Finger Gestures Research Articles

An Explorative Note on Apraxia Tests.

Apraxia is stated independent of primary motor disorders. However, patient groups suffering from stroke or dementia can reveal motor impairments. In this study we examined the dependence of apraxia tests of imitation and pantomime on a latent motor component using a principal component analysis. With samples sizes of 11 patients suffering from dementia of the Alzheimer's type and 15 healthy control subjects, clear limitations concerning the validity of the results are given. Nevertheless, we could observe strong dependence of the three apraxia tests, especially the imitation of finger and hand gestures, on a latent motor component in this preliminary examination. We suggest confirmation by larger samples sizes and to control for the basic motor capacity when testing for signs of apraxia in such patient samples.

Non – Vision Based Sensors for Dynamic Hand Gesture Recognition Systems: A Comparative Study

Gestures are considered as a type of configuration associated with motion in concerned body part, signifying meaningful information or expressing motion or intending to command and control. Wide ranges of sensors working with different technology are available in market. Gesture recognition process involves steps like data acquisition from sensor, segmentation, an algorithm for taking gesture data as input, an algorithm to extract parameters and algorithm to classify hand gestures. Three - dimensional hand gestures have been widely accepted for advanced applications like creation of virtual world where in users can feel the naturality of interacting or playing a musical instrument without presence of any physical device. Techniques for dynamic finger gesture recognition can be classified as visual based and wearable sensor based. The purpose of this paper is to compare various non – vision based sensors with different tracking technologies, updating advantages and drawbacks helping investigators and researchers working on this area.

Finger gesture recognition using a smartwatch with integrated motion sensors

Wearable device is becoming more and more popular, the emergence of wearable equipment, is widely used in daily life, medical, industrial and scientific research and so on. But most of the wearable equipment still needs people's actual operation, which will bring a lot of inconvenience. For example, when people's hands are not available, they can't be operated. Now, many sensors are embedded in off-the-shelf smartwatch, which make it possible to detect different finger gestures by using these sensors. Past research mostly concentrates on identifying gross hand gestures, which usually need the help of other additional hardware that is expensive and discomfort. In this work we utilize muscle activity data getting from integrated motion sensors (accelerometer, linear accelerometer and gyroscope) nested in smartwatch to recognize tiny finger gestures. We extract features within a 1 second sliding window. We test classification effect of different classifiers and different sensors. In the result, we can recognize 5 unremarkable finger gestures well and identify specific finger gestures with a high accuracy. The result can be applied on current popular smartwatches interaction. Also our result will lay the foundation for related research in finger gestures recognition field.

Mathematics textbook development for primary grades and its teachers in Mozambique

This is an analysis of the author’s involvement since 1970 in textbook development for primary schools and adult education in Mozambique, focusing on integrating local cultural traditions, covering the period up to 2013. As main example, addition of the type 8 + 5 = 13 is used around the question of whether textbooks (and curricula) advocate counting strategies and/or computation strategies. Different visualisations of these strategies in textbooks and corresponding manipulatives are analysed. During the 1990s, local languages started to be used in adult education, apart from Portuguese, Mozambique’s official language. Unschooled adults showed the importance of verbal computation in Mozambican languages—Bantu languages—most of which use also the auxiliary base five, apart from the base-ten numerals. The 2003 curriculum for primary education introduced the possibility of using local languages, and NGO’s started translating textbooks into 16 Mozambican languages. The paper includes an analysis of some of these textbooks and concludes with the author’s ideas in his teacher’s guides on teaching mathematics in Mozambican languages, showing the opportunities of verbal computation, exploring base-five numerals supported by finger gestures, instead of finger counting.

Role of Rayleigh numbers on characteristics of double diffusive salt fingers

Double diffusion convection, driven by two constituents of the fluid with different molecular diffusivity, is widely applied in oceanography and large number of other fields like astrophysics, geology, chemistry and metallurgy. In case of ocean, heat (T) and salinity (S) are the two components with varying diffusivity, where heat diffuses hundred times faster than salt. Component (T) stabilizes the system whereas components (S) destabilizes the system with overall density remains stable and forms the rising and sinking fingers known as salt fingers. Recent observations suggest that salt finger characteristics such as growth rates, wavenumber, and fluxes are strongly depending on the Rayleigh numbers as major driving force. In this paper, we corroborate this observation with the help of experiments, numerical simulations and linear theory. An eigenvalue expression for growth rate is derived from the linearized governing equations with explicit dependence on Rayleigh numbers, density stability ratio, Prandtl number and diffusivity ratio. Expressions for fastest growing fingers are also derived as a function various non-dimensional parameter. The predicted results corroborate well with the data reported from the field measurements, experiments and numerical simulations.

