scholarly journals Intrinsic Hand Muscle Activation for Grasp and Horizontal Transport

2007 ◽  
Author(s):  
Sara A. Winges ◽  
Bornali Kundu ◽  
John F. Soechting ◽  
Martha Flanders
Keyword(s):  
Muscle Activation ◽  
Horizontal Transport ◽  
Intrinsic Hand Muscle ◽  
Hand Muscle

scholarly journals Role of Interhemispheric Cortical Interactions in Poststroke Motor Function

2019 ◽  
Vol 33 (9) ◽  
pp. 762-774 ◽  
Author(s):  
Jacqueline A. Palmer ◽  
Lewis A. Wheaton ◽  
Whitney A. Gray ◽  
Mary Alice Saltão da Silva ◽  
Steven L. Wolf ◽  
...  
Keyword(s):  
Motor Function ◽  
Muscle Activation ◽  
Primary Motor Cortex ◽  
Silent Period ◽  
Negatively Associated ◽  
Interhemispheric Coherence ◽  
Stroke Group ◽  
Active Condition ◽  
Hand Muscle ◽  
Clinical Measures

Background/Objective. We investigated interhemispheric interactions in stroke survivors by measuring transcranial magnetic stimulation (TMS)–evoked cortical coherence. We tested the effect of TMS on interhemispheric coherence during rest and active muscle contraction and compared coherence in stroke and older adults. We evaluated the relationships between interhemispheric coherence, paretic motor function, and the ipsilateral cortical silent period (iSP). Methods. Participants with (n = 19) and without (n = 14) chronic stroke either rested or maintained a contraction of the ipsilateral hand muscle during simultaneous recordings of evoked responses to TMS of the ipsilesional/nondominant (i/ndM1) and contralesional/dominant (c/dM1) primary motor cortex with EEG and in the hand muscle with EMG. We calculated pre- and post-TMS interhemispheric beta coherence (15-30 Hz) between motor areas in both conditions and the iSP duration during the active condition. Results. During active i/ndM1 TMS, interhemispheric coherence increased immediately following TMS in controls but not in stroke. Coherence during active cM1 TMS was greater than iM1 TMS in the stroke group. Coherence during active iM1 TMS was less in stroke participants and was negatively associated with measures of paretic arm motor function. Paretic iSP was longer compared with controls and negatively associated with clinical measures of manual dexterity. There was no relationship between coherence and. iSP for either group. No within- or between-group differences in coherence were observed at rest. Conclusions. TMS-evoked cortical coherence during hand muscle activation can index interhemispheric interactions associated with poststroke motor function and potentially offer new insights into neural mechanisms influencing functional recovery.

scholarly journals Intrinsic Hand Muscle Function, Part 1: Creating a Functional Grasp

2013 ◽  
Vol 38 (11) ◽  
pp. 2093-2099 ◽  
Author(s):  
Ursina Arnet ◽  
David A. Muzykewicz ◽  
Jan Fridén ◽  
Richard L. Lieber
Keyword(s):  
Muscle Function ◽  
Intrinsic Hand Muscle ◽  
Hand Muscle

scholarly journals EFFECTS OF ALTERING PARAMETERS FOR ELECTRICAL STIMULATION ON CUTANEOUS REFLEXES IN HUMAN INTRINSIC HAND MUSCLE

2005 ◽  
Vol 54 (4) ◽  
pp. 315-323 ◽  
Author(s):  
TSUYOSHI NAKAJIMA ◽  
TAKASHI ENDOH ◽  
MASANORI SAKAMOTO ◽  
TOSHIKI TAZOE ◽  
TOMOYOSHI KOMIYAMA
Keyword(s):  
Electrical Stimulation ◽  
Cutaneous Reflexes ◽  
Intrinsic Hand Muscle ◽  
Hand Muscle

scholarly journals New Device for Intrinsic Hand Muscle Strength Measurement: An Alternative to Strain Gauge Handheld Dynamometer

2017 ◽  
Vol 42 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Jagannathan Madhanagopal ◽  
Om Prakash Singh ◽  
Vikram Mohan ◽  
Kathiresan V. Sathasivam ◽  
Abdul Hafidz Omar ◽  
...  
Keyword(s):  
Muscle Strength ◽  
Clinical Decision Making ◽  
Force Measurement ◽  
Interobserver Reliability ◽  
Clinical Decision ◽  
Strength Measurement ◽  
New Device ◽  
Intrinsic Hand Muscle ◽  
Hand Muscle ◽  
Accuracy And Repeatability

