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A novel method for the quantification of key components of manual dexterity after stroke.

Térémetz M, Colle F, Hamdoun S, Maier MA, Lindberg PG - J Neuroeng Rehabil (2015)

Bottom Line: Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls.Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients.

View Article: PubMed Central - PubMed

Affiliation: FR3636 CNRS, Université Paris Descartes, Sorbonne Paris Cité, 75006, Paris, France. mteremetz@gmail.com.

ABSTRACT

Background: A high degree of manual dexterity is a central feature of the human upper limb. A rich interplay of sensory and motor components in the hand and fingers allows for independent control of fingers in terms of timing, kinematics and force. Stroke often leads to impaired hand function and decreased manual dexterity, limiting activities of daily living and impacting quality of life. Clinically, there is a lack of quantitative multi-dimensional measures of manual dexterity. We therefore developed the Finger Force Manipulandum (FFM), which allows quantification of key components of manual dexterity. The purpose of this study was (i) to test the feasibility of using the FFM to measure key components of manual dexterity in hemiparetic stroke patients, (ii) to compare differences in dexterity components between stroke patients and controls, and (iii) to describe individual profiles of dexterity components in stroke patients.

Methods: 10 stroke patients with mild-to-moderate hemiparesis and 10 healthy subjects were recruited. Clinical measures of hand function included the Action Research Arm Test and the Moberg Pick-Up Test. Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).

Results: Most stroke patients could perform the tracking task, as well as the single and multi-finger tapping tasks. However, only four patients performed the sequence task. Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls. Unwanted (erroneous) finger taps and overflow to non-tapping fingers were increased in patients. Dexterity components were not systematically related among each other, resulting in individually different profiles of deficient dexterity. Some of the FFM measures correlated with clinical scores.

Conclusions: Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients. The FFM can detect group differences and individual profiles of deficient dexterity. The FFM is a promising tool for the measurement of key components of manual dexterity after stroke and could allow improved targeting of motor rehabilitation.

No MeSH data available.


Related in: MedlinePlus

The Finger Force Manipulandum (FFM). Index, middle, ring and little finger each apply forces on a spring-loaded piston. Two types of tasks were implemented: continuous force tracking and finger tapping. Forces applied by each finger were recorded via a CED interface (not shown) and used for real-time visual feedback and for performance analysis
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Fig1: The Finger Force Manipulandum (FFM). Index, middle, ring and little finger each apply forces on a spring-loaded piston. Two types of tasks were implemented: continuous force tracking and finger tapping. Forces applied by each finger were recorded via a CED interface (not shown) and used for real-time visual feedback and for performance analysis

Mentions: Together with Sensix (www.sensix.fr) we developed the Finger Force Manipulandum (FFM) in order to quantify key components of manual dexterity in stroke (and other) patients. The FFM is equipped with four pistons positioned under the tip of the index, middle, ring and little finger, each coupled to an individual strain gauge force sensor (Fig. 1). The height of the pistons can be adjusted but in this study we used a constant piston height of 15 mm across all subjects. Pistons have a contact surface of 15 mm diameter and are 20 mm apart. With increasing force the pistons move against a spring load over a range of 10 mm. The end of this dynamic (non- static) range is reached with 1N. Above 1N, forces are controlled isometrically. Thus each sensor measures force along the piston axis exerted from each finger independently. The precision of the sensor is <0.01N, with a range of 0–9N. Force data of each finger was sampled to a CED 1401 (with 10 kHz sampling rate/digit) connected to a computer running Spike 2v6 (Cambridge Electronic Design, www.ced.co.uk) software. Custom-written CED scripts provided real-time visual display of digit forces and target instructions or target forces.Fig. 1


A novel method for the quantification of key components of manual dexterity after stroke.

Térémetz M, Colle F, Hamdoun S, Maier MA, Lindberg PG - J Neuroeng Rehabil (2015)

The Finger Force Manipulandum (FFM). Index, middle, ring and little finger each apply forces on a spring-loaded piston. Two types of tasks were implemented: continuous force tracking and finger tapping. Forces applied by each finger were recorded via a CED interface (not shown) and used for real-time visual feedback and for performance analysis
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4522286&req=5

Fig1: The Finger Force Manipulandum (FFM). Index, middle, ring and little finger each apply forces on a spring-loaded piston. Two types of tasks were implemented: continuous force tracking and finger tapping. Forces applied by each finger were recorded via a CED interface (not shown) and used for real-time visual feedback and for performance analysis
Mentions: Together with Sensix (www.sensix.fr) we developed the Finger Force Manipulandum (FFM) in order to quantify key components of manual dexterity in stroke (and other) patients. The FFM is equipped with four pistons positioned under the tip of the index, middle, ring and little finger, each coupled to an individual strain gauge force sensor (Fig. 1). The height of the pistons can be adjusted but in this study we used a constant piston height of 15 mm across all subjects. Pistons have a contact surface of 15 mm diameter and are 20 mm apart. With increasing force the pistons move against a spring load over a range of 10 mm. The end of this dynamic (non- static) range is reached with 1N. Above 1N, forces are controlled isometrically. Thus each sensor measures force along the piston axis exerted from each finger independently. The precision of the sensor is <0.01N, with a range of 0–9N. Force data of each finger was sampled to a CED 1401 (with 10 kHz sampling rate/digit) connected to a computer running Spike 2v6 (Cambridge Electronic Design, www.ced.co.uk) software. Custom-written CED scripts provided real-time visual display of digit forces and target instructions or target forces.Fig. 1

Bottom Line: Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls.Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients.

View Article: PubMed Central - PubMed

Affiliation: FR3636 CNRS, Université Paris Descartes, Sorbonne Paris Cité, 75006, Paris, France. mteremetz@gmail.com.

ABSTRACT

Background: A high degree of manual dexterity is a central feature of the human upper limb. A rich interplay of sensory and motor components in the hand and fingers allows for independent control of fingers in terms of timing, kinematics and force. Stroke often leads to impaired hand function and decreased manual dexterity, limiting activities of daily living and impacting quality of life. Clinically, there is a lack of quantitative multi-dimensional measures of manual dexterity. We therefore developed the Finger Force Manipulandum (FFM), which allows quantification of key components of manual dexterity. The purpose of this study was (i) to test the feasibility of using the FFM to measure key components of manual dexterity in hemiparetic stroke patients, (ii) to compare differences in dexterity components between stroke patients and controls, and (iii) to describe individual profiles of dexterity components in stroke patients.

Methods: 10 stroke patients with mild-to-moderate hemiparesis and 10 healthy subjects were recruited. Clinical measures of hand function included the Action Research Arm Test and the Moberg Pick-Up Test. Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).

Results: Most stroke patients could perform the tracking task, as well as the single and multi-finger tapping tasks. However, only four patients performed the sequence task. Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls. Unwanted (erroneous) finger taps and overflow to non-tapping fingers were increased in patients. Dexterity components were not systematically related among each other, resulting in individually different profiles of deficient dexterity. Some of the FFM measures correlated with clinical scores.

Conclusions: Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients. The FFM can detect group differences and individual profiles of deficient dexterity. The FFM is a promising tool for the measurement of key components of manual dexterity after stroke and could allow improved targeting of motor rehabilitation.

No MeSH data available.


Related in: MedlinePlus