Evidence map›Paper›PMID 36080915›Full record

ArticleSensors (Basel, Switzerland)2022

VibroTouch: Active Tactile Sensor for Contact Detection and Force Sensing via Vibrations.

Danissa Sandykbayeva, Zhanat Kappassov, Bakhtiyar Orazbayev

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Danissa SandykbayevaRobotics Department, Nazarbayev University, Nur-Sultan 010000, Kazakhstan.ORCID 0000-0003-1037-5642
Zhanat KappassovRobotics Department, Nazarbayev University, Nur-Sultan 010000, Kazakhstan.ORCID 0000-0003-3262-3993
Bakhtiyar OrazbayevPhysics Department, Nazarbayev University, Nur-Sultan 010000, Kazakhstan.ORCID 0000-0002-3597-1873

Funding

Ministry of Education and Science of the Republic of Kazakhstan AP09058050Nazarbayev University 11022021FD2901Nazarbayev University 11022021FD2923
6 · The paper itself

Abstract

Accurate and fast contact detection between a robot manipulator and objects is crucial for safe robot-object and human-robot interactions. Traditional collision detection techniques relied on force-torque sensors and Columb friction cone estimation. However, the strain gauges used in the conventional force sensors require low-noise and high-precision electronics to deliver the signal to the final user. The Signal-to-Noise Ratio (SNR) in these devices is still an issue in light contact detection. On the other hand, the Eccentric Rotating Mass (ERM) motors are very sensitive to subtle touch as their vibrating resonant state loses immediately. The vibration, in this case, plays a core role in triggering the tactile event. This project's primary goal is to use generated and received vibrations to establish the scope of object properties that can be obtained through low-frequency generation on one end and Fourier analysis of the accelerometer data on the other end. The main idea behind the system is the phenomenon of change in vibration propagation patterns depending on the grip properties. Moreover, the project's original aim is to gather enough information on vibration feedback on objects of various properties and compare them. These data sets are further analyzed in terms of frequency and applied grip force correlations in order to prepare the ground for pattern extraction and recognition based on the physical properties of an object.

Indexed as

TouchTouch PerceptionFrictionHand StrengthHumansVibrationactive vibration sensingcontact detectiontactile sensing

Identifiers

PMID36080915
PMCPMC9460586

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.