Uneven Terrain Recognition Using Neuromorphic Haptic Feedback

Sahana Prasanna, Jessica D’Abbraccio, Mariangela Filosa, Davide Ferraro, Ilaria Cesini, Giacomo Spigler, Andrea Aliperta, Filippo Dell’Agnello, Angelo Davalli, Emanuele Gruppioni, Simona Crea, Nicola Vitiello, Alberto Mazzoni, Calogero Maria Oddo

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)

    Abstract

    Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot–ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%,
    solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor
    conditions while walking, adapt their gait and promote a more confident use of their artificial limb.
    Original languageEnglish
    Article number4521
    Pages (from-to)1-17
    Number of pages17
    JournalSensors
    Volume23
    Issue number9
    DOIs
    Publication statusPublished - 6 May 2023

    Keywords

    • Neuromorphic haptic feedback
    • FPGA neuron model
    • Izhikevich
    • Terrain recognition
    • PSTH-based classification

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