A wearable ring-shaped inertial system to identify action planning impairments during reach-to-grasp sequences: a pilot study.

Action planning Dysexecutive syndrome Executive functions Mild cognitive impairment Motor programming Reach-to-grasp

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 07 2021
Historique:
received: 25 11 2020
accepted: 16 07 2021
entrez: 28 7 2021
pubmed: 29 7 2021
medline: 26 11 2021
Statut: epublish

Résumé

The progressive ageing of the population is leading to an increasing number of people affected by cognitive decline, including disorders in executive functions (EFs), such as action planning. Current procedures to evaluate cognitive decline are based on neuropsychological tests, but novel methods and approaches start to be investigated. Reach-to-grasp (RG) protocols have shown that intentions can influence the EFs of action planning. In this work, we proposed a novel ring-shaped wearable inertial device, SensRing, to measure kinematic parameters during RG and after-grasp (AG) tasks with different end-goals. The aim is to evaluate whether SensRing can characterize the motor performances of people affected by Mild Neurocognitive Disorder (MND) with impairment in EFs. Eight Individuals with dysexecutive MND, named d-MND, were compared to ten older healthy subjects (HC). They were asked to reach and grasp a can with three different intentions: to drink (DRINK), to place it on a target (PLACE), or to pass it to a partner (PASS). Twenty-one kinematic parameters were extracted from SensRing inertial data. Seven parameters resulted able to differentiate between HC and d-MND in the RG phase, and 8 features resulted significant in the AG phase. d-MND, indeed, had longer reaction times (in RG PLACE), slower peak velocities (in RG PLACE and PASS, in AG DRINK and PLACE), longer deceleration phases (in all RG and AG DRINK), and higher variability (in RG PLACE, in AG DRINK and PASS). Furthermore, d-MND showed no significant differences among conditions, suggesting that impairments in EFs influence their capabilities in modulating the action planning based on the end-goal. Based on this explorative study, the system might have the potential for objectifying the clinical assessment of people affected by d-MND by administering an easy motor test. Although these preliminary results have to be investigated in-depth in a larger sample, the portability, wearability, accuracy, and ease-of use of the system make the SensRing potentially appliable for remote applications at home, including analysis of protocols for neuromotor rehabilitation in patients affected by MND.

Sections du résumé

BACKGROUND
The progressive ageing of the population is leading to an increasing number of people affected by cognitive decline, including disorders in executive functions (EFs), such as action planning. Current procedures to evaluate cognitive decline are based on neuropsychological tests, but novel methods and approaches start to be investigated. Reach-to-grasp (RG) protocols have shown that intentions can influence the EFs of action planning. In this work, we proposed a novel ring-shaped wearable inertial device, SensRing, to measure kinematic parameters during RG and after-grasp (AG) tasks with different end-goals. The aim is to evaluate whether SensRing can characterize the motor performances of people affected by Mild Neurocognitive Disorder (MND) with impairment in EFs.
METHODS
Eight Individuals with dysexecutive MND, named d-MND, were compared to ten older healthy subjects (HC). They were asked to reach and grasp a can with three different intentions: to drink (DRINK), to place it on a target (PLACE), or to pass it to a partner (PASS). Twenty-one kinematic parameters were extracted from SensRing inertial data.
RESULTS
Seven parameters resulted able to differentiate between HC and d-MND in the RG phase, and 8 features resulted significant in the AG phase. d-MND, indeed, had longer reaction times (in RG PLACE), slower peak velocities (in RG PLACE and PASS, in AG DRINK and PLACE), longer deceleration phases (in all RG and AG DRINK), and higher variability (in RG PLACE, in AG DRINK and PASS). Furthermore, d-MND showed no significant differences among conditions, suggesting that impairments in EFs influence their capabilities in modulating the action planning based on the end-goal.
CONCLUSIONS
Based on this explorative study, the system might have the potential for objectifying the clinical assessment of people affected by d-MND by administering an easy motor test. Although these preliminary results have to be investigated in-depth in a larger sample, the portability, wearability, accuracy, and ease-of use of the system make the SensRing potentially appliable for remote applications at home, including analysis of protocols for neuromotor rehabilitation in patients affected by MND.

Identifiants

pubmed: 34315497
doi: 10.1186/s12984-021-00913-4
pii: 10.1186/s12984-021-00913-4
pmc: PMC8314592
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

118

Informations de copyright

© 2021. The Author(s).

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Auteurs

Erika Rovini (E)

Department of Industrial Engineering, University of Florence, Florence, Italy.

Guenda Galperti (G)

The BioRobotics Institute of Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy.
Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.

Valeria Manera (V)

CoBTeK Lab of the Université Cote D'Azur, Nice, France.

Gianmaria Mancioppi (G)

The BioRobotics Institute of Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy.
Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.

Laura Fiorini (L)

Department of Industrial Engineering, University of Florence, Florence, Italy.

Auriane Gros (A)

CoBTeK Lab of the Université Cote D'Azur, Nice, France.

Philippe Robert (P)

CoBTeK Lab of the Université Cote D'Azur, Nice, France.

Filippo Cavallo (F)

Department of Industrial Engineering, University of Florence, Florence, Italy. filippo.cavallo@unifi.it.
The BioRobotics Institute of Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy. filippo.cavallo@unifi.it.
Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy. filippo.cavallo@unifi.it.

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