The road forward for upper-extremity rehabilitation robotics

Rose, Chad G.; Deshpande, Ashish D.; Carducci, Jacob; Brown, Jeremy D.

doi:10.1016/j.cobme.2021.100291

Cited by 14 publications

(10 citation statements)

References 60 publications

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“…Yet, our current understanding of sensory impairment and sensory recovery is limited. Robotic technologies are well-poised to help fill this knowledge gap by providing robust and reliable assessments of sensory function [ 161 ]. Michelle Johnson: toward automated assessment of human–human and human–robot interaction for neurorehabilitation There is an increased need for rehabilitation to occur outside traditional settings and in real-world environments.…”

Section: Appendix: Overview Of Key Insights Made In Presentations By ...mentioning

confidence: 99%

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NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

Valero-Cuevas,

Finley,

Orsborn

et al. 2024

J NeuroEngineering Rehabil

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We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation’s Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling ‘In-the-Wild’. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

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Section: Appendix: Overview Of Key Insights Made In Presentations By ...mentioning

confidence: 99%

Section: Appendix: Overview Of Key Insights Made In Presentations By ...mentioning

confidence: 99%

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

Valero-Cuevas,

Finley,

Orsborn

et al. 2024

J NeuroEngineering Rehabil

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“…The use of exoskeletons guarantees the fulfillment of all of these requirements, allowing the intensive training sessions with specific therapeutic purposes to be carried out while always adapting to the residual motor skills of the patients [ 7 ]. Nowadays, despite all of the advantages that we described, exoskeletons are poorly diffused in daily clinical practice [ 8 ]. Therapists tend to find them challenging to use and often do not think that robots can offer an actual improvement to the classical therapy that they perform every day.…”

Section: Introductionmentioning

confidence: 99%

Technology Acceptance Model for Exoskeletons for Rehabilitation of the Upper Limbs from Therapists’ Perspectives

Luciani

Braghin

Pedrocchi

et al. 2023

Sensors

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Over the last few years, exoskeletons have been demonstrated to be useful tools for supporting the execution of neuromotor rehabilitation sessions. However, they are still not very present in hospitals. Therapists tend to be wary of this type of technology, thus reducing its acceptability and, therefore, its everyday use in clinical practice. The work presented in this paper investigates a novel point of view that is different from that of patients, which is normally what is considered for similar analyses. Through the realization of a technology acceptance model, we investigate the factors that influence the acceptability level of exoskeletons for rehabilitation of the upper limbs from therapists’ perspectives. We analyzed the data collected from a pool of 55 physiotherapists and physiatrists through the distribution of a questionnaire. Pearson’s correlation and multiple linear regression were used for the analysis. The relations between the variables of interest were also investigated depending on participants’ age and experience with technology. The model built from these data demonstrated that the perceived usefulness of a robotic system, in terms of time and effort savings, was the first factor influencing therapists’ willingness to use it. Physiotherapists’ perception of the importance of interacting with an exoskeleton when carrying out an enhanced therapy session increased if survey participants already had experience with this type of rehabilitation technology, while their distrust and the consideration of others’ opinions decreased. The conclusions drawn from our analyses show that we need to invest in making this technology better known to the public—in terms of education and training—if we aim to make exoskeletons genuinely accepted and usable by therapists. In addition, integrating exoskeletons with multi-sensor feedback systems would help provide comprehensive information about the patients’ condition and progress. This can help overcome the gap that a robot creates between a therapist and the patient’s human body, reducing the fear that specialists have of this technology, and this can demonstrate exoskeletons’ utility, thus increasing their perceived level of usefulness.

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“…Robot-mediated medical services have been identified as a possible solution to the ageing population in developed countries in the last few decades. An older population implies a lower active workforce and an increase in age-related diseases, increasing strain on the healthcare sector [1]- [6]. Additionally, as highlighted from the COVID-19 pandemic, reduced access to healthcare facilities can currently compromise healthcare quality.…”

Section: Introductionmentioning

confidence: 99%

“…Rehabilitation and robot-aided surgery were among the first applications in medical robotics [1], [7]. The rehabilitation robots have shown how the introduction of these technologies in the rehabilitation centre has allowed an increase of bandwidth and therapeutic improvements in the patients.…”

Section: Introductionmentioning

confidence: 99%

Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics

Tiseo¹,

Rouxel²,

Asenov³

et al. 2022

Preprint

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Medical robotics can help improve and extend the reach of healthcare services. A major challenge for medical robots is the complex physical interaction between the robot and the patients which is required to be safe. This work presents the preliminary evaluation of a recently introduced control architecture based on the Fractal Impedance Control (FIC) in medical applications. The deployed FIC architecture is robust to delay between the master and the replica robots. It can switch online between an admittance and impedance behaviour, and it is robust to interaction with unstructured environments. Our experiments analyse three scenarios: teleoperated surgery, rehabilitation, and remote ultrasound scan. The experiments did not require any adjustment of the robot tuning, which is essential in medical applications where the operators do not have an engineering background required to tune the controller. Our results show that is possible to teleoperate the robot to cut using a scalpel, do an ultrasound scan, and perform remote occupational therapy. However, our experiments also highlighted the need for a better robots embodiment to precisely control the system in 3D dynamic tasks.

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The road forward for upper-extremity rehabilitation robotics

Cited by 14 publications

References 60 publications

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

Technology Acceptance Model for Exoskeletons for Rehabilitation of the Upper Limbs from Therapists’ Perspectives

Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics

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