedback for Upper-Limb Prostheses: Opportunities and Barriers

The addition of sensory feedback to upper-limb prostheses has been shown to improve control, increase embodiment, and reduce phantom limb pain. However, most commercial prostheses do not incorporate sensory feedback due to several factors. This paper focuses on the major challenges of a lack of deep understanding of user needs, the unavailability of tailored, realistic outcome measures and the segregation between research on control and sensory feedback. The use of methods such as the Person-Based Approach and co-creation can improve the design and testing process. Stronger collaboration between researchers can integrate different prostheses research areas to accelerate the translation process.

Embedded Electrotactile Feedback System for Hand Prostheses Using Matrix Electrode and Electronic Skin

As the technology moves towards more human-like bionic limbs, it is necessary to develop a feedback system that provides active touch feedback to a user of a prosthetic hand. Most of the contemporary sensory substitution methods comprise simple position and force sensors combined with few discrete stimulation units, and hence they are characterized with a limited amount of information that can be transmitted by the feedback. The present study describes a novel system for tactile feedback integrating advanced multipoint sensing (electronic skin) and stimulation (matrix electrodes). The system comprises a flexible sensing array (16 sensors) integrated on the index finger of a Michelangelo prosthetic hand mockup, embedded interface electronics and multichannel stimulator connected to a flexible matrix electrode (24 pads). The developed system conveys contact information (binary detections) to the user. To demonstrate the feasibility, the system was tested in six able-bodied subjects who were asked to recognize static patterns (contact position) with two different spatial resolutions and dynamic movement patterns (i.e., sliding along and/or across the finger) presented on the electronic skin. The experiments demonstrated that the system successfully translated the mechanical interaction into electrotactile profiles, which the subjects could recognize with good performance. The success rates (mean ± standard deviation) for the static patterns were 91 ± 4% and 58 ± 10% for low and high spatial resolution, respectively, while the success rate for sliding touch was 94 ± 4%. These results demonstrate that the developed system is an important step towards a new generation of tactile feedback interfaces that can provide high-bandwidth connection between the user and his/her bionic limb. Such systems would allow mimicking spatially distributed natural feedback, thereby facilitating the control and embodiment of the artificial device into the user body scheme.

Grasping Embodiment: Haptic Feedback for Artificial Limbs

Upper-limb prostheses are subject to high rates of abandonment. Prosthesis abandonment is related to a reduced sense of embodiment, the sense of self-location, agency, and ownership that humans feel in relation to their bodies and body parts. If a prosthesis does not evoke a sense of embodiment, users are less likely to view them as useful and integrated with their bodies. Currently, visual feedback is the only option for most prosthesis users to account for their augmented activities. However, for activities of daily living, such as grasping actions, haptic feedback is critically important and may improve sense of embodiment. Therefore, we are investigating how converting natural haptic feedback from the prosthetic fingertips into vibrotactile feedback administered to another location on the body may allow participants to experience haptic feedback and if and how this experience affects embodiment. While we found no differences between our experimental manipulations of feedback type, we found evidence that embodiment was not negatively impacted when switching from natural feedback to proximal vibrotactile feedback. Proximal vibrotactile feedback should be further studied and considered when designing prostheses.

Closed-loop Control using Electrotactile Feedback Encoded in Frequency and Pulse Width

Sensory substitution by electrotactile stimulation has been widely investigated for improving the functionality of human-machine interfaces. Few studies, however, have objectively compared different ways in which such systems can be implemented. In this study, we compare encoding of a feedback variable in stimulation pulse width or stimulation frequency during a closed-loop control task. Specifically, participants were asked to track a predefined pseudorandom trajectory using a joystick with electrotactile feedback as the only indication of the tracking error. Each participant performed eight 90 s trials per encoding scheme. Tracking performance using frequency modulation enabled lower tracking error (RMSE: Frequency modulation: 0.27 ± 0.03; Pulse width modulation: 0.31 ± 0.05; p < 0.05) and a higher correlation with the target trajectory (Frequency modulation: 83.4 ± 4.1%; Pulse width modulation: 79.8 ± 5.2%; p < 0.05). There was no significant improvement in performance over the eight trials. Furthermore, frequency-domain analysis revealed that frequency modulation was characterized with a higher gain at lower error frequencies. In summary, the results indicate that encoding of feedback variables in the frequency of pulses enables better control than pulse width modulation in closed-loop dynamic tasks.

The Interaction Between Feedback Type and Learning in Routine Grasping With Myoelectric Prostheses

While prosthetic fitting after upper-limb loss allows for restoration of motor functions, it deprives the amputee of tactile sensations that are essential for grasp control in able-bodied subjects. Therefore, it is commonly assumed that restoring the force feedback would improve the control of prosthesis grasping force. However, the literature regarding the benefit of feedback is controversial. Here, we investigated how the type of feedback affects learning and steady-state performance of routine grasping with a prosthesis. The experimental task was to grasp an object using a prosthesis and generate a low or high target-force range (TFR), both initially unknown, in three feedback conditions: basic auditory feedback on task outcome, and additional visual or vibratory feedback on the force magnitude. The results demonstrated that the performance was rather good and stable for the low TFR, whereas it was substantially worse for the high TFR with a pronounced training effect. Surprisingly, learning curve and steady-state performance did not depend on the feedback condition. Hence, in the specific context of routing grasping with a prosthesis controlled via surface EMG, the basic feedback on task outcome was not outperformed by force-related end-of-trial feedback and hence seemed to be sufficient for accomplishing the task.This conclusion applies to the context of routine grasping using a myoelectric prosthesis with surface EMG electrodes, which means that the control signals are variable and the feedback is perceived and processed at the end of the trial (motor adaption).

