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Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...

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Related Experiment Video

Updated: Jun 22, 2026

Rod-based Fabrication of Customizable Soft Robotic Pneumatic Gripper Devices for Delicate Tissue Manipulation
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Neuromorphic Sensor Based on Force-Sensing Resistors.

Alexandru Barleanu1, Mircea Hulea1

  • 1Department of Computer Engineering, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania.

Biomimetics (Basel, Switzerland)
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a novel neuromorphic sensor (NS) using force-sensing resistors (FSR) and spiking neurons for robotics. The FSR-based sensor offers high sensitivity to low forces and significantly lower power consumption compared to traditional load cells.

Keywords:
anthropomorphic finger controlforce-sensing resistorsneuromorphic sensorsshape memory alloyspiking neurons

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Area of Science:

  • Robotics
  • Neuro-inspired Engineering
  • Sensor Technology

Background:

  • Traditional sensors in robotics often have high power demands.
  • Developing low-power, sensitive sensors is crucial for advanced robotic systems.

Purpose of the Study:

  • To introduce and evaluate a novel neuromorphic sensor (NS) integrating force-sensing resistors (FSR) with spiking neurons.
  • To assess the sensor's performance in robotic control applications and compare it with conventional sensors.

Main Methods:

  • Integration of FSRs into a spiking neuron circuit to create a force-dependent spiking frequency.
  • Evaluation of the NS in controlling a SMA-actuated robotic finger during a force-monitoring task.
  • Comparative analysis against a standard compression load cell (CLC).

Main Results:

  • The FSR-based NS demonstrates high sensitivity to low forces with a continuous, wide-ranging spiking rate function.
  • The NS exhibits a logarithmic-like force-response curve, outperforming the CLC in small force sensitivity.
  • The NS consumes only 128 µW, which is 270 times less power than the CLC's 3.5 mW.

Conclusions:

  • The proposed FSR-based neuromorphic sensor is a viable, low-power, and cost-effective alternative for robotic applications.
  • Its characteristics are well-suited for bio-inspired control in humanoid robotics.
  • This technology advances the development of efficient and sensitive robotic sensing systems.