F153. Role of the premotor and precentral negative motor area in praxis: A direct electrical stimulation study with behavioral analysis

Introduction The negative motor area (NMA) is defined as a cortical area where 50 Hz electrical cortical stimulation produces inhibition of the voluntary movement such as finger tapping. The function of NMA has not been well delineated since previous 50 Hz stimulation studies mainly focused on the all or none response, namely, arrest of finger tapping at high current intensity. We hypothesized that NMA plays a role in higher motor behavior such as praxic movements. Our objective was to clarify the praxis-related function of NMA by applying stimulation at smaller intensity to evaluate the effect of stimulation. Methods Subjects were 5 patients with intractable focal epilepsy who underwent implantation of intracranial electrodes in the inferior frontal area for presurgical evaluation (IRB#C1062). After defining NMA with 50 Hz stimulation at high intensity for clinical purpose, stimulation was delivered at the intensity 1–3 mA smaller than that of clinical mapping in the following tasks: finger tapping, reach-to-grasp task, imitation of meaningless finger gesture, and pantomime of tool-use. The stroke of finger tapping, and the maximum aperture and reaching velocity in reach-to-grasp tasks are evaluated quantitatively with 3D motion capture system. Finger gesture and tool-use pantomime were evaluated qualitatively because of technical difficulty in applying the motion capture system. Results NMA was identified in the precentral gyrus in 3 patients, the ventral premotor area in 1 and at their border in 1. In 3 patients with precentral NMA, quantitative analysis revealed the significant decrease of stroke in finger tapping and maximum aperture in grasping, while the reaching velocity did not change significantly. Qualitative analysis showed mild slowing of finger gesture in 1 out of 3 patients but did not change the performance of tool-use pantomime. In the more rostral NMA (in the ventral premotor area and at the border), quantitative analysis also showed the significant decrease of stroke and aperture in finger tapping and grasping, respectively. In addition, reaching velocity was significantly decreased. Qualitative analysis showed either mild slowing (at the border) or arrest of finger gesture (ventral premotor area), and arrest of tool-use pantomime in 1 out of 2 patients (ventral premotor area). Conclusion Precentral NMA seems to play a more role in controlling elementary finger movements than praxic movements, and, when impaired, could be responsible for limb-kinetic apraxia. More rostral NMA is likely associated with more complex movements. These findings suggested functional subdivisions within NMA and may help understand the disability after focal resection of NMA.

EMG finger movement classification based on ANFIS

An increase number of people suffering from stroke has impact to the rapid development of finger hand exoskeleton to enable an automatic physical therapy. Prior to the development of finger exoskeleton, a research topic yet important i.e. machine learning of finger gestures classification is conducted. This paper presents a study on EMG signal classification of 5 finger gestures as a preliminary study toward the finger exoskeleton design and development in Indonesia. The EMG signals of 5 finger gestures were acquired using Myo EMG sensor. The EMG signal features were extracted and reduced using PCA. The ANFIS based learning is used to classify reduced features of 5 finger gestures. The result shows that the classification of finger gestures is less than the classification of 7 hand gestures.

SignFi

We propose SignFi to recognize sign language gestures using WiFi. SignFi uses Channel State Information (CSI) measured by WiFi packets as the input and a Convolutional Neural Network (CNN) as the classification algorithm. Existing WiFi-based sign gesture recognition technologies are tested on no more than 25 gestures that only involve hand and/or finger gestures. SignFi is able to recognize 276 sign gestures, which involve the head, arm, hand, and finger gestures, with high accuracy. SignFi collects CSI measurements to capture wireless signal characteristics of sign gestures. Raw CSI measurements are pre-processed to remove noises and recover CSI changes over sub-carriers and sampling time. Pre-processed CSI measurements are fed to a 9-layer CNN for sign gesture classification. We collect CSI traces and evaluate SignFi in the lab and home environments. There are 8,280 gesture instances, 5,520 from the lab and 2,760 from the home, for 276 sign gestures in total. For 5-fold cross validation using CSI traces of one user, the average recognition accuracy of SignFi is 98.01%, 98.91%, and 94.81% for the lab, home, and lab+home environment, respectively. We also run tests using CSI traces from 5 different users in the lab environment. The average recognition accuracy of SignFi is 86.66% for 7,500 instances of 150 sign gestures performed by 5 different users.

