scholarly journals Identification of 7 Movements of the Human Hand Using sEMG - 360° on the Forearm

2021 ◽  
Author(s):  
◽  
A. Ibarra-Fuentes
Keyword(s):  
Time Domain ◽  
Index Finger ◽  
Ring Finger ◽  
Middle Finger ◽  
Svm Classifier ◽  
Human Hand ◽  
Absolute Value ◽  
The Time Domain ◽  
Flexion And Extension ◽  
Semg Signals

This document shows the identification of 7 gestures (movements) of the human hand from sEMG – 360° signals in the forearm. sEMG – 360° is the sEMG measurement through 8 channels every 45° making a total of 360°. When making a hand gesture, there will be 8 independents sEMG signals that will be used to identify the movement. The 7 gestures to identify are: relaxed hand (closed), open hand (fingers extended), flexion and extension of the little finger, the ring finger, the middle finger, the index finger and the thumb separately. 100 samples of each of the gesture were captured and 3 feature extractors were applied in the time domain (mean absolute value (MAV), root mean square value (RMS) and area vale under the curve (CUA)), then a vector support machine (SVM) classifier was applied to each extractor. The movements were identified and the percentage of accuracy in the identification was calculated for each extractor + SVM classifier. The calculation of the percentage of accuracy took into account the 8 channels for each gesture. 97.61 % accuracy was achieved in the identification of human hand gestures by applying sEMG – 360°.

scholarly journals Prediction of Flexion and Extension Movements of 4 Fingers of the Hand Using a New Labeled Method

2021 ◽  
Vol 2008 (1) ◽  
pp. 012015
Author(s):  
J A García Torres ◽  
A Ibarra Fuentes ◽  
E Morales Sánchez ◽  
A Hernández Zavala
Keyword(s):  
Neural Network ◽  
Ring Finger ◽  
Middle Finger ◽  
Mean Value ◽  
Semg Signal ◽  
The Neural Network ◽  
The Time Domain ◽  
Labeling Method ◽  
Time Segmentation ◽  
Flexion And Extension

Abstract This work presents a neural network classifier for identifying the flexion and extension movements for four fingers from the hand, out of the surface electromyography signals in the forearm muscles. A new labeled data method was proposed based on time segmentation to relate the sEMG signal with the corresponding finger movement. This is a different way of labeling the data for training the neural network, a llowing to reduce the amount of training gesture hand. The experiment consists of 10 sessions in which the fingers are flexed randomly, one at a time for 2 minutes with a 16ms sample time. The absolute mean value (MAV) is used as a feature extractor in the time domain to a verage 5 samples a nd the normalized data is used for the neural network. Results show that this system with the labeled data method, provides a 98.83% precision value for the index finger, 93.46% for the ring finger, 80.34% for the middle finger, and 68.46% for the little finger. The results are simila r to those found in the literature where they classify different gestures using the conventional labeling method.

scholarly journals Comparable behaviour of ring and little fingers due to an artificial reduction in thumb contribution to hold objects

2020 ◽  
Author(s):  
Banuvathy Rajakumar ◽  
Varadhan SKM
Keyword(s):  
Normal Force ◽  
Safety Margin ◽  
Ring Finger ◽  
Middle Finger ◽  
Human Hand ◽  
Mechanical Advantage ◽  
Free Condition ◽  
Normal Forces ◽  
Fixed Condition ◽  
Little Finger

