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Several neuroimaging studies have shown the somatotopy of body part representations in primary somatosensory cortex (S1), but the functional hierarchy of distinct subregions in human S1 has not been adequately addressed. The current study investigates the functional hierarchy of cyto-architectonically distinct regions, Brodmann areas BA3, BA1, and BA2, in human S1. During functional MRI experiments, we presented participants with vibrotactile stimulation of the fingertips at three different vibration frequencies. Using population Receptive Field (pRF) modeling of the fMRI BOLD activity, we identified the hand region in S1 and the somatotopy of the fingertips. For each voxel, the pRF center indicates the finger that most effectively drives the BOLD signal, and the pRF size measures the spatial somatic pooling of fingertips. We find a systematic relationship of pRF sizes from lower-order areas to higher-order areas. Specifically, we found that pRF sizes are smallest in BA3, increase slightly towards BA1, and are largest in BA2, paralleling the increase in visual receptive field size as one ascends the visual hierarchy. Additionally, we find that the time-to-peak of the hemodynamic response in BA3 is roughly 0.5 s earlier compared to BA1 and BA2, further supporting the notion of a functional hierarchy of subregions in S1. These results were obtained during stimulation of different mechanoreceptors, suggesting that different afferent fibers leading up to S1 feed into the same cortical hierarchy.

Die erfolgreiche Therapie eines komplexen regionalen Schmerzsyndroms (CRPS) hangt im Wesentlichen von der fruhzeitigen Diagnosestellung und einem adaquaten Therapiekonzept mit aktiver Beteiligung des Patienten ab. Diagnostik und Therapieempfehlung des CRPS mit Lokalisation an der Hand. Darstellung und Recherche von Studien zur Behandlungsempfehlungen sowie der CRPS-Leitlinien. Die fruhzeitige Diagnose des CRPS, eine suffiziente Schmerztherapie und die an den Leitlinien des CRPS orientierte Behandlung begunstigen den erfolgreichen Behandlungsverlauf. „Graded motor imagery training“ ist v. a. in der Akutphase die Therapie der Wahl und wesentlich fur die Verbesserung der kortikalen Reprasentation, Schmerzreduktion und Reintegration ins Korperschema. Eine gute Patientenaufklarung bezuglich des Krankheitsbildes und Krankheitsverlaufes bildet die Basis fur die Therapie des CRPS. Der Patient sollte stets als Ganzes gesehen und entsprechend durch ein multiprofessionelles Team behandelt werden. Eine von Beginn an aktive Einbindung des Patienten in die Therapie ist immer erforderlich.

Recognition of Indian Sign Language (ISL) could bridge the gap between deaf-mute people and society. Hand recognition is a key requirement for ISL recognition system. In this paper, the hand region...

OBJECTIVE: Electrocorticography (ECoG) based Brain-Computer Interfaces (BCIs) can be used to restore communication in individuals with locked-in syndrome. In motor-based BCIs, the number of degrees-of-freedom, and thus the speed of the BCI, directly depends on the number of classes that can be discriminated from the neural activity in the sensorimotor cortex. When considering minimally invasive BCI implants, the size of the subdural ECoG implant must be minimized without compromising the number of degrees-of-freedom. APPROACH: Here we investigated if four hand gestures could be decoded using a single ECoG strip of four consecutive electrodes spaced 1 cm apart and compared the performance between a unipolar and a bipolar montage. For that we collected data of seven individuals with intractable epilepsy implanted with ECoG grids, covering the hand region of the sensorimotor cortex. Based on the implanted grids, we generated virtual ECoG strips and compared the decoding accuracy between 1) a single unipolar electrode (Unipolar Electrode), 2) a combination of 4 unipolar electrodes (Unipolar Strip), 3) a single bipolar pair (Bipolar Pair) and 4) a combination of 6 bipolar pairs (Bipolar Strip). MAIN RESULTS: We show that four hand gestures can be equally well decoded using ‘Unipolar Strips’ (mean 67.4 ± 11.7%), ‘Bipolar Strips’ (mean 66.6 ± 12.1%) and ‘Bipolar Pairs’ (mean 67.6 ± 9.4%), while ‘Unipolar Electrodes’ (61.6 ± 5.9%) performed significantly worse compared to ‘Unipolar Strips’ and ‘Bipolar Pairs’. SIGNIFICANCE: We conclude that a single bipolar pair is a potential candidate for minimally invasive motor-based BCIs and encourage the use of ECoG as a robust and reliable BCI platform for multi-class movement decoding.

