ArticleSensors (Basel, Switzerland)2017
Inferring Interaction Force from Visual Information without Using Physical Force Sensors.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- A Two-Stage Grip Force Estimation Method for Surgical Robots Based on BiLSTM-iTransformer.Annals of the New York Academy of Sciences · 2026Article
- A Transparent Teleoperated Robotic Surgical System with Predictive Haptic Feedback and Force Modelling.Sensors (Basel, Switzerland) · 2022Article
- A Force-Feedback Methodology for Teleoperated Suturing Task in Robotic-Assisted Minimally Invasive Surgery.Sensors (Basel, Switzerland) · 2022Article
- Cross-Modal Reconstruction for Tactile Signal in Human-Robot Interaction.Sensors (Basel, Switzerland) · 2022Article
- Vision-Based Suture Tensile Force Estimation in Robotic Surgery.Sensors (Basel, Switzerland) · 2020Article
- Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks.Sensors (Basel, Switzerland) · 2020Article
- Future-Frame Prediction for Fast-Moving Objects with Motion Blur.Sensors (Basel, Switzerland) · 2020Article
- Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics.Sensors (Basel, Switzerland) · 2020Article
- A Clamping Force Estimation Method Based on a Joint Torque Disturbance Observer Using PSO-BPNN for Cable-Driven Surgical Robot End-Effectors.Sensors (Basel, Switzerland) · 2019Article
- Transparent and Flexible Mayan-Pyramid-based Pressure Sensor using Facile-Transferred Indium tin Oxide for Bimodal Sensor Applications.Scientific reports · 2019Article
- An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video.Sensors (Basel, Switzerland) · 2019Article
- Context-Aware Winter Sports Based on Multivariate Sequence Learning.Sensors (Basel, Switzerland) · 2019Article
- Design and Evaluation of FBG-Based Tension Sensor in Laparoscope Surgical Robots.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.