Finger Position Research Articles

Effects of wrist and finger posture on finger independence

Intended actions of one finger produce involuntary movement or force in other fingers. Mechanical and neural factors limit finger independence. The interplay between anatomical factors, wrist and finger postures, and finger independence remain unclear. The purpose of this study was to determine the effects of wrist and metacarpophalangeal (MCP) posture on involuntary finger forces and extensor digitorum (ED) activity. Twenty participants performed submaximal isometric finger extensions in three wrist positions (30° extension, neutral, and 30° flexion) and two MCP postures (straight and 90° flexion). Involuntary index finger force increased with MCP flexion, suggesting the importance of intertendinous connections in finger independence. Consistent with previous research, ED activity was generally higher in wrist extension than neutral and flexed postures. Understanding the role of passive properties within the hand may help us improve finger rehabilitation strategies.

E-waste driven eco-friendly highly sensitive wearable capacitive sensors for physical activity monitoring and sign language recognition

Owing to the continual and rapid advancement in technology, there has been a significant surge in electronic waste (e-waste) in recent decades, resulting in a harmful impact on our environment. Due to the growing demand for battery-powered devices, non-rechargeable batteries are a significant source of e-waste. This study describes fabricating a paper-based graphite strain sensor that is eco-friendly, inexpensive, biodegradable, and sensitive to the applied strain. Readily accessible materials are employed to design and fabricate MIDC strain sensors. The Modified Interdigital Capacitor (MIDC) structure is formed on basic printing paper, and the conductive substance made with the ink of a gel pen and graphite from a drained dry cell is painted on the paper and used as electrode material. The suggested sensor has a high gauge factor value of 1998.578 and fast response and recovery time of 112 ms and 100 ms, respectively. The proposed prototype has also been utilized to inspect fitness exercises and physical activities on the human body, inspiring the potential for its integration into wearable systems for various applications. Reliable detection and differentiation of finger positions are achieved, highlighting the sensor's potential for more advanced sign language applications. It’s a good option to integrate the suggested strain sensor in wearable systems, such as telemedicine systems, electromechanical detection, healthcare, and physical activity monitoring of humans.

Modeling the Dynamics of Prosthetic Fingers for the Development of Predictive Control Algorithms

In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis’ dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss.

Learning to Play Guitar with Robotic Hands

AbstractPlaying the guitar is a dexterous human skill that poses significant challenges in computer graphics and robotics due to the precision required in finger positioning and coordination between hands. Current methods often rely on motion capture data to replicate specific guitar playing segments, which restricts the range of performances and demands intricate post‐processing. In this paper, we introduce a novel reinforcement learning model that can play the guitar using robotic hands, without the need for motion capture datasets, from input tablatures. To achieve this, we divide the simulation task for playing guitar into three stages. (a): for an input tablature, we first generate corresponding fingerings that align with human habits. (b): based on the generated fingerings as the guidance, we train a neural network for controlling the fingers of the left hand using deep reinforcement learning, and (c): we generate plucking movements for the right hand based on inverse kinematics according to the tablature. We evaluate our method by employing precision, recall, and F1 scores as quantitative metrics to thoroughly assess its performance in playing musical notes. In addition, we conduct qualitative analysis through user studies to evaluate the visual and auditory effects of guitar performance. The results demonstrate that our model excels in playing most moderately difficult and easier musical pieces, accurately playing nearly all notes.

Benefits and Mode of Action of Various Hasta Mudra

Introduction: In this modern era, our lifestyle is changed and become sedentary. Lack of physical exercise, faulty dietary habits, increased stress, and environment changes lead to many diseases. Everybody is fed up by taking so many medicines. Hence, people are showing interest in drugless therapies. Practicing Hasta mudra is totally drugless and cost less. Aim: The aim of this article is to describe the preventive and curative benefits of Various Hasta Mudra and to explain the mode of action of Various Hasta Mudra. Materials and Methods: Various benefits and modes of action of Hasta Mudra have been explained on the basis of classical texts and literature. Results: With regular practice of Hasta mudra, various diseases can be prevented and cured. Discussion: Mudra should be performed for at least 45 min in a day. Conclusion: Hasta Mudra is helpful in Mudhra therapy can control stress, depression, anxiety, fear and promotion of mental health and happiness prevention and cure of many diseases. in Mudra.[3] In Shiva Samhita, it is mentioned that just by practicing The word mudra is a Sanskrit word which has the meaning “gesture” or “attitude.” Mudra can be described as emotional, psychic, devotional, and esthetic gestures or attitudes. Mudra are attitudes of energy flow, which help in linking person’s pranic force with universal or cosmic force.[1] The literal meaning of mudra is the expression of internal feelings by way of different postures of fingers, palms, hands feet, and or body. This is an integral part of yoga and a scientific knowledge of spirituality and physical well. There are different types of Mudra, but in this article, we are discussing only about Hasta Mudra. By doing Hasta mudra, subtle hand and finger movements make important connections in the nervous system and stimulate specific energy pathways. In Hatha Yoga, there are 10 mudra which are mentioned as Jaramarananashaka.[2] In Gheranda Samhita, there are 25 Mudra and according to it, various eye and body positions (Asana) and Locks (Bandha) are also included

