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Related Concept Videos

Somatosensation01:33

Somatosensation

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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Related Experiment Video

Updated: Aug 4, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Affect Recognition in Hand-Object Interaction Using Object-Sensed Tactile and Kinematic Data.

Radoslaw Niewiadomski, Cigdem Beyan, Alessandra Sciutti

    IEEE Transactions on Haptics
    |April 4, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Object-sensed data can recognize human emotions and action styles. Tactile data alone is as effective as combined tactile and kinematics data for emotion recognition, achieving 82.7% accuracy.

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    Area of Science:

    • Human-Computer Interaction
    • Affective Computing
    • Robotics

    Background:

    • Understanding human affective states during object interaction is crucial for intuitive human-robot collaboration.
    • Basic actions like grasping and rotating are fundamental to daily-life interactions and can convey emotional information.

    Purpose of the Study:

    • To investigate the recognition of human affective states (emotions and vitality forms) by processing object-sensed data.
    • To evaluate the effectiveness of tactile and kinematics data from an object for emotion and action style classification.

    Main Methods:

    • Utilized the iCube, a 5 cm cube, to collect tactile maps and rotation data during basic actions.
    • Conducted two studies: emotion classification (anger, sadness, excitement, gratitude) and vitality form classification (gentle vs. rude actions).
    • Trained machine learning models using hand-crafted features and evaluated classifier performance.

    Main Results:

    • Emotion recognition achieved up to 82.7% accuracy. Notably, tactile data alone performed comparably to models using all 10 features.
    • Vitality form classification differentiated gentle from rude actions with 84.85% accuracy.
    • Confirmed that affective states and attitudes influence how individuals interact with objects.

    Conclusions:

    • Object-sensed tactile and kinematics data can effectively infer human affective states and action styles.
    • Tactile sensing alone shows significant potential for emotion recognition in human-object interactions.
    • This research provides a foundation for developing more perceptive and responsive interactive systems.