Promoting Functional and Independent Sitting in Children With Cerebral Palsy Using the Robotic Trunk Support Trainer

Santamaria, Victor; Khan, Moiz; Luna, Tatiana; Kang, Jiyeon; Dutkowsky, Joseph P.; Gordon, Andrew M.; Agrawal, Sunil K.

doi:10.1109/tnsre.2020.3031580

Cited by 27 publications

(35 citation statements)

References 62 publications

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“…if F lag = False and y Calculate direction and amplitude differences between two frames: This study supports the hypothesis that TruST intervention with a dynamic-boundary-based BAANC could achieve more significant sitting workspace area improvement than that based on the fixed boundary. Seated postural tasks (such as the modified SEBT) require motor planning and a high level of control and coordination of the torso muscles to displace and rotate the upper body as far as possible in multiple directions while keeping the upper body balanced [3], [5].…”

Section: B Statistical Analysis and Resultsmentioning

confidence: 99%

A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and its Application in Trunk Rehabilitation

Ai¹,

Santamaria²,

Chen³

et al. 2023

Preprint

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<p>Seated postural limit defines the region from beyond which a subject cannot return the trunk to the neutral position without additional external support. The seated postural limits can be used as a reference to provide assistive support to the torso by an appropriately designed robotics, e.g., Trunk Support Trainer (TruST). However, fixed boundary representations of seated postural limits in the literature cannot capture the dynamically changing seated postural limits during training. In this study, we propose a conceptual model of the dynamic boundary of the trunk center using a boundary vector designed to track the postural-goal direction and trunk movement amplitude during a sitting task. We experimented with 20 healthy subjects. The results support our hypothesis that TruST intervention with an assist-as-needed controller based on the proposed dynamic boundary representation could achieve more significant sitting workspace area improvements than a fixed boundary representation. The second contribution of this paper is that we provide an approach to effectively introduce deep learning into TruST's real-time controller design. We compiled a 3D trunk movement dataset which is currently the largest in the literature. We designed a loss capable of solving the gate-controlled regression problem. We proposed a novel roadmap for the exploration study. Following the roadmap, we developed a deep learning architecture, modified the widely used Inception module, and then obtained a deep learning model capable of accurately predicting the dynamic boundary in real-time. We believe this approach can be extended across other rehabilitation robots towards designing intelligent dynamic boundary-based assist-as-needed controllers.</p>

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Section: B Statistical Analysis and Resultsmentioning

confidence: 99%

A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and its Application in Trunk Rehabilitation

Ai¹,

Santamaria²,

Chen³

et al. 2023

Preprint

Self Cite

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“…The advent and advancement of robotics have enabled the development of tasks that humans cannot perform, or at least not with such precision [66][67][68]. In the field of eHealth, robotics has played a fundamental role in helping and treating people with various neurological conditions such as trauma [69], dementia [33][34][35][36][37], ASD [24][25][26][27][28], cerebral palsy [29][30][31][32] and ADHD [18][19][20][21][22][23]. Although in recent years the application of robotics in the treatment and support of ADHD has received considerable research attention, however, it is far inferior compared to dementia, autism, and cerebral palsy.…”

Section: Evolution Of Robotic Application In Adhd Treatmentmentioning

confidence: 99%

“…Several NDD treatments have significantly harnessed the potential of robotics. The use of robots in the treatment of autism spectrum disorder (ASD) [24][25][26][27][28], cerebral palsy [29][30][31][32] and dementia [33][34][35][36][37] has produced good results and established the basis for applying their developments in the treatment of ADHD. However, its application in the field of ADHD has been much less, representing 1.1% -1.8% compared to other NND.…”

Section: Introductionmentioning

confidence: 99%

Robotic Technologies in ADHD Care: Literature Review

et al. 2022

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Robotics has made it possible to change and improve many support processes for vulnerable people in different settings. In recent years, its use has been oriented toward supporting therapeutic interventions of neurodevelopmental disorders (NDD), including attention deficit hyperactivity disorder (ADHD). This review of the literature highlights how advances in robotics have evolved in different scenarios of ADHD treatment, its collaboration with other emerging technologies, its results, its limitations, and the research challenges for the future development of robotics in the field of supporting children with ADHD. The authors conducted a literature review based on the location of keywords 'robotics' and several NNDs such as 'ADHD', 'Autism Spectrum Disorder (ASD)', 'cerebral palsy', and 'dementia' in titles, abstracts, and introduction of scientific articles in the Scopus and Web of Science (WoS) database. The reviewed literature was classified according to the type of therapy supported by the robots, the type of robot and the associated technologies. From this analysis, we can solve the research question: Which types of robots have the potential for specific applications in ADHD treatment? Furthermore, this article shows that despite favorable technical results, robotic technologies that support ADHD therapies require significant improvements in terms of scalability, human-machine interaction, and treatment and processing of acquired information to be applied effectively in real-world therapies. The most significant research challenges are proposed to drive research efforts to develop new approaches to enable robotic assistants to participate in ADHD therapies.

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“…Khan et al [ 70 , 71 ] have developed the cable-driven trunk support trainer (TruST), which is helpful for the training of the seated posture for patients suffering from musculoskeletal and neurological disorders, where they have compromised postural stability. Based on a patient’s maximum trunk excursion, TruST control algorithms create a circular planar force tunnel around the trunk and provides as-needed assistance forces to the torso while performing the intended movements [ 72 ].…”

Section: Advancement In Spinal Rehabilitation Orthosesmentioning

confidence: 99%

Spinal Deformities and Advancement in Corrective Orthoses

et al. 2020

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Spinal deformity is an abnormality in the spinal curves and can seriously affect the activities of daily life. The conventional way to treat spinal deformities, such as scoliosis, kyphosis, and spondylolisthesis, is to use spinal orthoses (braces). Braces have been used for centuries to apply corrective forces to the spine to treat spinal deformities or to stabilize the spine during postoperative rehabilitation. Braces have not modernized with advancements in technology, and very few braces are equipped with smart sensory design and active actuation. There is a need to enable the orthotists, ergonomics practitioners, and developers to incorporate new technologies into the passive field of bracing. This article presents a review of the conventional passive braces and highlights the advancements in spinal orthoses in terms of improved sensory designs, active actuation mechanisms, and new construction methods (CAD/CAM, three-dimensional (3D) printing). This review includes 26 spinal orthoses, comprised of passive rigid/soft braces, active dynamics braces, and torso training devices for the rehabilitation of the spine.

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Promoting Functional and Independent Sitting in Children With Cerebral Palsy Using the Robotic Trunk Support Trainer

Cited by 27 publications

References 62 publications

A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and its Application in Trunk Rehabilitation

A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and its Application in Trunk Rehabilitation

Robotic Technologies in ADHD Care: Literature Review

Spinal Deformities and Advancement in Corrective Orthoses

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