Converging evidence suggests that transcranial alternating current stimulation (tACS) may entrain endogenous neural oscillations to match the frequency and phase of the exogenously applied current and this entrainment may outlast the stimulation (although only for a few oscillatory cycles following the cessation of stimulation). However, observing entrainment in the electroencephalograph (EEG) during stimulation is extremely difficult due to the presence of complex tACS artifacts. The present study assessed entrainment to slow oscillatory (SO) tACS by measuring motor cortical excitability across different oscillatory phases during (i.e., online) and outlasting (i.e., offline) stimulation. 30 healthy participants received 60 trials of intermittent SO tACS (0.75 Hz; 16 s on/off interleaved) at an intensity of 2 mA peak-to-peak. Motor cortical excitability was assessed using transcranial magnetic stimulation (TMS) of the hand region of the primary motor cortex (M1HAND) to induce motor evoked potentials (MEPs) in the contralateral thumb. MEPs were acquired at four time-points within each trial – early online, late online, early offline, and late offline – as well as at the start and end of the overall stimulation period (to probe longer-lasting aftereffects of tACS). A significant increase in MEP amplitude was observed from pre- to post-tACS (paired-sample t-test; t29 = 2.64, P = 0.013, d = 0.48) and from the first to the last tACS block (t29 = −2.93, P = 0.02, d = 0.54). However, no phase-dependent modulation of excitability was observed. Therefore, although SO tACS had a facilitatory effect on motor cortical excitability that outlasted stimulation, there was no evidence supporting entrainment of endogenous oscillations as the underlying mechanism., Graphical Abstract Thirty healthy participants received 60 trials of intermittent SO (0.75 Hz) tACS (1 trial = 16 s on + 16 s off) at an intensity of 2 mA. Motor cortical excitability was assessed using TMS-induced MEPs (blue waveforms) acquired across different oscillatory phases during (i.e., online; red arrows) and outlasting (i.e., offline; green arrows) tACS, as well as at the start and end of the stimulation session (blue arrows). Mean MEP amplitude increased by ∼41% from pre- to post-tACS (P = 0.013); however, MEP amplitudes were not modulated with respect to the tACS phase.

The number of wheelchair patients is increasing every day all over the world. Safe navigation and independent mobility are key issues for wheelchair users especially those suffering from dexterity inhibiting disorders and mobility impairments. Following modern technologies, this paper designs a control system prototype to easily navigate the wheelchair by performing finger gestures rather than operating a traditional joystick. Hand or finger gesture recognition using deep Convolutional Neural Network (CNN) and interaction with a powered wheelchair are major features of this system. This system is focused more on the flexibilities of wheelchair users by ensuring less movement of the hand as well as the cost of the implementation. To achieve these goals, some necessary steps have been performed, i.e., locating and continuously tracking the hand region, feeding the CNN model using Raspberry pi for gesture recognition, and perform navigation. After testing, our proposed system has achieved the recognition accuracy of 98.33% and a response time of 34 ms with a Raspberry Pi 4 device (1.5 GHz of CPU frequency), which is suitable for real-time wheelchair control application with reasonably low-cost.

Developing a multi-hand gesture to create a magical mouse movement by which we can tab, swipe, zoom-in, zoom-out by using one or more fingers. The machine learning technique can be used to perform all the operations as a state of art. The objective of the proposed algorithm is to use the hand gesture in front of the built-in webcam in the computers or the cellphones which can control the mouse movement. A multilayer convolution neural network is proposed in this paper which considers the pixel-level segmentation to identify the hand region, in the second stage, it identifies the number of fingers used, and in the last stage, the movement detection of the fingers to identify the operations is to be performed.

AIMS: Professional ballet dancers are at risk for degenerative knee cartilage changes. In the current study, we evaluated the knee cartilage with T2 mapping methods in professional ballet dancers and healthy controls and investigated possible differences of T2 values between these groups. METHODS: We included healthy dancers with 5−20 years of professional ballet dancing experience and sex-matched healthy controls without knee pathology. T2 values of the knee cartilage were measured from axial, coronal, and sagittal images. The values were measured by free hand region of interest (ROI). RESULTS: The study population consisted of 44 people (22 dancers, 22 controls), with mean age of 25.57 ± 5.53 yrs. We found no significant relationship between sex and T2 values. We detected a significant positive correlation between age and T2 values for patellofemoral joint cartilage. T2 values of patellofemoral and tibiofemoral joints of dancers were significantly higher. Mean T2 values of patellofemoral joint were around 32 in all planes in dancers and around 12 in controls (p

Recently the analysis of egocentric videos is one of the hot topics in computer vision. In this paper, we mainly focus on cooking activities recognition in egocentric videos. To recognize cooking activities automatically and precisely, we need to solve the problems how to detect hand region in egocentric videos, how to represent hand motion and how to classify the cooking activities. In this research, to solve these problems, we propose a new cooking activities recognition method in egocentric videos. The characteristic points of our proposed method are 1) hand regions are accurately detected in cluttered background by exploiting color, texture and location information, 2) temporal hand features are extracted from sequential frame images with a thinning algorithm, 3) fully-connected multi layer neural network is utilized to recognize cooking activities with the extracted features. Towards fine-grained cooking activities recognition, we have investigated the performance of our proposed method with our benchmark including 12 fine-grained cooking activities in 5 coarse categories of them. From the experimental results, we have confirmed that our proposed method allows us to recognize cooking activities with 45.2%.