A novel trident shaped microstrip antenna sensor for glucose detection in human blood

A novel trident-shaped microstrip patch antenna sensor with great sensitivity is developed to estimate the amounts of glucose in blood samples of human. The suggested sensor is built on a 30 × 24 × 1.6 mm3 FR-4 substrate layer with a quality factor of 64 and a dielectric constant value of 4.3 resonating at 4.5 GHz. A finger phantom is the replica of the human finger and it is originated in the electromagnetic simulator in order to forecast the glucose content. By positioning the phantom replica at various localities on the constructed antenna sensor, the frequency shifts are observed for various levels of glucose concentration in different degrees, ranging from 0 to 1000 mg/dL are detected. The proposed sensor can identify diabetic conditions in patients as it has a finger phantom positioned parallel to the feed at the top of the antenna giving a 156 MHz paramount shift in frequency, sensitivity of 156 kHz/(mg/dL), and a minimum frequency shift of 24 MHz and sensitivity of 24 kHz/(mg/dL). To confirm the suggested sensor's functionality in a real-time environment, its performance is examined for various actual human finger postures, and the resulting resonance frequencies are observed. In order to detect the levels of glucose concentration, the suggested sensor's error performance is calculated and are found to be near to 1%.

CNN Algorithm with SIFT to Enhance the Arabic Sign Language Recognition

Sign language is used as a primary means of communication by millions of people who suffer from hearing problems. The unhearing people used visual language to interact with each other, Represented in sign language. There are features that the hearing impaired use to understand each other, which are difficult for normal people to understand. Therefore, deaf people will struggle to interact with society. This research aims to introduce a system for recognizing hand gestures in Arabic Sign Language (ArSL) through training the Convolutional Neural Network (CNN) on the images of ArSL gestures launched by the University of Prince Mohammad Bin Fahd, Saudi Arabia. A Scale Invariant Feature Transform (SIFT) algorithm is used for creating the feature vectors that contain shape, finger position, size, center points of palm, and hand margin by extracting the Important features for images of ArSL and transforming them to points of the vector. The accuracy of the proposed system is 97% using the SIFT with CNN, and equal to 94.8% nearly without SIFT. Finally, the proposed system was tried and tested on a group of persons and its effectiveness was proven after considering their observations.

Methods of teaching flute playing: Synthesis of Ukrainian and European schools

The aim of the study was to investigate the methods of teaching flute playing through the unification of international schools. In the course of the study, a series of focus groups were held with 26 people, including experienced flutists, teachers of music schools and conservatories, and students of music faculties. The focus groups helped to find out which teaching methods are most effective and which aspects of Ukrainian and European schools can be successfully combined to achieve the best results in flute teaching. The study found that different breathing techniques, hand and finger positions, interpretation of musical works and development of musical ear are important for flute playing. The results of the study showed that the Ukrainian school emphasizes folk traditions and emotional expression, while the European school focuses on technical excellence and academic accuracy. The focus group participants agreed that a synthesis of these approaches could provide students with a more comprehensive music education, combining technical skills with a deep emotional understanding of music. Based on the data obtained, recommendations for teachers were developed that include a combination of techniques from both schools, which contributes to the harmonious development of students’ musical abilities. The study points out the importance of an individual approach and the need to adapt techniques to the needs of each student, which is a key aspect of successful flute teaching. This approach allows taking into account the individual characteristics of each musician, contributing to their harmonious development and success in their musical career

Combining Functional Electrical Stimulation (FES) to Elicit Hand Movements and a Mechanical Orthosis to Passively Maintain Wrist and Fingers Position in Individuals With Tetraplegia: A Feasibility Test

Combining Functional Electrical Stimulation (FES) to Elicit Hand Movements and a Mechanical Orthosis to Passively Maintain Wrist and Fingers Position in Individuals With Tetraplegia: A Feasibility Test

Design and Verification of Piano Playing Assisted Hand Exoskeleton Robot.