AbstractBackgroundThe human hand plays a crucial role in accomplishing activities of daily living. The contribution of each finger in the human hand is remarkably unique in establishing object stabilization. According to the mechanical advantage hypothesis, the little finger tends to exert a greater normal force than the ring finger during a supination moment production task to stabilize the object. Similarly, during pronation, the index finger produces more normal force when compared with the middle finger. Hence, the central nervous system employs the peripheral fingers for torque generation to establish the equilibrium as they have a mechanical advantage of longer moment arms for normal force. In our study, we tested whether the mechanical advantage hypothesis is supported in a task in which the contribution of thumb was artificially reduced. We also computed the safety margin of the individual fingers and thumb.MethodologyFifteen participants used five-finger prismatic precision grip to hold a custom-built handle with a vertical railing on the thumb side. A slider platform was placed on the railing such that the thumb sensor could move either up or down. There were two experimental conditions. In the “Fixed” condition, the slider was mechanically fixed, and hence the thumb sensor could not move. In the “Free” condition, the slider platform on which the thumb sensor was placed could freely move. In both conditions, the instruction was to grasp and hold the handle (and the platform) in static equilibrium. We recorded tangential and normal forces of all the fingers.ResultsThe distribution of fingertip forces and moments changed depending on whether the thumb platform was movable (or not). In the free condition, the drop in the tangential force of thumb was counteracted by an increase in the normal force of the ring and little finger. Critically, the normal forces of the ring and little finger were statistically equivalent. The safety margin of the index and middle finger did not show a significant drop in the free condition when compared to fixed condition.ConclusionWe conclude that our results does not support the mechanical advantage hypothesis at least for the specific mechanical task considered in our study. In the free condition, the normal force of little finger was comparable to the normal force of the ring finger. Also, the safety margin of the thumb and ring finger increased to prevent slipping of the thumb platform and to maintain the handle in static equilibrium during the free condition. However, the rise in the safety margin of the ring finger was not compensated by a drop in the safety margin of the index and middle finger.

An anatomic study of flexor tendon sheaths: a cadaveric study

2009 ◽  
Vol 34 (6) ◽  
pp. 762-765 ◽  
Author(s):  
J. M. FUSSEY ◽  
K. F. CHIN ◽  
N. GOGI ◽  
S. GELLA ◽  
S. C. DESHMUKH
Keyword(s):  
Flexor Tendon ◽  
Index Finger ◽  
Ring Finger ◽  
Middle Finger ◽  
Tendon Sheath ◽  
Water Soluble ◽  
Anatomic Study ◽  
Patterns Of Communication ◽  
Flexor Tendon Sheath ◽  
Spread Of Infection

Previous descriptions of the pattern of communication between the digital flexor tendon sheaths have been largely based on imaging studies. An anatomic study on 12 cadaveric hands was conducted using water soluble dye and directly observed patterns of communication between the digital flexor tendon sheaths and the radial and ulnar bursae. Four out of twelve specimens (33%) demonstrated a communication between the radial and ulnar bursae. The ulnar bursa communicated with the ring finger flexor sheath in two specimens, and the index finger flexor sheath in two specimens. One hand (8.3%) showed communication between the middle finger tendon sheath and radial bursa and between the index finger flexor tendon sheath and radial bursa. These findings show a considerable level of variation in communicating patterns between the synovial sheaths of the hand and wrist. Clinicians should be aware of the possibility of variations to the classical presentation of spread of infection through the digital flexor sheaths.

scholarly journals REGRESSION ANALYSIS TO DETERMINE STATURE FROM HAND ANTHROPOMETRY AMONG ILOCANO POPULATION IN PHILIPPINES FOR PERSON IDENTIFICATION

2021 ◽  
Vol 11 (1) ◽  
pp. 20
Author(s):  
T.Nataraja Moorthy
Keyword(s):  
Ring Finger ◽  
Middle Finger ◽  
Size Analysis ◽  
Human Hand ◽  
Regression Equations ◽  
Person Identification ◽  
Left Hand ◽  
Length Measurements ◽  
The Right ◽  
Hand Length

