Pattern recognition and direct control home use of a multi-articulating hand prosthesis

Simon, Ann M.; Turner, Kristi; Miller, Laura A.; Hargrove, Levi J.; Kuiken, Todd A.

doi:10.1109/icorr.2019.8779539

Cited by 29 publications

(35 citation statements)

References 20 publications

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“…A large number of pattern recognition (PR) approaches have been developed over the years aiming to increase the functionality and control of more dexterous prostheses [3][4][5] . Despite these efforts, PR-based prostheses have not managed to deliver their full potential in practical clinical applications 6,7 . One of the primary causes is that they are sensitive to the variability of sEMG, limiting the robustness and reliability in long-term practical applications 4,[8][9][10][11][12][13][14] .…”

Section: Background and Summarymentioning

confidence: 99%

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

et al. 2020

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a hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person's life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. this multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics.

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Section: Background and Summarymentioning

confidence: 99%

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

et al. 2020

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“…To study at-home prosthetic use, previous take-home systems have stored limited usage data, including the time the device was turned on (Graczyk et al, 2018;Simon et al, 2019), aggregated hand movement (Simon et al, 2019), how often FIGURE 6 | Transradial amputee performed supervised activities of daily living, of his own choice, at home using the portable take-home system. Images show the participant (A) turning faucet in the bathroom; (B) locking the dead-bolt on the front door; a bi-manual task not possible with his commercial prosthesis; (C) opening the mail box; and (D) retrieving water from the refrigerator.…”

Section: Failedmentioning

confidence: 99%

Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging With a Multi-Degree-of-Freedom Bionic Arm

et al. 2020

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This paper describes a portable, prosthetic control system and the first at-home use of a multi-degree-of-freedom, proportionally controlled bionic arm. The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional control. We describe (a) how the system trains motor control algorithms for use with an advanced bionic arm, and (b) the system's ability to record an unprecedented and comprehensive dataset of EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution description of daily use-essential information to determine clinical relevance and improve future research for advanced bionic arms.

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“…including limited number of pre-determined grips, temporal delay due to sequential inputs used to 32 select grips, fixed output force (e.g., from classifier algorithms), extensive training that lasts days to 33 This is a provisional file, not the final typeset article weeks, and non-intuitive methods of control (e.g., inertial measurement units (IMUs) on residual 34 limb or feet) ( or direct control algorithms have been studied at home (Pasquina et al, 2015;Simon et al, 2019). 40 However, a Kalman filter (Wu et al, 2006), modified with non-linear, ad-hoc adjustments (Jacob A.…”

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confidence: 99%

“…al., 2018; Hargrove et al, 2017; L. Resnik et al, 2017; L. J. Resnik, Acluche, Borgia, et al, 2018). 252However, these approaches only approximate actual prosthesis use and could be misinterpreted.253 Some pattern recognition studies have recorded kinematic output and use of predefined grasps 254(Kuiken et al, 2016;Simon et al, 2019). 255An important aspect of the portable system is the fast computation of position updates using a steady-256 state Kalman filter.…”

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confidence: 99%

Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging with a Multi-Degree-of-Freedom Bionic Arm

Brinton

Barcikowski

Davis

et al. 2020

Preprint

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14This paper describes a portable, prosthetic control system for at-home use of an advanced bionic arm. 15The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional 16 control. We describe (a) how the system trains motor control algorithms for use with an advanced 17 bionic arm, and (b) the system's ability to record an unprecedented and comprehensive dataset of 18 EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the 19 system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This 20 technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution 21 description of daily use-essential information to determine clinical relevance and improve future 22 research for advanced bionic arms. 23 24

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Pattern recognition and direct control home use of a multi-articulating hand prosthesis

Cited by 29 publications

References 20 publications

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging With a Multi-Degree-of-Freedom Bionic Arm

Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging with a Multi-Degree-of-Freedom Bionic Arm

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