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Tactile Perception in Upper Limb Prostheses: Mechanical Characterization, Human Experiments, and Computational Findings

Alessia Silvia Ivani, Manuel G. Catalano, Giorgio Grioli, Matteo Bianchi, Yon Visell, Antonio Bicchi

TL;DR

This paper examines vibrotactile perception through four socket-based prosthetic hands with varying rigidity and articulation, combining perceptual, transmission, and recognition experiments. Using a single participant, the study shows that simple rigid hands transmit stronger high-frequency vibrations and support finger discrimination, while advanced soft hands dampen cues yet still enable above-chance performance. Through IMU-based socket sensing and a sensorized pendulum, the authors quantify vibration transmission and demonstrate a strong link between transmitted energy and perceptual accuracy ($\rho \approx 0.8$). LSTM models trained on socket-accelerometer data achieve higher finger-discrimination accuracy than human performance, suggesting that ML can compensate for reduced natural transmission in advanced hands and informing socket-embedded haptic feedback designs with practical impact for prosthetic users.

Abstract

Our research investigates vibrotactile perception in four prosthetic hands with distinct kinematics and mechanical characteristics. We found that rigid and simple socket-based prosthetic devices can transmit tactile information and surprisingly enable users to identify the stimulated finger with high reliability. This ability decreases with more advanced prosthetic hands with additional articulations and softer mechanics. We conducted experiments to understand the underlying mechanisms. We assessed a prosthetic user's ability to discriminate finger contacts based on vibrations transmitted through the four prosthetic hands. We also performed numerical and mechanical vibration tests on the prostheses and used a machine learning classifier to identify the contacted finger. Our results show that simpler and rigid prosthetic hands facilitate contact discrimination (for instance, a user of a purely cosmetic hand can distinguish a contact on the index finger from other fingers with 83% accuracy), but all tested hands, including soft advanced ones, performed above chance level. Despite advanced hands reducing vibration transmission, a machine learning algorithm still exceeded human performance in discriminating finger contacts. These findings suggest the potential for enhancing vibrotactile feedback in advanced prosthetic hands and lay the groundwork for future integration of such feedback in prosthetic devices.

Tactile Perception in Upper Limb Prostheses: Mechanical Characterization, Human Experiments, and Computational Findings

TL;DR

This paper examines vibrotactile perception through four socket-based prosthetic hands with varying rigidity and articulation, combining perceptual, transmission, and recognition experiments. Using a single participant, the study shows that simple rigid hands transmit stronger high-frequency vibrations and support finger discrimination, while advanced soft hands dampen cues yet still enable above-chance performance. Through IMU-based socket sensing and a sensorized pendulum, the authors quantify vibration transmission and demonstrate a strong link between transmitted energy and perceptual accuracy (). LSTM models trained on socket-accelerometer data achieve higher finger-discrimination accuracy than human performance, suggesting that ML can compensate for reduced natural transmission in advanced hands and informing socket-embedded haptic feedback designs with practical impact for prosthetic users.

Abstract

Our research investigates vibrotactile perception in four prosthetic hands with distinct kinematics and mechanical characteristics. We found that rigid and simple socket-based prosthetic devices can transmit tactile information and surprisingly enable users to identify the stimulated finger with high reliability. This ability decreases with more advanced prosthetic hands with additional articulations and softer mechanics. We conducted experiments to understand the underlying mechanisms. We assessed a prosthetic user's ability to discriminate finger contacts based on vibrations transmitted through the four prosthetic hands. We also performed numerical and mechanical vibration tests on the prostheses and used a machine learning classifier to identify the contacted finger. Our results show that simpler and rigid prosthetic hands facilitate contact discrimination (for instance, a user of a purely cosmetic hand can distinguish a contact on the index finger from other fingers with 83% accuracy), but all tested hands, including soft advanced ones, performed above chance level. Despite advanced hands reducing vibration transmission, a machine learning algorithm still exceeded human performance in discriminating finger contacts. These findings suggest the potential for enhancing vibrotactile feedback in advanced prosthetic hands and lay the groundwork for future integration of such feedback in prosthetic devices.
Paper Structure (28 sections, 4 equations, 10 figures, 5 tables)

This paper contains 28 sections, 4 equations, 10 figures, 5 tables.

Table of Contents

  1. INTRODUCTION
  2. Materials: Prosthetic Hands
  3. Perliminary Analysis
  4. Tactile Feedback Perception Experiment
  5. Participant
  6. Experimental Setup
  7. Experimental Methods
  8. Data Analysis
  9. Tactile Feedback Transmission Experiment
  10. Experimental Setup
  11. Experimental Method
  12. Data Analysis
  13. Tactile Feedback Recognition Experiment
  14. Datasets
  15. LSTM Network Architecture and Hyperparameters
  16. ...and 13 more sections

Figures (10)

  • Figure 1: The prosthetic hands adopted in this study: (from left to right) a Cosmetic hand (CH) by Ottobock, a MyoHand VariPlus Speed (VP) by Ottobock, the I-Limb Access (IL) by Össur and a SoftHand Pro (SH). Hands reference planes are shown.
  • Figure 2: Impact response analysis of a prosthetic-shaped system modelled as a continuous plastic body to an impulse (with a force of 2N) on each fingertip. Acceleration propagation is shown. Grey circles highlight the differences in acceleration propagation depending on the finger contacted. The prosthetic system has a constraint on the end surface of the socket. Simulations are performed on Creo Parametric.
  • Figure 3: Tactile Feedback Perception Experiment setup: the prosthetic limb (here with VP hand) is kept in place by a supporting structure made of beams and two cables with Velcro.
  • Figure 4: Tactile Feedback Transmission Experiment setup: the pendulum comprises a load cell on the lower extremity, an encoder on the upper extremity, and a custom 3D-printed end-stop to select angles. The Bionic hands (here, the SoftHand Pro) are set with the supporting structure.
  • Figure 5: The reference socket is assembled with the I-Limb hand with the IMUs attached inside in radial distribution. A detailed view shows the inner part of the socket and the number of each IMU.
  • ...and 5 more figures