Finger technique is a crucial aspect of piano learning, and hand exoskeleton mechanisms effectively assist novice piano players in maintaining correct finger technique consistently. Addressing current issues with exoskeleton robots, such as the inability to provide continuous correction of finger technique and their considerable weight, a novel hand exoskeleton robot has been developed to enhance finger technique through continuous correction and reduced weight. Initial data are gathered using finger joint angle sensors to analyze movements during piano playing, focusing on the trajectory and angular velocity of key strikes. This analysis informs the design of a 6-bar double-closed-loop mechanism with an end equivalent sliding pair, using analytical methods to establish the relationship between motor extension and input rod rotation. Simulation studies assess the exoskeleton's motion space and dynamics, confirming its capability to meet structural and functional demands for accurate key striking. Prototype testing validates the exoskeleton's ability to maintain correct finger positioning and mimic natural strike speeds, thus improving playing technique while ensuring comfort and safety.

Use of computational modeling to examine fingertip force production in children with hemiplegic cerebral palsy

Most children with hemiplegic cerebral palsy (HCP), one of the most prevalent subtypes of cerebral palsy, struggle with grasping and manipulating objects. This impairment may arise from a diminished capacity to properly direct forces created with the finger pad due to aberrant force application. Children with HCP were asked to create maximal force with the index finger pad in the palmar (normal) direction with both the paretic and non-paretic hands. The resulting forces and finger postures were then applied to a computational musculoskeletal model of the hand to estimate the corresponding muscle activation patterns. Subjects tended to create greater shear force relative to normal force with the paretic hand (p < 0.05). The resultant force was directed 33.6°±10.8° away from the instructed palmar direction in the paretic hand, but only 8.0°±7.3° in the non-paretic hand. Additionally, participants created greater palmar force with the non-paretic hand than with the paretic hand (p < 0.05). These differences in force production are likely due to differences in muscle activation pattern, as our computational models showed differences in which muscles are active and their relative activations when recreating the measured force vectors for the two hands (p < 0.01). The models predicted reduced activation in the extrinsic and greater reductions in activation in the intrinsic finger muscles, potentially due to reduced voluntary activation or muscle atrophy. As the large shear forces could lead to objects slipping from grasp, muscle activation patterns may provide an important target for therapeutic treatment in children with HCP.

Comparison of Treatment Outcomes of Different Immobilized Finger Positions After Repair of Flexor Tendon Rupture in Zones I and II: A Nonrandomized Controlled Trial With Historical Control Group.

Introduction The position of finger immobilization after flexor tendon rupture repair is changed to the extended position to prevent flexion contracture of the interphalangeal (IP) joint. However, in Strickland's assessment, We believe that a reduction in TAF (total active flexion) affects the outcome and that extension fixation is not necessarily the primary focus.For example, there are management methods that swap the fixed position between day and night. It is assumed that some effect is sought by placing the fingers in the flexed position. That is, the method of fixation is currently selected at individual facilities through twists and turns; however,the indications and criteria for selecting finger fixation positions are ambiguous, and they are apparently subject to the experience of therapists. This study aimed to characterize follow-up outcomes of flexion and extension fixation after zones I and II flexor tendon rupture repair. Methods This nonrandomized controlled trial with historical controls included 25 patients with flexor tendon ruptures of 30 fingers. The flexion fixation group consisted of 12 patients (n=16 fingers) and the extension fixation group consisted of 13 patients (n=14 fingers). The group with flexion fixation comprised patients who slept with their injured fingers in the flexed position (intervention group). The group with extension was retrospectively selected between April 2017 and March 2019, who slept with their injured finger in the extended position (historical control group). Strickland assessments of the range of motion (ROM) of each joint at the conclusion of hand therapy, the ratio of total active motion of the repaired, to the healthy finger (%TAF), and IP joint extension limitation angles were compared using Mann-Whitney U tests. Ratios of excellent and good ratings based on the Strickland assessment were compared using Fisher exact tests. Result The results of the Strickland assessment showed excellent or goodoutcomes for 22(73%) of 30 fingers, which was in line with our previous findings. Strickland ratings of excellent were achieved in seven (44%) of 16 fingers and four (28%) of 14 fingers in the groups with flexion and extension fixation, respectively. The outcomes for two (22%) of 16 fingers and seven (78%) of 14 fingers in the groups with flexion and extension fixation were, respectively, rated asgood. The proportion of patients rated as excellent was significantly higher in the group with flexionthan extension fixation (p=0.040). The %TAF and the active flexion angle of the distal interphalangeal (DIP) joint were higher in the group with flexionthan extension fixation (p=0008 and p=0.025, respectively). Furthermore, the total angle of the IP joint limit of extension did not significantly differ between the groups. Conclusion Flexion fixation after flexor tendon rupture achieved an excellent Strickland rating and was more effective than extension fixation, especially in terms of the active flexion ROM of the DIP joint. Flexion fixation might be an alternative to extension fixation because the range of flexion should be greater and might provide a range of finger extension motion equivalent to that of extension fixation.

