EMG signal analysis to assess prosthetic hand failure during activities
EMG signal analysis to assess prosthetic hand failure during activities
Introduction: myoelectric upper-limb prosthetic retention is low [1] due to prosthetic hand failure caused by EMG electrode artifacts [2]. Observational studies using a range of specific activities have previously assessed prosthetic hand failure [3]. However, there lacks real-time signal processing studies to help underpin the mechanism and assessment of failure in hand activities. This preliminary study synchronises clinical (Ottobock) and research tools (Delsys) to analyse prosthetic hand failure risk.
Methods: a female, able-bodied participant was recruited. The VariPlus Speed prosthetic hand (Ottobock) is mounted to the participant, using a custom-designed 3D-printed cuff, shown in Figure 1. The TRIGNO system (Delsys), was attached proximally to the forearm. Two activities were studied: lifting a cup and placing a cup on a plinth. EMG signals were processed and separated into intended activation and idle windows. These were analysed based on signal proximity to the activation threshold.
Results & Discussion: figure 2 shows typical EMG signals for placing a cup on a plinth. Figure 3 shows percentage of readings crossing the activation threshold during different phases of the movement. Whilst it was observed that 60% of the recording sessions resulted in prosthetic failure, all sessions showed high percentages (~20%) of EMG data near the threshold of movement throughout the whole session. Most activities showed comparable percentages of EMG data crossing the threshold during periods of no intended activation (~9%) to periods of intended activation (~12%), signifying risk of prosthetic failure. Notably, placing the cup showed higher chance of prosthetic failure (~14%) than simply lifting (~10%), suggesting the lateral movement influences electrode artefacts.
Conclusion: by using observational data alone, it may be challenging to fully understand to risk of prosthetic failure for a given action. Signal analysis helps to determine when prosthetic failure is more likely to occur and during which activities. This is made possible by the unique combination of engineering and clinical tools.
Shaw, H.O.
b98622aa-8c92-4912-9bf7-78f88d30bed6
Devin, K.M.
17564d09-f686-4686-a39f-65ce0f4d160b
Tang, J.
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Jiang, L.
374f2414-51f0-418f-a316-e7db0d6dc4d1
14 September 2023
Shaw, H.O.
b98622aa-8c92-4912-9bf7-78f88d30bed6
Devin, K.M.
17564d09-f686-4686-a39f-65ce0f4d160b
Tang, J.
b4b9a22c-fd6d-427a-9ab1-51184c1d2a2c
Jiang, L.
374f2414-51f0-418f-a316-e7db0d6dc4d1
Shaw, H.O., Devin, K.M., Tang, J. and Jiang, L.
(2023)
EMG signal analysis to assess prosthetic hand failure during activities.
BioMedEng23, Swansea University, Swansea, United Kingdom.
14 - 15 Sep 2023.
1 pp
.
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Conference or Workshop Item
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Abstract
Introduction: myoelectric upper-limb prosthetic retention is low [1] due to prosthetic hand failure caused by EMG electrode artifacts [2]. Observational studies using a range of specific activities have previously assessed prosthetic hand failure [3]. However, there lacks real-time signal processing studies to help underpin the mechanism and assessment of failure in hand activities. This preliminary study synchronises clinical (Ottobock) and research tools (Delsys) to analyse prosthetic hand failure risk.
Methods: a female, able-bodied participant was recruited. The VariPlus Speed prosthetic hand (Ottobock) is mounted to the participant, using a custom-designed 3D-printed cuff, shown in Figure 1. The TRIGNO system (Delsys), was attached proximally to the forearm. Two activities were studied: lifting a cup and placing a cup on a plinth. EMG signals were processed and separated into intended activation and idle windows. These were analysed based on signal proximity to the activation threshold.
Results & Discussion: figure 2 shows typical EMG signals for placing a cup on a plinth. Figure 3 shows percentage of readings crossing the activation threshold during different phases of the movement. Whilst it was observed that 60% of the recording sessions resulted in prosthetic failure, all sessions showed high percentages (~20%) of EMG data near the threshold of movement throughout the whole session. Most activities showed comparable percentages of EMG data crossing the threshold during periods of no intended activation (~9%) to periods of intended activation (~12%), signifying risk of prosthetic failure. Notably, placing the cup showed higher chance of prosthetic failure (~14%) than simply lifting (~10%), suggesting the lateral movement influences electrode artefacts.
Conclusion: by using observational data alone, it may be challenging to fully understand to risk of prosthetic failure for a given action. Signal analysis helps to determine when prosthetic failure is more likely to occur and during which activities. This is made possible by the unique combination of engineering and clinical tools.
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EMG Signal Analysis to Assess Prosthetic Hand Failure During Activities
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Accepted/In Press date: September 2023
Published date: 14 September 2023
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Acknowledgments: this work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/S02249X/1 for the Centre for Doctoral Training in Prosthetics and Orthotics.
Venue - Dates:
BioMedEng23, Swansea University, Swansea, United Kingdom, 2023-09-14 - 2023-09-15
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Local EPrints ID: 483986
URI: http://eprints.soton.ac.uk/id/eprint/483986
PURE UUID: 10f41b05-839b-4058-b2b7-78a9111b0a40
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Date deposited: 08 Nov 2023 18:16
Last modified: 18 Mar 2024 03:54
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Author:
H.O. Shaw
Author:
K.M. Devin
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