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

Abstract

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.

Getting a Grip on the Impact of Incidental Feedback From Body-Powered and Myoelectric Prostheses

“…Additionally, it is important to evaluate feedback in contexts that will be relevant to prosthesis users. Many studies have highlighted the importance of stiffness and force identification in manipulation tasks [21], [23]- [25], as these skills address the concerns of an individual crushing or dropping an object with their prosthesis. Pose matching tasks are also relevant, though less common [16], [20], as they quantify a prosthesis users' ability to match their hand aperture to the size of the object to initiate a grasp.…”

“…Pose matching tasks are also relevant, though less common [16], [20], as they quantify a prosthesis users' ability to match their hand aperture to the size of the object to initiate a grasp. While force feedback is typically delivered continuously [21], [23]- [25], hand aperture can be delivered either continuously [16], [17], [20], [21] or discretely [9], [26], with discrete methods typically denoting the contact events for grasping or releasing an object. Together, the context of a task and the type of prosthesis an individual is using may affect the utility of the feedback being delivered, or how available a particular feedback modality is to the user at all.…”

The Effects of Torque Magnitude and Stiffness in Arm Guidance Through Joint Torque Feedback

“…For example, Carignan et al utilized a multi-joint system [10] of JTF to display a virtual wall during a wall-painting task. Thomas et al adopted both JTF and vibrotactile feedback to convey information about the stiffness of objects [11]. Since JTF uses the same sensory organs as used during volitional motions, it is useful to discuss the neural pathways involved in motion and sensory feedback.…”

Co-adaptive myoelectric control for upper limb prostheses

“…On the one side, non-invasive methods, as vibrotactors (Figure 2.3c), are easily introduced into the prosthesis (Guémann et al, 2018;Thomas et al, 2019). On the other side, invasive electrodes directly innervating the nerves might simulate human sensory feedback to a much higher degree.…”

“…Since the beginning, it is necessary to define which kind of information we want the machine to understand and learn. The models are divided into two approaches: classification (Castellini and Van der Smagt, 2009;Krasoulis et al, 2019b;Rahimi et al, 2016;Spanias et al, 2015) and regression (Ameri et al, 2014c;Fang et al, 2017;Guémann et al, 2018;Hochberg et al, 2006;Krasoulis and Nazarpour, 2020;Krasoulis et al, 2019a;Markovic et al, 2018b;Thomas et al, 2019). The difference relies on the system's output: a label to tag the input data in a class (classification) or a continuous mapping of the output (regression).…”