Body machine interfaces for neuromotor rehabilitation: A case study

Pierella, Camilla; Abdollahi, Farnaz; Farshchiansadegh, Ali; Pedersen, Jessica Presperin; Chen, David; Mussa-Ivaldi, Ferdinando A.; Casadio, Maura

doi:10.1109/embc.2014.6943612

Cited by 12 publications

(11 citation statements)

References 10 publications

(10 reference statements)

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“…In this study, the BoMI's sensors are four wireless and low cost inertial measurement units (IMUs) (Yei Technology, 3-Space Sensor™ Wireless) placed on a garment attached by Velcro™ strips to the shoulders and upper arms [24]. The sensors were positioned as follows: sensor 1 on left arm, sensor 2 on left shoulder, sensor 3 on right shoulder and sensor 4 on right arm (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Remapping residual coordination for controlling assistive devices and recovering motor functions

et al. 2015

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The concept of human motor redundancy attracted much attention since the early studies of motor control, as it highlights the ability of the motor system to generate a great variety of movements to achieve any single well-defined goal. The abundance of degrees of freedom in the human body may be a fundamental resource in the learning and remapping problems that are encountered in human–machine interfaces (HMIs) developments. The HMI can act at different levels decoding brain signals or body signals to control an external device. The transformation from neural signals to device commands is the core of research on brain-machine interfaces (BMIs). However, while BMIs bypass completely the final path of the motor system, body-machine interfaces (BoMIs) take advantage of motor skills that are still available to the user and have the potential to enhance these skills through their consistent use. BoMIs empower people with severe motor disabilities with the possibility to control external devices, and they concurrently offer the opportunity to focus on achieving rehabilitative goals. In this study we describe a theoretical paradigm for the use of a BoMI in rehabilitation. The proposed BoMI remaps the user’s residual upper body mobility to the two coordinates of a cursor on a computer screen. This mapping is obtained by principal component analysis (PCA). We hypothesize that the BoMI can be specifically programmed to engage the users in functional exercises aimed at partial recovery of motor skills, while simultaneously controlling the cursor and carrying out functional tasks, e.g. playing games. Specifically, PCA allows us to select not only the subspace that is most comfortable for the user to act upon, but also the degrees of freedom and coordination patterns that the user has more difficulty engaging. In this article, we describe a family of map modifications that can be made to change the motor behavior of the user. Depending on the characteristics of the impairment of each high-level spinal cord injury (SCI) survivor, we can make modifications to restore a higher level of symmetric mobility (left versus right), or to increase the strength and range of motion of the upper body that was spared by the injury. Results showed that this approach restored symmetry between left and right side of the body, with an increase of mobility and strength of all the degrees of freedom in the participants involved in the control of the interface. This is a proof of concept that our BoMI may be used concurrently to control assistive devices and reach specific rehabilitative goals. Engaging the users in functional and entertaining tasks while practicing the interface and changing the map in the proposed ways is a novel approach to rehabilitation treatments facilitated by portable and low-cost technologies.

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Section: Methodsmentioning

confidence: 99%

Remapping residual coordination for controlling assistive devices and recovering motor functions

et al. 2015

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“…1a) (see also 15, 16 ). Specifically sensor 1 was on the left arm, sensor 2 on the left shoulder, sensor 3 on the right shoulder and sensor 4 on the right arm.…”

Section: Methodsmentioning

confidence: 93%

Learning new movements after paralysis: Results from a home-based study

Pierella

Abdollahi

Thorp

et al. 2017

Sci Rep

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Body-machine interfaces (BMIs) decode upper-body motion for operating devices, such as computers and wheelchairs. We developed a low-cost portable BMI for survivors of cervical spinal cord injury and investigated it as a means to support personalized assistance and therapy within the home environment. Depending on the specific impairment of each participant, we modified the interface gains to restore a higher level of upper body mobility. The use of the BMI over one month led to increased range of motion and force at the shoulders in chronic survivors. Concurrently, subjects learned to reorganize their body motions as they practiced the control of a computer cursor to perform different tasks and games. The BMI allowed subjects to generate any movement of the cursor with different motions of their body. Through practice subjects demonstrated a tendency to increase the similarity between the body motions used to control the cursor in distinct tasks. Nevertheless, by the end of learning, some significant and persistent differences appeared to persist. This suggests the ability of the central nervous system to concurrently learn operating the BMI while exploiting the possibility to adapt the available mobility to the specific spatio-temporal requirements of each task.

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“…The BMI is also and perhaps more importantly a means to keep the body engaged in performing coordinated motor control tasks. Unlike the brain-machine interface and joystick controllers, the body machine interface can also be programmed to promote physical exercise and to challenge the users to engage parts of the body that lie on the boundary of the paralysis or that tend to be underused [47]. This benefit is critically important to promote recovery and prevent comorbidities in severe paralyses and can be obtained by programming the body-machine map and/or by placing the IMU sensors so as to target specific degrees of freedom of the user's body [48].…”

Section: Discussionmentioning

confidence: 99%

Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia

Thorp

Abdollahi

Chen

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user’s residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional controls commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control the power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control.

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Body machine interfaces for neuromotor rehabilitation: A case study

Cited by 12 publications

References 10 publications

Remapping residual coordination for controlling assistive devices and recovering motor functions

Remapping residual coordination for controlling assistive devices and recovering motor functions

Learning new movements after paralysis: Results from a home-based study

Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia

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