Stature determination aids the person identification during forensic investigation. The human hand research is the current topic of interest among forensic scientist, forensic medicine experts and anthropologists. Based on sample size analysis, the study involved consented 60 males and 60 females, age ranged from 18 to 55 years old. Stature and hand lengths measurements were made with Stadiometer and Vernier Calipers for analysis.  From each participant, ten hand length measurements, five from left and five from right hands were taken. The five length measurements in left hand are the inter-distance between the distal traverse crease of the wrist (LH) and tip of thumb (T), index finger (I), middle finger (M), ring finger (R) & little finger (L), as abbreviated LHT, LHI, LHM, LHR and LHL. Similarly, the right hand lengths indicated as RHT, RHI, RHM, RHR, and RHL.  The data were statistically analyzed by using SPSS software, version 23 and column chart. The information about age, gender, name, and place of origin of the participants was coded for easy reference. This study finally developed regression equations to determine stature from hand anthropometry among Ilocano population in Philippines for person identification

scholarly journals Comparable behaviour of ring and little fingers due to an artificial reduction in thumb contribution to hold objects

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9962
Author(s):  
Banuvathy Rajakumar ◽  
Varadhan SKM
Keyword(s):  
Normal Force ◽  
Safety Margin ◽  
Ring Finger ◽  
Middle Finger ◽  
Human Hand ◽  
Mechanical Advantage ◽  
Free Condition ◽  
Normal Forces ◽  
Fixed Condition ◽  
Little Finger

Background The human hand plays a crucial role in accomplishing activities of daily living. The contribution of each finger in the human hand is remarkably unique in establishing object stabilization. According to the mechanical advantage hypothesis, the little finger tends to exert a greater normal force than the ring finger during a supination moment production task to stabilize the object. Similarly, during pronation, the index finger produces more normal force when compared with the middle finger. Hence, the central nervous system employs the peripheral fingers for torque generation to establish the equilibrium as they have a mechanical advantage of longer moment arms for normal force. In our study, we tested whether the mechanical advantage hypothesis is supported in a task in which the contribution of thumb was artificially reduced. We also computed the safety margin of the individual fingers and thumb. Methodology Fifteen participants used five-finger prismatic precision grip to hold a custom-built handle with a vertical railing on the thumb side. A slider platform was placed on the railing such that the thumb sensor could move either up or down. There were two experimental conditions. In the “Fixed” condition, the slider was mechanically fixed, and hence the thumb sensor could not move. In the “Free” condition, the slider platform on which the thumb sensor was placed could freely move. In both conditions, the instruction was to grasp and hold the handle (and the platform) in static equilibrium. We recorded tangential and normal forces of all the fingers. Results The distribution of fingertip forces and moments changed depending on whether the thumb platform was movable (or not). In the free condition, the drop in the tangential force of thumb was counteracted by an increase in the normal force of the ring and little finger. Critically, the normal forces of the ring and little finger were statistically equivalent. The safety margin of the index and middle finger did not show a significant drop in the free condition when compared to fixed condition. Conclusion We conclude that our results does not support the mechanical advantage hypothesis at least for the specific mechanical task considered in our study. In the free condition, the normal force of little finger was comparable to the normal force of the ring finger. Also, the safety margin of the thumb and ring finger increased to prevent slipping of the thumb platform and to maintain the handle in static equilibrium during the free condition. However, the rise in the safety margin of the ring finger was not compensated by a drop in the safety margin of the index and middle finger.

scholarly journals Analysis of the effect of fingertip size for sex differences in current perception thresholds with transcutaneous electrical stimulation in healthy subjects

2021 ◽  
Author(s):  
Shin-ichiro Seno ◽  
Hideaki Shimazu ◽  
Eiki Kogure ◽  
Atsushi Watanabe ◽  
Hiroko Kobayashi
Keyword(s):  
Sex Differences ◽  
Healthy Subjects ◽  
Index Finger ◽  
Ring Finger ◽  
Middle Finger ◽  
Perception Threshold ◽  
Left Hand ◽  
Current Perception Threshold ◽  
Males And Females ◽  
Little Finger

