A unified scheme for the benchmarking of upper limb functions in neurological disorders.

Benchmark Exoskeletons Functional evaluation Neurological disorders Performance evaluation Rehabilitation robotics Stroke Testing Upper limb

Journal

Journal of neuroengineering and rehabilitation
ISSN: 1743-0003
Titre abrégé: J Neuroeng Rehabil
Pays: England
ID NLM: 101232233

Informations de publication

Date de publication:
27 09 2022
Historique:
received: 13 04 2022
accepted: 08 09 2022
entrez: 27 9 2022
pubmed: 28 9 2022
medline: 30 9 2022
Statut: epublish

Résumé

In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary. This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols. We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles. This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

Sections du résumé

BACKGROUND
In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary.
METHODS
This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols.
RESULTS
We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles.
CONCLUSIONS
This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

Identifiants

pubmed: 36167552
doi: 10.1186/s12984-022-01082-8
pii: 10.1186/s12984-022-01082-8
pmc: PMC9513990
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

102

Informations de copyright

© 2022. The Author(s).

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Auteurs

Valeria Longatelli (V)

Neuroengineering and Medical Robotics Laboratory and WE-COBOT Laboratory, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy. valeria.longatelli@polimi.it.

Diego Torricelli (D)

Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Madrid, Spain.

Jesús Tornero (J)

Advanced Neurorehabilitation Unit, Hospital Los Madroños, Madrid, Spain.

Alessandra Pedrocchi (A)

Neuroengineering and Medical Robotics Laboratory and WE-COBOT Laboratory, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.

Franco Molteni (F)

Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy.

José L Pons (JL)

Shirley Ryan AbilityLab, Chicago, USA.

Marta Gandolla (M)

WE-COBOT Laboratory, Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.

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