Mechanical Interaction Between the Tendons of the Extrinsic Finger Flexors

ABSTRACTHumans possess an incredible capacity for dexterity, but independent finger control is limited. One factor constraining finger independence is the connections between the tendons of the extrinsic finger muscles. The aim of this study was to assess to what extent the linkages between the distal tendons of flexor digitorum profundus (FDP) and those of the flexor digitorum superficialis (FDS) constrain finger independence. Experiments on human fresh frozen cadaveric upper extremities (n = 6) were performed. First, one finger (target) was flexed whereas the other (nontarget) fingers were held in a nearly extended position. The change (Δ) in total flexion angle (∑Θ; i.e., the sum of angles of the different finger joints) of the target finger from the ∑Θ corresponding to the extended position at the start of the movement until the ∑Θ corresponding to the onset of force exertion at the nontarget fingers was assessed. Second, the distribution of force across the four fingers upon loading the tendon of the target finger was assessed for two finger positions (extended, 90° flexion of metacarpal phalangeal joint). For both muscles and for all fingers, the range of independent movement was small (&lt;7°). Δ∑Θ at force onset was lowest for fingers immediately adjacent to the target finger and highest for more distant fingers. For both muscles and for all fingers, some of the target finger force (&lt;14% for FDP, &lt;2% for FDS) was distributed to the nontarget fingers, which increased (up to 58%) only for FDP in response to target finger flexion. We conclude that mechanical connections between the FDP and FDS tendons constrain finger independence. Such constraints become apparent when moving one finger relative to the other fingers.

Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features

Manual acupuncture manipulation (MAM) is essential in traditional Chinese medicine treatment. MAM action recognition is important for junior acupuncturists’ training and education; however, there are obvious personalized differences in hand gestures among expert acupuncturists for the same type of MAM. In addition, during the MAM operations, the magnitude and frequency of the expert acupuncturists’ hand shape and relative needle-holding finger position changes are tiny and fast, resulting in difficulties in observing MAM action details. Thus, we propose a Spatial Multiscale Interactive Fusion MAM Recognition Network to solve the difficulties in MAM recognition. First, this paper presents an optical flow-based hand motion contour global feature extraction method for acupuncture hand shape. Second, to explore the motion rule between the needle-holding fingers during the MAM operations, we design a quantitative description method of the relative motion of the needle-holding fingers: an “interactive attention module,” which achieves feature fusion and mines the correlation between different scales of MAM action features. Finally, the proposed MAM recognition method was validated by 20 acupuncturists from the Beijing University of Traditional Chinese Medicine and 10 from the Beijing Zhongguancun Hospital who participated in the MAM video signal collection. The proposed recognition method achieves the highest average validation accuracy of 95.3% and the highest test accuracy of 96.0% for four typical MAMs, proving its feasibility and effectiveness.

Simulation of performance skills teaching system based on machine vision and sensor audio signal processing

In the traditional network media education system, there is little content about skill performance, and the malleability is poor. With the combination of Internet technology, online teaching videos of performance skills began to be promoted on a large scale, and with the help of the Internet, the learning of online performance skills has been updated. The aim of this study is to develop a performance skills teaching system based on machine vision and sensor audio signal processing technology to provide a simulated performance environment and personalized instruction. In this study, multiple sensors were used to capture various movement data during the performance of students. Through the use of sensor technology, the system is able to capture information about students' posture, finger position and force in real time, and analyze posture and movement in combination with machine vision technology. The system can also process and analyze the audio signals in the students' performance to evaluate the accuracy and fluency of the playing technique. The application of sensor technology enables the system to accurately capture students' movements and provide timely feedback. The use of machine vision technology allows the system to accurately analyze the student's posture and movements to provide more targeted guidance. The application of audio signal processing algorithm enables the system to objectively evaluate the students' playing skills and help them improve the accuracy and fluency in the playing process.