Abstract Objective This study aimed to measure the current perception threshold (CPT) of five fingertips of the left hand in healthy subjects and analyze whether sex differences in perception thresholds are suppressed when adjusting for fingertip size among males and females. Results For fingertips from the thumb to the little finger, the males’ CPT values were 1.03, 0.83, 0.86, 0.86, and 0.88 mA; the females’ results were 0.63, 0.55, 0.54, 0.51, and 0.50 mA. The CPTs were higher in males than in females for every fingertip. Upon adjusting for fingertip length, the log-transformed CPT values were found to have sex differences, except for the index finger: thumb, t(20.05) = 3.493, p = 0.002; middle finger, U(30) = 44.50, p = 0.005; ring finger, t(30) = 55.50, p = 0.018; little finger, U(30) = 30.00, p = 0.001. Similarly, the CPT values, transformed into log values when adjusting for the fingertip area, were found to have sex differences for three fingertips: thumb, t(18) = 2.649, p = 0.016; middle finger, U(20) = 12.00, p = 0.004; ring finger, t(18) = 2.206, p = 0.041. According to this study, sex differences in CPTs were not completely abolished by adjusting for fingertip length or area.

scholarly journals Detection of inflammation from finger temperature profile in rheumatoid arthritis

2019 ◽  
Vol 57 (12) ◽  
pp. 2629-2639
Author(s):  
J. Pauk ◽  
M. Ihnatouski ◽  
A. Wasilewska
Keyword(s):  
Rheumatoid Arthritis ◽  
Standard Deviation ◽  
Index Finger ◽  
Ring Finger ◽  
Provocation Test ◽  
Middle Finger ◽  
Temperature Profiles ◽  
Operating Characteristics ◽  
Cold Provocation Test ◽  
Cold Provocation

Abstract Rheumatoid arthritis (RA) is a chronic inflammatory tissue disease that leads to cartilage, bone, and periarticular tissue damage. This study aimed to investigate whether the use of infrared thermography and measurement of temperature profiles along the hand fingers could detect the inflammation and improve the diagnostic accuracy of the cold provocation test (0 °C for 5 s) and rewarming test (23 °C for180 s) in RA patients. Thirty RA patients (mean age = 49.5 years, standard deviation = 13.0 years) and 22 controls (mean age = 49.8 years, standard deviation = 7.5 years) were studied. Outcomes were the minimal and maximal: baseline temperature (T1), the temperature post-cooling (T2), the temperature post-rewarming (T3), and the Tmax-Tmin along the axis of each finger. The statistical significance was observed for the thumb, index finger, middle finger, and ring finger post-cooling and post-rewarming. Receiver operating characteristics (ROC) analysis to distinguish between the two groups revealed that for the thumb, index finger, middle finger, and ring finger, the area under the ROC curve was statistically significantly (p < 0.05) post-cooling. The cold provocation test used in this study discriminates between RA patients and controls and detects an inflammation in RA patients by the measurement of temperature profiles along the fingers using an infrared camera.

The Control Method for the Robot Hand Based on the Fuzzy Theory

1992 ◽  
Vol 4 (4) ◽  
pp. 262-267 ◽  
Author(s):  
Masafumi Uchida ◽  
◽  
Hideto Ide
Keyword(s):  
Control Method ◽  
Index Finger ◽  
Fuzzy Theory ◽  
Middle Finger ◽  
Human Hand ◽  
Robot Hand ◽  
Production Rules ◽  
Living Body ◽  
Physical Conditions ◽  
State Of Mind

By moving muscles, the myogenic potential (Electromyogram: EMG) is observed on the surface of the living body. It is considered that the EMG is useful for controlling a robot hand. However, the EMG depends on physical conditions, the state of mind and so on. So, the original EMG will be not used for controlling the robot hand directly. In this study, it is considered that the EMG relating the motion of the human hand is analyzed by the fuzzy theory for making the robot hand performs the same motion as the human hand. EMG were measured under the following conditions. (1) opening the hand, (2) bending the thumb, (3) bending the middle finger, (4) bending the index finger, (5) closing the hand, (6) not move. Six production rules were made with fuzzificate data resulted from fourier transforming the EMG (30-band 1/3 octave analysis). Also the EMG measured by experimental motion of the human hand was transformed into the fuzzificate date. Rates of recognitions were calculated in comparison with the six production rules and the experimental data. And one production rule with highest rate of recognition was used for recognition of movement of the human hand in the computer. From the experimental results, about 90% of movement were recognized by the computer. The results were applied to control the robot hand.

