Tactile Feedback in Closed-Loop Control of Myoelectric Hand Grasping: Conveying Information of Multiple Sensors Simultaneously via a Single Feedback Channel

Mayer, Raphael M.; Garcia-Rosas, Ricardo; Mohammadi, Alireza; Tan, Ying; Alıcı, Gürsel; Choong, Peter; Oetomo, Denny

doi:10.3389/fnins.2020.00348

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…The single abutment point in OI implants limits the available stimulation points for sensory feedback. However, Mayer et al demonstrated that supplementary tactile feedback can be transmitted simultaneously through a single feedback channel to achieve adequate performance in these tasks (Mayer et al, 2020). This addresses the challenges of conveying information through one channel by improving its efficiency.…”

Section: Sensory Feedbackmentioning

confidence: 99%

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

2021

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Over the last few decades there has been a push to enhance the use of advanced prosthetics within the fields of biomedical engineering, neuroscience, and surgery. Through the development of peripheral neural interfaces and invasive electrodes, an individual's own nervous system can be used to control a prosthesis. With novel improvements in neural recording and signal decoding, this intimate communication has paved the way for bidirectional and intuitive control of prostheses. While various collaborations between engineers and surgeons have led to considerable success with motor control and pain management, it has been significantly more challenging to restore sensation. Many of the existing peripheral neural interfaces have demonstrated success in one of these modalities; however, none are currently able to fully restore limb function. Though this is in part due to the complexity of the human somatosensory system and stability of bioelectronics, the fragmentary and as-yet uncoordinated nature of the neuroprosthetic industry further complicates this advancement. In this review, we provide a comprehensive overview of the current field of neuroprosthetics and explore potential strategies to address its unique challenges. These include exploration of electrodes, surgical techniques, control methods, and prosthetic technology. Additionally, we propose a new approach to optimizing prosthetic limb function and facilitating clinical application by capitalizing on available resources. It is incumbent upon academia and industry to encourage collaboration and utilization of different peripheral neural interfaces in combination with each other to create versatile limbs that not only improve function but quality of life. Despite the rapidly evolving technology, if the field continues to work in divided “silos,” we will delay achieving the critical, valuable outcome: creating a prosthetic limb that is right for the patient and positively affects their life.

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Section: Sensory Feedbackmentioning

confidence: 99%

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

2021

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“…The introduction of direct feedback modalities can prevent amputees to rely exclusively on sight (Biddiss et al, 2007 ; Pylatiuk et al, 2007 ), reducing the mental effort and, therefore, facilitating the communication between user intention and prosthesis action (Markovic et al, 2018 ; Valle et al, 2018 ; Clemente et al, 2019 ). In fact, it has been demonstrated that the introduction of haptic feedback improves the control of the prosthesis (Mayer et al, 2020 ; Sensinger and Dosen, 2020 ; Yildiz et al, 2020 ; Chai et al, 2022 ) due to its fundamental role during human–objects interactions (Hsiao et al, 2011 ; Valle et al, 2018 ; Pena et al, 2019 ; Di Pino et al, 2020 ; Shehata et al, 2020 ; Raspopovic et al, 2021 ), allowing subjects to embody the device (Antfolk et al, 2013 ; Svensson et al, 2017 ; Raspopovic et al, 2021 ), hence, improving the compliance among the user, the prosthesis, and the grasped objects (Osborn et al, 2016 ). In the literature, this interaction is mainly assessed by providing grasp force or proprioceptive information (Stephens-Fripp et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

et al. 2023

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IntroductionIn recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object – in this case a prosthetic device – into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction.MethodsBased on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees.ResultsThe classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).

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“…The spatial parameters define the capabilities of the interface to perceive stimulation on multiple sites on the physiologically given bony landmarks on the elbow when stimulation was applied one-at-a-time. This is of interest in prosthetic grasping as combination of different types of feedback information are required (Westling and Johansson, 1984 ; Johansson and Westling, 1987 ; Augurelle et al, 2003 ; Mayer et al, 2020b ). The temporal and spatial characteristics of the bone conduction has been conducted on both able-bodied subjects and subjects with trans-radial amputation and compared to each other and to the invasive bone conduction method.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spatial characteristics of bone conduction as non-invasive haptic sensory feedback for upper-limb prosthesis

et al. 2023

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Bone conduction is a promising haptic feedback modality for upper-limb prosthesis users, however, its potential and characteristics as a non-invasive feedback modality have not been thoroughly investigated. This study aimed to establish the temporal and spatial characteristics of non-invasive bone conduction as a sensory feedback interface for upper-limb prostheses. Psychometric human-subject experiments were conducted on three bony landmarks of the elbow, with a vibrotactile transducer affixed to each to provide the stimulus. The study characterized the temporal domain by testing perception threshold and resolution in amplitude and frequency. The spatial domain was evaluated by assessing the ability of subjects to detect the number of simultaneous active stimulation sites. The experiment was conducted with ten able-bodied subjects and compared to two subjects with trans-radial amputation. The psychometric evaluation of the proposed non-invasive bone conduction feedback showed results comparable to invasive methods. The experimental results demonstrated similar amplitude and frequency resolution of the interface for all three stimulation sites for both able-bodied subjects and subjects with trans-radial amputation, highlighting its potential as a non-invasive feedback modality for upper-limb prostheses.

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Tactile Feedback in Closed-Loop Control of Myoelectric Hand Grasping: Conveying Information of Multiple Sensors Simultaneously via a Single Feedback Channel

Cited by 14 publications

References 34 publications

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

Temporal and spatial characteristics of bone conduction as non-invasive haptic sensory feedback for upper-limb prosthesis

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