Repetitive transcranial magnetic stimulation reveals a role for the left inferior parietal lobule in matching observed kinematics during imitation.

Apraxia (a disorder of complex movement) suggests that the left inferior parietal lobule (IPL) plays a role in kinematic or spatial aspects of imitation, which may be particularly important for meaningless (i.e. unfamiliar intransitive) actions. Mirror neuron theories indicate that the IPL is part of a frontoparietal system that can support imitation by linking observed and stored actions through visuomotor matching, and have less to say about different subregions of the left IPL, or how different types of action (i.e. meaningful or meaningless) are processed for imitation. We used repetitive transcranial magnetic stimulation (rTMS) to bridge this gap and better understand the roles of the left supramarginal gyrus (SMG) and left angular gyrus (AG) in imitation. We also examined whether these areas are differentially involved in meaningful and meaningless action imitation. We applied rTMS over the left SMG, over the left AG or during a no-rTMS baseline condition, and then asked participants to imitate a confederate's actions whilst the arm and hand movements of both individuals were motion-tracked. rTMS over both the left SMG and the left AG reduced the velocity of participants' finger movements relative to the actor during imitation of finger gestures, regardless of action meaning. Our results support recent claims in apraxia and confirm a role for the left IPL in kinematic processing during gesture imitation, regardless of action meaning.

Finger Angle-Based Hand Gesture Recognition for Smart Infrastructure Using Wearable Wrist-Worn Camera

The arising of domestic robots in smart infrastructure has raised demands for intuitive and natural interaction between humans and robots. To address this problem, a wearable wrist-worn camera (WwwCam) is proposed in this paper. With the capability of recognizing human hand gestures in real-time, it enables services such as controlling mopping robots, mobile manipulators, or appliances in smart-home scenarios. The recognition is based on finger segmentation and template matching. Distance transformation algorithm is adopted and adapted to robustly segment fingers from the hand. Based on fingers’ angles relative to the wrist, a finger angle prediction algorithm and a template matching metric are proposed. All possible gesture types of the captured image are first predicted, and then evaluated and compared to the template image to achieve the classification. Unlike other template matching methods relying highly on large training set, this scheme possesses high flexibility since it requires only one image as the template, and can classify gestures formed by different combinations of fingers. In the experiment, it successfully recognized ten finger gestures from number zero to nine defined by American Sign Language with an accuracy up to 99.38%. Its performance was further demonstrated by manipulating a robot arm using the implemented algorithms and WwwCam to transport and pile up wooden building blocks.

A Study of Partial Image Classification of Vehicles Using Finger Gestures

A Study of Partial Image Classification of Vehicles Using Finger Gestures

Mutations in the BAF-Complex Subunit DPF2 Are Associated with Coffin-Siris Syndrome

Variants affecting the function of different subunits of the BAF chromatin-remodelling complex lead to various neurodevelopmental syndromes, including Coffin-Siris syndrome. Furthermore, variants in proteins containing PHD fingers, motifs recognizing specific histone tail modifications, have been associated with several neurological and developmental-delay disorders. Here, we report eight heterozygous de novo variants (one frameshift, two splice site, and five missense) in the gene encoding the BAF complex subunit double plant homeodomain finger 2 (DPF2). Affected individuals share common clinical features described in individuals with Coffin-Siris syndrome, including coarse facial features, global developmental delay, intellectual disability, speech impairment, and hypoplasia of fingernails and toenails. All variants occur within the highly conserved PHD1 and PHD2 motifs. Moreover, missense variants are situated close to zinc binding sites and are predicted to disrupt these sites. Pull-down assays of recombinant proteins and histone peptides revealed that a subset of the identified missense variants abolish or impaire DPF2 binding to unmodified and modified H3 histone tails. These results suggest an impairment of PHD finger structural integrity and cohesion and most likely an aberrant recognition of histone modifications. Furthermore, the overexpression of these variants in HEK293 and COS7 cell lines was associated with the formation of nuclear aggregates and the recruitment of both wild-type DPF2 and BRG1 to these aggregates. Expression analysis of truncating variants found in the affected individuals indicated that the aberrant transcripts escape nonsense-mediated decay. Altogether, we provide compelling evidence that de novo variants in DPF2 cause Coffin-Siris syndrome and propose a dominant-negative mechanism of pathogenicity.