Choice Reaction Times for Structural Relationships between Possible Response Sequences

1993 ◽  
Vol 77 (3_suppl) ◽  
pp. 1203-1212 ◽  
Author(s):  
Kazunori Shidoji
Keyword(s):  
Index Finger ◽  
Ring Finger ◽  
Reaction Times ◽  
Middle Finger ◽  
Left Hand ◽  
Right Hand ◽  
Structural Relationships ◽  
Middle Ring ◽  
The Right ◽  
Choice Reaction Times

To investigate human motor programming, choice reaction times were measured on tasks for which subjects made choices between two alternative finger-tapping-movement sequences. The total-number-of-responses and the hierarchical editor models were tested. In Exp. 1 the choice was carried on the situations with the same total numbers of possible responses and different structural relations between alternative sequences. The right-hand reaction times in mirror choice (e.g., subject chose between the middle, index, and ring finger sequences of the left or right hand) were shorter than those in nonmirror choice (e.g., subject chose between the middle, index, and ring finger sequence on one hand and the middle, ring, and index finger sequence on the other hand); the total-number-of-responses model was not supported. In Exp. 2 two conditions had the same operation numbers of the hierarchical editor model. In Condition 1 subjects chose between the index finger of the right hand and the ring, index, and middle finger sequence of the left hand. In Condition 2 subjects chose between the index, ring, and middle finger sequences of the left or right hand. The reaction time in the former condition was shorter than that in the latter condition. Exp. 2 exhibited a counterexample of the hierarchical editor model that had been fairly robust in previous studies.

scholarly journals Extra-Articular Tenosynovial Chondromatosis of the Finger: A Case Series Study of Three Cases, One Including Excessive Osseous Invasion

2017 ◽  
Vol 11 (1) ◽  
pp. 417-423 ◽  
Author(s):  
Akio Sakamoto ◽  
Takahiko Naka ◽  
Eisuke Shiba ◽  
Masanori Hisaoka ◽  
Shuichi Matsuda
Keyword(s):  
Index Finger ◽  
Case Series ◽  
Ring Finger ◽  
Middle Finger ◽  
Synovial Chondromatosis ◽  
Fatty Tissue ◽  
Complex Anatomy ◽  
Case Series Study ◽  
History Of ◽  
Tenosynovial Chondromatosis

Background: Synovial chondromatosis is characterized by cartilaginous metaplasia in synovial tissues. Extra-articular tenosynovial chondromatosis is considered to be an anatomical counterpart of articular synovial chondromatosis. Extra-articular tenosynovial chondromatosis occurs preferentially in the hand, although its frequency is low. Results: We report three cases of extra-articular tenosynovial chondromatosis. A 65-year-old female presented with a history of symptoms over 40 years related to the dorsum of her index finger (Case 1), A 46-year-old female presented with a 6-month history of symptoms at the volar surface of her middle finger (Case 2), and a 66-year-old male presented with a 3-month history of symptoms in a dorsal ring finger. Case 2 had evidence of ossification, which could be classified as osteochondromatosis. Interestingly, the index finger lesions (Case 1) were accompanied by excessive bone involvement. The signal intensity of T2-weighted magnetic resonance imaging varies from low to high, possibly reflecting histological variations, such as ossification and fatty tissue changes. All lesions were resected without complications. Conclusion: Variations in anatomical sites suggest that overuse or mechanical overloading was not causative. Extensive involvement of the nearby tendon and joint capsule, as well as the bone, would require attention during the resection. Preoperative analysis of images is important, not only for the diagnosis, but also to assess the extent of the lesion, particularly given the complex anatomy of the finger.

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