This paper proposes a method to enter characters and/or character string in an augmented reality using a gesture motion. The proposed method detects the region of character string using the gesture motion. It consists of five phases; template generation, skin color detection, hand region detection, gesture motion extraction and designation of character string region. The template image consists of two fingers because a gesture is to take hold the tips of the first and second fingers. In the skin color detection, we extract the skin color on the basis of values in saturation by using threshold processing. The hand region is detected by calculating areas and detecting the area with the maximum value as a hand. The gesture motion is extracted using template matching. In order to show the effectiveness of the proposed method, we conduct experiments for character string specification.

Objective To evaluate the effect of a laparoscopic instrument with a 360° rotatable handle piece (rot-HP) on biomechanical stress and precision in different areas of a simulated operating field at two working heights. Background Surgeons performing laparoscopic procedures are exposed to biomechanical stress and have an increased risk of musculoskeletal complaints. Method Fifty-seven healthy subjects (27 men, median age 26) without experience in laparoscopy performed a precision task in four quadrants (A–D) of the operating field using the rot-HP or a common fixed handle piece (fixed-HP) at an individually adjusted lower or higher working height. Biomechanical stress was assessed by surface EMG, wrist joint angles, and arm postures and precision by the number of mistakes. Results Using the rot-HP reduced muscle activity of the biceps brachii and flexor carpi radialis muscle. An interaction of flexor activity and area of the operating field occurred with the lowest activity in Quadrant C. Wrist joint angles were more neutral using the rot-HP, especially when the lower working height was applied and in Quadrants B and C. However, increased wrist dorsal flexion occurred in Quadrant A while using the rot-HP. Arm postures and precision were less affected. Conclusion The rot-HP allows some reductions of stresses in the arm and hand region, whereas the stress in the shoulder neck region is not modified. Application The instrument’s position and the working height may have to be considered as mediatory factors when describing the effectiveness of an ergonomic handle design for laparoscopic instruments.

Ganglion cysts are common cystic lesions filled with a jelly-like substance and originate from a joint capsule or tendon sheath through a stalk. Ganglion cysts mostly occur in the hand region, for which surgical excision usually results in good outcomes. In contrast, toe ganglions are relatively rare, and surgical treatment is associated with a high recurrence rate because of unidentifiable ganglion stalks, requiring large skin incisions. We have treated toe ganglion cysts using endoscopy in the ganglion cyst by injecting methylene blue into the tendon sheath that connects to the ganglion stalk. The result has been favorable, without recurrence. The advantages of our technique include the following: (1) Endoscopy using a color aid can show the location of a ganglion stalk, and removing the stalk can prevent recurrence. (2) Endoscopic stalk-only resection is minimally invasive, allowing early mobilization and reducing surgical-site complications. The purpose of this Technical Note is to describe our endoscopic stalk resection technique with color-aided visualization, and we have included a video presentation.

According to the important principle of plastic surgery “replace like-with like”, a successful soft tissue reconstruction requires autologous tissue transfer. Lipofilling is a frequently used technique in esthetic and reconstructive surgery. In the hand region, one well-known indication of its esthetic application is the lipofilling of the dorsum of the hand. There are limited literature references regarding lipofilling for reconstruction and scar correction in the pulp of the digits. Insufficient fat tissue in the digital pulp can lead to discomfort and reduced finger employment, as the pulp is an important functional area in everyday activities. We present two cases related to trauma sequela, managed with fat grafting for digital pulp reconstruction. Level of evidence: Level V, therapeutic study.

Introduction Winter pruning is a cultivation practice necessary for maintaining the balance between the vegetative and the productive activity of plants and requires many working days using hand scissors. This operation involves the subjects carrying out a series of gestures that are repeated with considerable frequency, which are all musculo-skeletal disorders risk factors (MSDs) for the hand-wrist area. Objective The aim of this study was to investigate the forces applied to pruning tools. Material and methods Using a sensor matrix, peak and average forces were measured which are exerted while cutting branches of 3 different diameters, from 5 wine-grape cultivars. Samples were tested on 8 participants using sensored scissors to record, in 6 hand areas, the forces necessary to cut. Results Results showed that while cutting, the factors which can impact the force employed (peak and average forces) by the subjects are branch diameter and percentage of branch humidity. Cut duration was inversely related to the size of the subject's hand. The middle finger area of the hand recorded the highest force average and peak levels, while the hand region least affected during the cuts was the farthest from the thumb. Conclusions The study enabled the highlighting of which factors influence the forces employed by the operator while cutting grape branches, and to identify the hand regions where muscle activation is at its most. These findings can be relevant in preventing MSDs. Further studies need to be conducted with a larger number of subjects.