A Single Layer 3-D Touch Sensing System for Mobile Devices Application

Touch sensing has been widely implemented as a main methodology to bridge human and machine interactions. The traditional touch sensing range is 2-D and therefore limits the user experience. To overcome these limitations, we propose a novel 3-D contactless touch sensing called Airtouch system, which improves user experience by remotely detecting single/multi-finger position. A single layer touch panel with triangle-shaped electrodes is proposed to achieve multitouch detection capability as well as manufacturing cost reduction. Moreover, an oscillator-based-capacitive touch sensing circuit is implemented as the sensing hardware with the bootstrapping technique to eliminate the interchannel coupling effects. To further improve the system accuracy, a grouping algorithm is proposed to group the useful channels’ data and filter out hardware noise impact. Finally, improved algorithms are proposed to eliminate the fringing capacitance effect and achieve accurate finger position estimation. EM simulation proved that the proposed algorithm reduced the maximum systematic error by 11 dB in the horizontal position detection. The proposed system consumes 2.3 mW and is fully compatible with existing mobile device environments. A prototype is built to demonstrate that the system can successfully detect finger movement in a vertical direction up to 6 cm and achieve a horizontal resolution up to 0.6 cm at 1 cm finger-height. As a new interface for human and machine interactions, this system offers great potential in finger movement detection and gesture recognition for small-sized electronics and advanced human interactive games for mobile device.

Interactive Holographic Display Based on Finger Gestures

In this paper, we demonstrate an interactive, finger-sensitive system which enables an observer to intuitively handle electro-holographic images in real time. In this system, a motion sensor detects finger gestures (swiping and pinching) and translates them into the rotation and enlargement/reduction of the holographic image, respectively. By parallelising the hologram calculation using a graphics processing unit, we realised the interactive handling of the holographic image in real time. In a demonstration of the system, we used a Leap Motion sensor and a phase modulation-type spatial light modulator with 1,920 × 1,080 pixels and a pixel pitch of 8.0 µm × 8.0 µm. The constructed interactive finger-sensitive system was able to rotate a holographic image composed of 4,096 point light sources using a swiping gesture and enlarge or reduce it using a pinching gesture in real time. The average calculation speed was 27.6 ms per hologram. Finally, we extended the constructed system to a full-colour reconstruction system that generates a more realistic three-dimensional image. The extended system successfully allowed the handling of a full-colour holographic image composed of 1,709 point light sources with a calculation speed of 22.6 ms per hologram.

Characteristics of a PHD Finger Subtype.

Although the plant homeodomain (PHD) finger superfamily is known for its site-specific readouts of histone tails, the origins of the mechanistic differences in histone H3 readout by different PHD subtypes remain less clear. We show that sequences containing the xCDxCDx motif in the PHD treble clef (xCDxCDx-PHD) constitute a distinct subtype, based on the following observations: (i) the amino acid composition of the binding site is strikingly different from other subtypes due to position-specific enrichment of negatively charged and bulky nonpolar residues, (ii) the binding site positions are mutually and positively correlated, and this correlation is absent in other subtypes, and (iii) there are only small structural deviations, despite low sequence similarity. The xCDxCDx-PHD constitutes ∼20% of the PHD family, and the double PHD fingers (DPFs) are 10% of the total number of xCDxCDx-PHDs. This subtype originated early in the evolution of eukaryotes but has diversified within the metazoan lineage. Despite sequence diversification, the positions of the enriched nonpolar residues, in particular, show very small structural deviations, suggesting critical contributions of nonpolar residues in the binding mechanism of this subtype. Using mutagenesis, we probed the contributions of the binding-site positions enriched in nonpolar residues in four xCDxCDx-PHD proteins and found that they contribute to the tight packing of the H3 residues. This effect may potentially be exploited, as we observed affinity enhancement upon substituting a bulky nonpolar residue at the same binding site in another histone reader. Overall, we present a detailed characterization of PHD subtypes.

