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Research ArticleResearch Article: Methods/New Tools, Sensory and Motor Systems

Open-Source Instrumented Object to Study Dexterous Object Manipulation

David Córdova Bulens, Sophie du Bois de Dunilac, Benoit P. Delhaye, Philippe Lefèvre and Stephen J. Redmond
eNeuro 14 December 2023, 11 (1) ENEURO.0211-23.2023; https://doi.org/10.1523/ENEURO.0211-23.2023
David Córdova Bulens
1Biomedical Sensors & Signals Group, School of Electrical and Electronic Engineering, University College Dublin, D04V1W8, Dublin, Republic of Ireland
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  • For correspondence: david.cordovabulens@ucd.ie davidcordovabulens@gmail.com
Sophie du Bois de Dunilac
1Biomedical Sensors & Signals Group, School of Electrical and Electronic Engineering, University College Dublin, D04V1W8, Dublin, Republic of Ireland
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Benoit P. Delhaye
2Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, 1348, Louvain-la-Neuve, Belgium
3Institute of Neuroscience (IoNS), Université catholique de Louvain, 1200, Brussels, Belgium
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Philippe Lefèvre
2Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, 1348, Louvain-la-Neuve, Belgium
3Institute of Neuroscience (IoNS), Université catholique de Louvain, 1200, Brussels, Belgium
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Stephen J. Redmond
1Biomedical Sensors & Signals Group, School of Electrical and Electronic Engineering, University College Dublin, D04V1W8, Dublin, Republic of Ireland
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  • Figure 1.
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    Figure 1.

    Methods. A, Photograph of the object showing the glass plate, connected to the force sensor, through which the finger is going to be imaged. B, Side view of the instrumented object. The light source, half-mirror, viewing window, and camera represent the optical system allowing for the finger in contact with the viewing window to be imaged. The force sensor measures the 3D forces and torques. The LEDs are used to verify the synchronization of the force signals and the videos. C, Imaging principle of the instrument object. A light source (light-blue rectangle) illuminates the finger–glass contact. The image of the finger is reflected into the camera by a half-mirror. D, Image of the finger captured by InOb. E, Triangle shape evolution from frame t to frame t + 1 and corresponding representation of compression (red triangle) and dilation (blue triangle). F, Schematic of the forces acting on the object as well as the movement frequency and amplitude. G, Schematic view of the contact area with the division in three parts for analysis of the vertical strains of the finger (ϵyy ). The green and orange regions represent the ulnar and radial parts of the finger, respectively.

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    Figure 2.

    Forces, acceleration (Acc), SR, peeling, and strain rate for a single trial of an exemplar participant. A, GF applied (blue line) and LF experienced (black line) by the index finger of the participant during the trial. B, Vertical acceleration of the instrumented object measured by the IMU. C, Ratio of the GF relative to the LF (black line) and friction limit (orange dashed line) which is estimated at the end of each experimental trial. D, SR of the skin, i.e., the region of the contact area where the skin is stuck to the plate as a proportion of the total contact area. E, Peeling and laying, and compression and dilation of the finger, i.e., amount of skin coming into and losing contact with the glass plate. F, Horizontal (ϵxx) , vertical (ϵyy ), and shear (ϵxy ) strain rates on the finger surface for a single oscillation. The filled-in dots represent the time stamps of the oscillation marked by vertical gray lines in panels A–E.

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    Figure 3.

    Mean GF and LF evolution, peeling amplitude, minimum slip ratio, and strain amplitude for each oscillation, aggregated across all trials. A, GF (blue line) and LF (black line) evolution within and across the 15 trials in the experiment aggregated across all participants. The shaded areas represent the standard deviation. B, Mean (black line) and SD (gray area) peeling amplitude across participants for each oscillation and each trial. C, Mean (black line) and SD (gray area) of the minimum slip ratio of each oscillation across participants. D, Mean (solid line) and SD (faded area) of the vertical strain modulation for the radial part of the finger (orange line) and the ulnar part of the finger (green line) of each oscillation across participants.

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    Figure 4.

    A, Participants’ mean GF and mean SF (black dots). The error bars represent the standard deviation. The dashed black line represents the SF limit, below which slip will occur. B, Participants’ mean GF and mean strain amplitude for each oscillation for the ulnar (green dots) and radial (orange dots) parts of the finger.

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    Figure 5.

    Mean difference in GF change amplitude for oscillations labeled as “slip” (light-blue dots) and “no slip” (red dots) for each participant.

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    Figure 6.

    A, Mean amplitude of ϵulnar (green line) and ϵradial (orange line) for all participants. B, Modulation of the heatmap of ϵradial and ϵulnar and mean cross-covariance across trials between GF and ϵradial and ϵulnar , for each participant. The cross-covariance was computed using a time window of 2.25 s. The yellow color highlights a positive covariance and the blue color highlights a negative covariance.

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    Figure 7.

    Heatmap of the mean cross-covariance between GF and the strains for each participant. The cross-covariance was computed using a time window of 2.25 s. The yellow color highlights a positive covariance, and the blue color highlights a negative covariance.

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    Figure 8.

    Heatmap of the mean time-windowed cross-covariance between GF and LF across all trials for each participant. The yellow color highlights a positive covariance and the blue color highlights a negative covariance.

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    Figure 9.

    A, Covariance between the strains in the radial part of the finger and GF. B, Covariance between the strains in the ulnar part of the finger and GF. The pink line represents the mean and standard deviation of group 1, i.e., the group with the largest average max covariance, and the light green line represents the mean and standard deviation of group 2.

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    Table 1.

    Mean ± SD of the maximum covariance between ϵradial and GF, and ϵulnar and GF across all trials for all participants

    ParticipantMax covarianceLag at max covariance (ms)
    P10.23 ± 0.117.5 ± 391
    0.25 ± 0.11−12.8 ± 482
    P20.77 ± 0.3017.9 ± 281
    0.77 ± 0.3017 ± 470
    P30.68 ± 0.29−17.3 ± 371
    0.50 ± 0.25−52.8 ± 524.5
    P40.29 ± 0.1415 ± 521
    0.30 ± 0.1419 ± 615
    P50.24 ± 0.11−68 ± 549
    0.29 ± 0.13−17.8 ± 615.9
    P60.46 ± 0.2214 ± 473
    0.46 ± 0.221.1 ± 521.6
    P70.19 ± 0.10−5.1 ± 579
    0.26 ± 0.136.6 ± 619
    P80.18 ± 0.09−9.8 ± 620
    0.24 ± 0.11−10.4 ± 683
    P90.34 ± 0.16−22.3 ± 495.1
    0.32 ± 0.1411.1 ± 480.2
    P100.18 ± 0.09−23.9 ± 629.6
    0.20 ± 0.10−6.9 ± 483.3
    Global0.23 ± 0.108−4.4 ± 501
    0.257 ± 0.11−5.8 ± 544.6
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Open-Source Instrumented Object to Study Dexterous Object Manipulation
David Córdova Bulens, Sophie du Bois de Dunilac, Benoit P. Delhaye, Philippe Lefèvre, Stephen J. Redmond
eNeuro 14 December 2023, 11 (1) ENEURO.0211-23.2023; DOI: 10.1523/ENEURO.0211-23.2023

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Open-Source Instrumented Object to Study Dexterous Object Manipulation
David Córdova Bulens, Sophie du Bois de Dunilac, Benoit P. Delhaye, Philippe Lefèvre, Stephen J. Redmond
eNeuro 14 December 2023, 11 (1) ENEURO.0211-23.2023; DOI: 10.1523/ENEURO.0211-23.2023
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