An accurate measurement of intrinsic hand muscle strength (IHMS) is required by clinicians for effective clinical decision-making, diagnosis of certain diseases, and evaluation of the outcome of treatment. In practice, the clinicians use Intrins-o-meter and Rotterdam Intrinsic Hand Myometer for IHMS measurement. These are quite bulky, expensive, and possess poor interobserver reliability (37–52%) and sensitivity. The purpose of this study was to develop an alternative lightweight, accurate, cost-effective force measurement device with a simple electronic circuit and test its suitability for IHMS measurement. The device was constructed with ketjenblack/deproteinized natural rubber sensor, 1-MΩ potential divider, and Arduino Uno through the custom-written software. Then, the device was calibrated and tested for accuracy and repeatability within the force range of finger muscles (100 N). The 95% limit of agreement in accuracy from −1.95 N to 2.06 N for 10 to 100 N applied load and repeatability coefficient of ±1.91 N or 6.2% was achieved. Furthermore, the expenditure for the device construction was around US$ 53. For a practical demonstration, the device was tested among 16 participants for isometric strength measurement of the ulnar abductor and dorsal interossei. The results revealed that the performance of the device was suitable for IHMS measurement.

Extrinsic versus intrinsic hand muscle dominance in finger flexion

2016 ◽  
Vol 41 (4) ◽  
pp. 392-399 ◽  
Author(s):  
A. Al-Sukaini ◽  
H. P. Singh ◽  
J. J. Dias
Keyword(s):  
Motion Tracking ◽  
Tendon Repair ◽  
Metacarpophalangeal Joint ◽  
Active Motion ◽  
Early Motion ◽  
Finger Flexion ◽  
Distal Interphalangeal ◽  
Intrinsic Hand Muscle ◽  
Hand Muscle ◽  
Intrinsic Muscles

This study aims to identify the patterns of dominance of extrinsic or intrinsic muscles in finger flexion during initiation of finger curl and mid-finger flexion. We recorded 82 hands of healthy individuals (18–74 years) while flexing their fingers and tracked the finger joint angles of the little finger using video motion tracking. A total of 57 hands (69.5%) were classified as extrinsic dominant, where the finger flexion was initiated and maintained at proximal interphalangeal and distal interphalangeal joints. A total of 25 (30.5%) were classified as intrinsic dominant, where the finger flexion was initiated and maintained at the metacarpophalangeal joint. The distribution of age, sex, dominance, handedness and body mass index was similar in the two groups. This knowledge may allow clinicians to develop more efficient rehabilitation regimes, since intrinsic dominant individuals would not initiate extrinsic muscle contraction till later in finger flexion, and might therefore be allowed limited early active motion. For extrinsic dominant individuals, by contrast, initial contraction of extrinsic muscles would place increased stress on the tendon repair site if early motion were permitted.

Muscle activation and time to task failure differ with load type and contraction intensity for a human hand muscle

2005 ◽  
Vol 167 (2) ◽  
pp. 165-177 ◽  
Author(s):  
Katrina S. Maluf ◽  
Minoru Shinohara ◽  
Jennifer L. Stephenson ◽  
Roger M. Enoka
Keyword(s):  
Muscle Activation ◽  
Human Hand ◽  
Task Failure ◽  
Hand Muscle

scholarly journals Force-Independent Distribution of Correlated Neural Inputs to Hand Muscles During Three-Digit Grasping

2010 ◽  
Vol 104 (2) ◽  
pp. 1141-1154 ◽  
Author(s):  
Brach Poston ◽  
Alessander Danna-Dos Santos ◽  
Mark Jesunathadas ◽  
Thomas M. Hamm ◽  
Marco Santello
Keyword(s):  
Muscle Activation ◽  
Force Production ◽  
Emg Activity ◽  
Muscle Coordination ◽  
Single Digit ◽  
Hand Muscles ◽  
Hand Muscle ◽  
Grasp Force ◽  
Independent Distribution ◽  
Coordination Patterns

The ability to modulate digit forces during grasping relies on the coordination of multiple hand muscles. Because many muscles innervate each digit, the CNS can potentially choose from a large number of muscle coordination patterns to generate a given digit force. Studies of single-digit force production tasks have revealed that the electromyographic (EMG) activity scales uniformly across all muscles as a function of digit force. However, the extent to which this finding applies to the coordination of forces across multiple digits is unknown. We addressed this question by asking subjects ( n = 8) to exert isometric forces using a three-digit grip (thumb, index, and middle fingers) that allowed for the quantification of hand muscle coordination within and across digits as a function of grasp force (5, 20, 40, 60, and 80% maximal voluntary force). We recorded EMG from 12 muscles (6 extrinsic and 6 intrinsic) of the three digits. Hand muscle coordination patterns were quantified in the amplitude and frequency domains (EMG–EMG coherence). EMG amplitude scaled uniformly across all hand muscles as a function of grasp force (muscle × force interaction: P = 0.997; cosines of angle between muscle activation pattern vector pairs: 0.897–0.997). Similarly, EMG–EMG coherence was not significantly affected by force ( P = 0.324). However, coherence was stronger across extrinsic than that across intrinsic muscle pairs ( P = 0.0039). These findings indicate that the distribution of neural drive to multiple hand muscles is force independent and may reflect the anatomical properties or functional roles of hand muscle groups.

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