Controlling a robotic arm for functional tasks using a wireless head-joystick: A case study of a child with congenital absence of upper and lower limbs

Aspelund, Sanders; Patel, Priya; Lee, Mei-Hua; Kagerer, Florian A.; Ranganathan, Rajiv; Mukherjee, Ranjan

doi:10.1371/journal.pone.0226052

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…However, there are other interfaces that can potentially be used for ULE control yet not explorer in scientific articles for this application. For example, shoulder and head movements can be mapped to control commands for assistive devices such as robotic manipulators (Aspelund et al, 2020) and electric wheelchairs (Thorp et al, 2016). Another study used a sequence matching algorithm to detect user inputs for controlling an assistive manipulator with a sipand-puff input device (Schweitzer and Campeau-Lecours, 2020).…”

Section: Discussionmentioning

confidence: 99%

Tongue control of a five-DOF upper-limb exoskeleton rehabilitates drinking and eating for individuals with severe disabilities

Mohammadi

Knoche²,

Thøgersen³

et al. 2023

International Journal of Human-Computer Studies

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Section: Discussionmentioning

confidence: 99%

Tongue control of a five-DOF upper-limb exoskeleton rehabilitates drinking and eating for individuals with severe disabilities

Mohammadi

Knoche²,

Thøgersen³

et al. 2023

International Journal of Human-Computer Studies

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“…Interfaces between human and a machine are at the forefront of research in human augmentation [e.g., supernumerary limbs (Prattichizzo et al, 2014;Parietti and Asada, 2016;Yamen Saraiji et al, 2018), myoelectric prostheses (Antuvan et al, 2014;Wright et al, 2016;Dyson et al, 2018)], assistance [e.g., braincomputer interfaces (Santhanam et al, 2006;Millán et al, 2010;Nicolas-Alonso and Gomez-Gil, 2012;Jarosiewicz et al, 2015), brain-machine interfaces (Collinger et al, 2013), body-machine interfaces (Antuvan et al, 2014;Farshchiansadegh et al, 2014;Chau et al, 2017;Fall et al, 2017;Aspelund et al, 2020;Rizzoglio et al, 2020)], and rehabilitation (Rohm et al, 2013;Pierella et al, 2014;Donati et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

Santis

2021

Front. Neurorobot.

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The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive “tool” whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.

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“…The exploitation of redundancy also requires a reorganization, or remapping, of the residual ability to control body motions. When subjects use movements of the eye [ 18 , 19 ], head [ 20 , 21 ], shoulders [ 22 , 23 , 24 ], or tongue [ 25 , 26 ] for driving a wheelchair or piloting a robotic arm, they associate controlling these parts of the body with functions that before the injury were performed by other parts.…”

Section: Introductionmentioning

confidence: 99%

Recovery of Distal Arm Movements in Spinal Cord Injured Patients with a Body-Machine Interface: A Proof-of-Concept Study

Pierella

Galofaro

Luca

et al. 2021

Sensors

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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 has still been limited to shoulder movements. Here, we expanded the use of BoMI to promote the whole arm’s mobility, with a special focus on elbow movements. We also developed an instrumented evaluation test and a set of kinematic indicators for assessing residual abilities and recovery. Methods: Five inpatient cSCI subjects (four acute, one chronic) participated in a BoMI treatment complementary to their standard rehabilitative routine. The subjects wore a BoMI with sensors placed on both proximal and distal arm districts and practiced for 5 weeks. The BoMI was programmed to promote symmetry between right and left arms use and the forearms’ mobility while playing games. To evaluate the effectiveness of the treatment, the subjects’ kinematics were recorded while performing an evaluation test that involved functional bilateral arms movements, before, at the end, and three months after training. Results: At the end of the training, all subjects learned to efficiently use the interface despite being compelled by it to engage their most impaired movements. The subjects completed the training with bilateral symmetry in body recruitment, already present at the end of the familiarization, and they increased the forearm activity. The instrumental evaluation confirmed this. The elbow motion’s angular amplitude improved for all subjects, and other kinematic parameters showed a trend towards the normality range. Conclusion: The outcomes are preliminary evidence supporting the efficacy of the proposed BoMI as a rehabilitation tool to be considered for clinical practice. It also suggests an instrumental evaluation protocol and a set of indicators to assess and evaluate motor impairment and recovery in cSCI.

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Controlling a robotic arm for functional tasks using a wireless head-joystick: A case study of a child with congenital absence of upper and lower limbs

Cited by 5 publications

References 33 publications

Tongue control of a five-DOF upper-limb exoskeleton rehabilitates drinking and eating for individuals with severe disabilities

Tongue control of a five-DOF upper-limb exoskeleton rehabilitates drinking and eating for individuals with severe disabilities

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

Recovery of Distal Arm Movements in Spinal Cord Injured Patients with a Body-Machine Interface: A Proof-of-Concept Study

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