Double cross finger flaps from the middle to the index or ring finger: A case series of 4 patients with an emphasis on donor finger morbidity

IntroductionThe use of two cross finger flaps from one digit has not been previously reported and the technique raises concerns regarding donor finger morbidity. In this paper, the authors report on a case series of double cross fingers flaps harvested from the middle finger to reconstruct large defects in the adjacent index or ring finger; with an emphasis on documenting morbidity in the donor middle finger. MethodsA total of four cases of double cross finger flaps were retrospectively reviewed. Demographic data, surgery, and postoperative complications were documented. Donor middle finger morbidity (stiffness, painful neuromas, skin graft instability, cold intolerance and cosmetic concerns) were also documented. ResultsAll patients were young male industrial workers. Two patients underwent reconstruction with de-epithelialized cross finger flaps and the other two patients had classic cross finger flaps. No postoperative complications were noted. Mild stiffness at the distal interphalangeal joints were noted in all patients. There were no painful neuromas and one donor site had occasional blistering at the site of the skin graft. Mild cold intolerance was seen in the two patients with electric burns. Hyperpigmentation of the skin grafts was noted in all patients. ConclusionsOur paper introduces to the literature the technique of utilizing two cross finger flaps from the middle finger to reconstruct large dorsal or volar defects of the adjacent index or ring finger. The study shows that the technique is feasible and is easily executed. The results document an acceptable donor finger morbidity.

Hand finger gesture modeling in advanced CAD system

ABSTRACTThe current paper presents a system for the dynamic simulation of the human hand. The simulation of the human hand offers the capability to acquire handshapes that correspond to letters of the finger alphabet, enabling an integrated representation of words and sentences. The hand model is designed using the Autodesk InventorTM and Autodesk AutoCadTM design environments. The user is able to type words or sentences which are dynamically translated into postures according to the finger alphabet. The system is based on the physiometric characteristics of an average human hand. High precision design is utilized in every part through integration of all the necessary functionalities needed to perform the movements required. The system has been tested on more than 500 words with a letter representation success rate in the range of 95–97%.

Finger gesture input utilizing the rear camera of a mobile phone: A perspective of mobile CAD

AbstractVarious interfaces have been suggested for mobile devices, including touch gesture and embedded sensor‐based interfaces. However, if a user's task requires a thorough look, these interfaces hinder sight of view in display and thus can be inappropriate for the tasks. This problem is more important especially in the mobile computer aided design (CAD) context, which performs visually demanding tasks on the limited screen. To address this point, this study suggests a mobile interface utilizing the rear camera of the device and describes the function mapping for CAD. The suggested interface detects finger attachment and movement direction. The number of colors in camera vision and local binary pattern are used as features, and a support vector machine (SVM) is used for feature classification. A prototype application is designed for validation, and appropriate SVM models are selected through benchmarking tests. The validation results show that the suggested interface can perform with high accuracy and low computational resources.

An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal.

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results.

Independent Bayesian classifier combination based sign language recognition using facial expression

Automatic Sign Language Recognition (SLR) systems are usually designed by means of recognizing hand and finger gestures. However, facial expressions play an important role to represent the emotional states during sign language communication, has not yet been analyzed to its fullest potential in SLR systems. A SLR system is incomplete without the signer’s facial expressions corresponding to the sign gesture. In this paper, we present a novel multimodal framework for SLR system by incorporating facial expression with sign gesture using two different sensors, namely Leap motion and Kinect. Sign gestures are recorded using Leap motion and simultaneously a Kinect is used to capture the facial data of the signer. We have collected a dataset of 51 dynamic sign word gestures. The recognition is performed using Hidden Markov Model (HMM). Next, we have applied Independent Bayesian Classification Combination (IBCC) approach to combine the decision of different modalities for improving recognition performance. Our analysis shows promising results with recognition rates of 96.05% and 94.27% for single and double hand gestures, respectively. The proposed multimodal framework achieves 1.84% and 2.60% gains as compared to uni-modal framework on single and double hand gestures, respectively.