Simulated robotic device malfunctions resembling malicious cyberattacks impact human perception of trust, satisfaction, and frustration

Robot assistants and wearable devices are highly useful; however, these artificial systems are susceptible to hackers. In this article, two sets of experiments were conducted. The first part of this study simulated a malicious attack on a prosthetic arm system to adversely affect the operation of the prosthetic system, while the perception of 10 human subjects was surveyed. These 10 able-bodied subjects controlled the prosthetic arm and hand with electromyogram signals, while an artificial sensation of touch was conveyed to their arms as they operated the system, which enabled them to feel what the prosthetic hand was grasping as they were asked to transport an object from one location to another. This haptic feedback was provided in both the normal and abnormal operational modes but was disabled in the extremely abnormal mode. The electromyogram control signals for the arm were reversed in both the abnormal and extremely abnormal modes. Results from the simulated malicious attack on a prosthetic arm system showed that the subjects found the haptic feedback helpful in both the normal and abnormal modes of operation. Both the abnormal and extremely abnormal modes of operation negatively impacted the self-reported levels of trust, satisfaction, and frustration with the prosthetic system as the subjects grasped and transported an object. While these metrics were negatively impacted by system malfunctions resembling a malicious attack on the control functionality, it was possible to rebuild them to their former higher levels after the functionality of the prosthetic system was restored. A parallel study in this article involved simulating a malicious attack on a robot assistant to unfavorably affect the delivery operation modes, while the perception of 20 human subjects was surveyed. Results showed that the simulated malfunctions unfavorably impacted the perception of trust, satisfaction, and frustration, but it was possible to restore these metrics in two different ways as the device functionality was restored.

A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control

Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator’s electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.

Comparison of vibrotactile and joint-torque feedback in a myoelectric upper-limb prosthesis

BACKGROUND

Despite the technological advancements in myoelectric prostheses, body-powered prostheses remain a popular choice for amputees, in part due to the natural sensory advantage they provide. Research on haptic feedback in myoelectric prostheses has delivered mixed results. Furthermore, there is limited research comparing various haptic feedback modalities in myoelectric prostheses. In this paper, we present a comparison of the feedback intrinsically present in body-powered prostheses (joint-torque feedback) to a commonly proposed feedback modality for myoelectric prostheses (vibrotactile feedback). In so doing, we seek to understand whether the advantages of kinesthetic feedback present in body-powered prostheses translate to myoelectric prostheses, and whether there are differences between kinesthetic and cutaneous feedback in prosthetic applications.

METHODS

We developed an experimental testbed that features a cable-driven, voluntary-closing 1-DoF prosthesis, a capstan-driven elbow exoskeleton, and a vibrotactile actuation unit. The system can present grip force to users as either a flexion moment about the elbow or vibration on the wrist. To provide an equal comparison of joint-torque and vibrotactile feedback, a stimulus intensity matching scheme was utilized. Non-amputee participants (n=12) were asked to discriminate objects of varying stiffness with the prosthesis in three conditions: no haptic feedback, vibrotactile feedback, and joint-torque feedback.

RESULTS

Results indicate that haptic feedback increased discrimination accuracy over no haptic feedback, but the difference between joint-torque feedback and vibrotactile feedback was not significant. In addition, our results highlight nuanced differences in performance depending on the objects' stiffness, and suggest that participants likely pay less attention to incidental cues with the addition of haptic feedback.

CONCLUSION

Even when haptic feedback is not modality matched to the task, such as in the case of vibrotactile feedback, performance with a myoelectric prosthesis can improve significantly. This implies it is possible to achieve the same benefits with vibrotactile feedback, which is cheaper and easier to implement than other forms of feedback.

Design and Integration of an Inexpensive Wearable Mechanotactile Feedback System for Myoelectric Prostheses

The aim of this paper was to demonstrate the functionality of an inexpensive mechanotactile sensory feedback system for transhumeral myoelectric prostheses. We summarize the development of a tactile-integrated prosthesis, including 1) evaluation of sensors that were retrofit onto existing commercial terminal devices; 2) design of two custom mechanotactile tactors that were integrated into a socket without compromising suction suspension; 3) design of a modular controller which translated sensor input to tactor output, was wirelessly adjusted, and fit within a prosthetic forearm; and 4) evaluation of the system with a single transhumeral participant. Prosthesis functionality was demonstrated over three test sessions; the participant was able to identify tactor stimulation location and demonstrated a reduction in grasp force with the mechanotactile stimulation. This system offers an inexpensive and modular solution for integration of a mechanotactile sensory feedback system into a prosthetic socket without compromising the suction seal. These principles can be applied in future studies to investigate the direct impact of sensory feedback on tangible outcomes for prosthetic users, thereby reducing barriers to clinical translation.