Relevant bibliographies by topics / Body-machine interface (BoMI)

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Academic literature on the topic 'Body-machine interface (BoMI)'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Body-machine interface (BoMI)"

1

Miehlbradt, Jenifer, Alexandre Cherpillod, Stefano Mintchev, et al. "Data-driven body–machine interface for the accurate control of drones." Proceedings of the National Academy of Sciences 115, no. 31 (2018): 7913–18. http://dx.doi.org/10.1073/pnas.1718648115.

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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
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2

Abdollahi, Farnaz, Ali Farshchiansadegh, Camilla Pierella, et al. "Body-Machine Interface Enables People With Cervical Spinal Cord Injury to Control Devices With Available Body Movements: Proof of Concept." Neurorehabilitation and Neural Repair 31, no. 5 (2017): 487–93. http://dx.doi.org/10.1177/1545968317693111.

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This study tested the use of a customized body-machine interface (BoMI) for enhancing functional capabilities in persons with cervical spinal cord injury (cSCI). The interface allows people with cSCI to operate external devices by reorganizing their residual movements. This was a proof-of-concept phase 0 interventional nonrandomized clinical trial. Eight cSCI participants wore a custom-made garment with motion sensors placed on the shoulders. Signals derived from the sensors controlled a computer cursor. A standard algorithm extracted the combinations of sensor signals that best captured each
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3

Pierella, Camilla, Elisa Galofaro, Alice De Luca, et al. "Recovery of Distal Arm Movements in Spinal Cord Injured Patients with a Body-Machine Interface: A Proof-of-Concept Study." Sensors 21, no. 6 (2021): 2243. http://dx.doi.org/10.3390/s21062243.

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Background: The recovery of upper limb mobility and functions is essential for people with cervical spinal cord injuries (cSCI) to maximize independence in daily activities and ensure a successful return to normality. The rehabilitative path should include a thorough neuromotor evaluation and personalized treatments aimed at recovering motor functions. Body-machine interfaces (BoMI) have been proven to be capable of harnessing residual joint motions to control objects like computer cursors and virtual or physical wheelchairs and to promote motor recovery. However, their therapeutic application
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4

Freccero, Aurora, Maddalena Feder, Giorgio Grioli, et al. "A Body–Machine Interface for Assistive Robot Control in Spinal Cord Injury: System Description and Preliminary Tests." Applied Sciences 15, no. 4 (2025): 1792. https://doi.org/10.3390/app15041792.

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Motor impairments, particularly spinal cord injuries, impact thousands of people each year, resulting in severe sensory and motor disabilities. Assistive technologies play a crucial role in supporting these individuals with activities of daily living. Among such technologies, body–machine interfaces (BoMIs) are particularly important, as they convert residual body movements into control signals for external robotic devices. The main challenge lies in developing versatile control interfaces that can adapt to the unique needs of individual users. This study aims to adapt for people with spinal c
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5

Rizzoglio, Fabio, Camilla Pierella, Santis Dalia De, Ferdinando A. Mussa-Ivaldi, and Maura Casadio. "A hybrid body-machine interface integrating signals from muscles and motions." Journal of Neural Engineering, June 10, 2020. https://doi.org/10.1088/1741-2552/ab9b6c.

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Body-Machine Interfaces (BoMIs) establish a way to operate a variety of devices, allowing their users to extend the limits of their motor abilities by exploiting the redundancy of muscles and motions that remain available after spinal cord injury or stroke. Here, we considered the integration of two types of signals, motion signals derived from inertial measurement units (IMUs) and muscle activities recorded with electromyography (EMG), both contributing to the operation of the BoMI. A direct combination of IMU and EMG signals might result in inefficient control due to the differences in their
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6

Lee, Jongmin M., Temesgen Gebrekristos, Dalia De Santis, et al. "Learning to Control Complex Robots Using High-Dimensional Body-Machine Interfaces." ACM Transactions on Human-Robot Interaction, January 16, 2024. http://dx.doi.org/10.1145/3630264.

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When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement assistance and to promote physical rehabilitation, learning to use such interfaces to control complex machines is not well understood. In a five session study, we demonstrate that a subset of an uninjured population is able to learn and improve their ability to use a high-dimensional Body-Machine Interface (BoMI), to control a robotic arm. We use a se
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7

De, Santis Dalia. "A Framework for Optimizing Co-adaptation in Body-Machine Interfaces." Frontiers in Neurorobotics 15, no. 662181 (2021). https://doi.org/10.3389/fnbot.2021.662181.

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Interfaces between human and a machine are at the forefront of research in human augmentation, assistance and rehabilitation.Initial acceptance of the interface relies on its intuitiveness, while continued use over a long period of time requires reliability.Co-adaptive interfaces have gained great popularity as a viable way to compensate for various sources of instability and to facilitate human operation. Nevertheless, correct functioning of the interface relies on user adaptation, a process that can be lengthy and cognitively demanding, and not enough effort has been devoted to address the m
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8

Bellitto, Amy, Ferdinando A. Mussa-Ivaldi, Camilla Pierella, and Maura Casadio. "Synergic Practice with a Body-Machine Interface: Implications for Individual and Collective Motor Learning." Journal of Neural Engineering, July 11, 2025. https://doi.org/10.1088/1741-2552/adeec9.

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Abstract Objective.
Body-Machine Interfaces (BoMIs) translate human body movements into commands for controlling external devices, such as computer cursors. This process allows researchers to study the development and refinement of inverse models, which generate motor commands necessary for achieving desired movements. Traditionally, motor learning has focused on solo practice, but recent research has shifted towards exploring dyadic tasks, where two individuals practice together. Within dyadic tasks, synergic practice - where partners collaborate towards a shared goal - has shown prom
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9

Zhou, Jian, Xinxin Long, Jian Huang, et al. "Multiscale and hierarchical wrinkle enhanced graphene/Ecoflex sensors integrated with human-machine interfaces and cloud-platform." npj Flexible Electronics 6, no. 1 (2022). http://dx.doi.org/10.1038/s41528-022-00189-1.

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AbstractCurrent state-of-the-art stretchable/flexible sensors have received stringent demands on sensitivity, flexibility, linearity, and wide-range measurement capability. Herein, we report a methodology of strain sensors based on graphene/Ecoflex composites by modulating multiscale/hierarchical wrinkles on flexible substrates. The sensor shows an ultra-high sensitivity with a gauge factor of 1078.1, a stretchability of 650%, a response time of ~140 ms, and a superior cycling durability. It can detect wide-range physiological signals including vigorous body motions, pulse monitoring and speec
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10

Brandt, Marisa Renee. "Cyborg Agency and Individual Trauma: What Ender's Game Teaches Us about Killing in the Age of Drone Warfare." M/C Journal 16, no. 6 (2013). http://dx.doi.org/10.5204/mcj.718.

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During the War on Terror, the United States military has been conducting an increasing number of foreign campaigns by remote control using drones—also called unmanned aerial vehicles (UAVs) or remotely piloted vehicles (RPVs)—to extend the reach of military power and augment the technical precision of targeted strikes while minimizing bodily risk to American combatants. Stationed on bases throughout the southwest, operators fly weaponized drones over the Middle East. Viewing the battle zone through a computer screen that presents them with imagery captured from a drone-mounted camera, these co
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