ArticleProceedings of the National Academy of Sciences of the United States of America2018
Data-driven body-machine interface for the accurate control of drones.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Motor equivalence in motor awareness.iScience · 2026Article
- Learning to Control Complex Robots Using High-Dimensional Body-Machine Interfaces.ACM transactions on human-robot interaction · 2024Article
- A Non-Linear Body Machine Interface for Controlling Assistive Robotic Arms.IEEE transactions on bio-medical engineering · 2023Article
- Young children can use their subjective straight-ahead to remap visuo-motor alterations.Scientific reports · 2023Article
- Extensible Neck: A Gesture Input Method to Extend/Contract Neck Virtually in Video See-through AR Environment.Sensors (Basel, Switzerland) · 2022Article
- Immersive virtual reality interferes with default head-trunk coordination strategies in young children.Scientific reports · 2021Article
- Recovery of Distal Arm Movements in Spinal Cord Injured Patients with a Body-Machine Interface: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2021Article
- Article
- The dynamics of motor learning through the formation of internal models.PLoS computational biology · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate teleoperation of robotic devices requires simple, yet intuitive and reliable control interfaces. However, current human-machine interfaces (HMIs) often fail to fulfill these characteristics, leading to systems requiring an intensive practice to reach a sufficient operation expertise. Here, we present a systematic methodology to identify the spontaneous gesture-based interaction strategies of naive individuals with a distant device, and to exploit this information to develop a data-driven body-machine interface (BoMI) to efficiently control this device. We applied this approach to the specific case of drone steering and derived a simple control method relying on upper-body motion. The identified BoMI allowed participants with no prior experience to rapidly master the control of both simulated and real drones, outperforming joystick users, and comparing with the control ability reached by participants using the bird-like flight simulator Birdly.
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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.