Biases in hand perception are driven by somatosensory computations, not a distorted hand model

To sense and interact with objects in the environment, we effortlessly configure our fingertips at desired locations. It is therefore reasonable to assume that the underlying control mechanisms rely on accurate knowledge about the structure and spatial dimensions of our hand and fingers. This intuition, however, is challenged by years of research showing drastic biases in the perception of finger geometry.1,2,3,4,5 This perceptual bias has been taken as evidence that the brain's internal representation of the body's geometry is distorted,6 leading to an apparent paradox regarding the skillfulness of our actions.7 Here, we propose an alternative explanation of the biases in hand perception-they are the result of the Bayesian integration of noisy, but unbiased, somatosensory signals about finger geometry and posture. To address this hypothesis, we combined Bayesian reverse engineering with behavioral experimentation on joint and fingertip localization of the index finger. We modeled the Bayesian integration either in sensory or in space-based coordinates, showing that the latter model variant led to biases in finger perception despite accurate representation of finger length. Behavioral measures of joint and fingertip localization responses showed similar biases, which were well fitted by the space-based, but not the sensory-based, model variant. The space-based model variant also outperformed a distorted hand model with built-in geometric biases. In total, our results suggest that perceptual distortions of finger geometry do not reflect a distorted hand model but originate from near-optimal Bayesian inference on somatosensory signals.

The Design of a Speech Recognition-Based Finger Training Rehabilitation Device: Mechanical and System Aspects

Aiming to address the extended diagnosis and recovery period along with low efficiency in traditional stroke patient rehabilitation, this study introduces a speech recognition-based finger rehabilitation training control system. This system enables patients to perform finger exercises while providing feedback on finger angle, speed, and position. Furthermore, it offers rehabilitation physicians valuable data for evaluation and reference in finger rehabilitation. The system is divided into hardware circuitry, a lower computer control system, and a voice recognition human-computer interaction system, all working in conjunction with the finger movement perception system. By applying the Hidden Markov Model (HMM) algorithm to the voice interaction system for pattern matching, the finger rehabilitation system undergoes simulation testing. The results demonstrate that the proposed rehabilitation training system based on speech recognition meets design requirements, ensuring safety, reliability, and substantial application potential in future finger rehabilitation training.

Inter- and intra-athlete technique variability of conventional new ball swing bowling in elite and pre-elite Australian male fast bowlers

ABSTRACT This study aimed to investigate inter- and intra-athlete technique variability in pre-elite and elite Australian fast bowlers delivering new ball conventional swing bowling. Ball grip angle and pelvis, torso, shoulder, elbow, wrist, upper arm, forearm, and hand kinematics were investigated at the point of ball release for inswing and outswing deliveries. Descriptive evaluations of group and individual data and k-means cluster analyses were used to assess inter- and intra-bowler technique variability. Inter-athlete technique and ball grip variability were identified, demonstrating that skilled bowlers use individualised strategies to generate swing. Functional movement variability was demonstrated by intra-athlete variability in successful swing bowling trials. Bowlers demonstrated stable technique parameters in large proximal body segments of the pelvis and torso, providing a level of repeatability to their bowling action. Greater variation was observed in bowling arm kinematics, allowing athletes to manipulate the finger and ball position to achieve the desired seam orientation at the point of ball release. This study demonstrates that skilled bowlers use individualised techniques and grips to generate swing and employ technique variations in successive deliveries. Coaches should employ individualised training strategies and use constraints-led approaches in training environments to encourage bowlers to seek adaptive movement solutions to generate swing.

Multi-Sensor Laser System for Electric Guitar Pitch-Detection.

To attain a direct MIDI output from an electric guitar, we devised and implemented a sophisticated laser sensor system capable of measuring finger positions. This sensor operates on the principle of optical triangulation, employing six lasers and seven position-sensing detectors that are time-multiplexed. The speed and precision of this sensor system meet the necessary criteria for creating an electric guitar with a direct digital output, perfectly satisfying the application's requirements.

The Design of a Speech Recognition-Based Finger Training Rehabilitation Device: Mechanical and System Aspects

This research introduces a novel speech recognition-based control system for finger rehabilitation aimed at addressing the inefficiencies and prolonged recovery periods commonly associated with traditional stroke rehabilitation methods. The system enables stroke patients to engage in finger exercises while simultaneously providing real-time feedback on the finger's angle, speed, and position. This feedback is invaluable for rehabilitation physicians, offering critical data for assessing and refining rehabilitation strategies. The system architecture is composed of three main components: the hardware circuitry, a lower-level computer control system, and a voice recognition-based human-computer interaction system, all integrated with a finger movement perception system. Employing the Hidden Markov Model (HMM) for pattern recognition in the voice interaction component, the system has undergone simulation testing to verify its effectiveness. The findings confirm that the speech recognition-based rehabilitation training system meets all design expectations, providing a safe, reliable, and promising approach for enhancing finger rehabilitation practices.