We propose an algorithm for automatically recognizing some certain amount of gestures from hand movements to help deaf and dumb and hard hearing people. Hand gesture recognition is quite a challenging problem in its form. We have considered a fixed set of manual commands and a specific environment, and develop a effective, procedure for gesture recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the gesture which in general terms means detecting, tracking and recognising. The algorithm is non-changing to rotations, translations and scale of the hand. We will be demonstrating the effectiveness of the technique on real imagery.

Orthotic brace design and manufacturing can be problematic for people who have physical dimensions in the 5 th or 95 th percentile regions, or for people with unique chronic conditions. Advanced manufacturing tools provide opportunities to realize specialty braces. The goal in this research paper is to investigate solutions for finger and hand brace design and fabrication. Parametric design approaches are taken, and select solutions are presented, which have been fabricated using material extrusion processes. Research in this area is ongoing as many practical issues have been raised throughout this project.

We have developed the wearable system for photographing of the scenery/composition designated by the user’s hand frame with a shoulder-mounted camera. The hand frame is the gesture of making a rectangular enclosure with both hands when the user considers the composition of a picture. The system detects the hand region of the user from an image of a head-mounted camera, and gets a “picking region image” by recognizing the hand gesture. The picking region is the region in the hand frame indicated by the user through the image of the head-mounted camera. It photographs high resolution image of the similar composition as the picking region image, called “target region image” by controlling PTZ (pan/tilt/zoom) of the shoulder-mounted camera. It performs robust control on noise such as the user’s body sway.

Gait is one of the biological information which indicates how to walk. Gait is able to acquire from a distance and any views. It is, however, difficult to identify a person with belongings. The purpose of this paper is, therefore, to propose an effective identification method when the subject has a bag. First, GEI(Gait Energy Image) is generated with head position adjustment. Then, gradient of GEI for head, both arms and leg regions are obtained. Finally, features are obtained from each region except for the hand region with a bag and the bag itself in the leg region. The proposed method obtains 95.0% identification ratio for back view gait with belongings.

Intracortical microstimulation (ICMS) is one of the most widely employed techniques for providing causal evidence of the relationship between neuronal activity and specific motor, perceptual, or even cognitive functions. In recent years, several new types of linear multielectrode silicon probes have been developed, allowing researchers to sample neuronal activity at different depths along the same cortical site simultaneously and with high spatial precision. Nevertheless, silicon multielectrode probes have been rarely employed for ICMS studies and, more importantly, it is unknown whether and to what extent they can be used for combined recording and stimulation experiments. Here, we addressed these issues during both acute and chronic conditions. First, we compared the behavioral outcomes of ICMS delivered to the hand region of a monkey’s motor cortex with multielectrode silicon probes, commercially available multisite stainless-steel probes and single-tip glass-coated tungsten microelectrodes. The results for all three of the probes were reliable and similar. Furthermore, we tested the impact of long-train ICMS delivered through chronically implanted silicon probes at different time intervals, from 1 to 198 days after ICMS sessions, showing that although the number of recorded neurons decreased over time, in line with previous studies, ICMS did not alter silicon probes’ recording capabilities. These findings indicate that in ICMS experiments, the performance of linear multielectrode silicon probes is comparable to that of both single-tip and multielectrode stainless-steel probes, suggesting that the silicon probes can be successfully used for combined recording and stimulation studies in chronic conditions.

The paper proposes a novel gesture recognition and finger tip detection algorithm for Human Computer Interaction, in particular mouse control operations using real time camera. The hand gestures are captured using real time camera. First, the hand region alone is segmented using region growing algorithm followed by morphological operations. The centroid of the palm region is calculated and the finger tips are then detected using the convex hull algorithm. The proposed method is tested on five different gestures and the results prove that the gestures are able to be recognized and the finger tips detected. The method can be applied for hand gesture controlled mouse operations.

Gesture recognition, although has been exploring for many years, is still a challenging problem. Complex background, camera angles and illumination conditions make the problem more difficult. Thus, this paper presents a fast and robust method for hand gesture recognition based on RGB video. First we detect the skin based on their color. Then we extract the contour and segment the hand region. Finally we recognize the gesture. The results of experiment demonstrate that the proposed method are efficient to recognize gesture with a higher accuracy than the state of the art.

Recognizing static hand gesture in complex backgrounds is a challenging task. This paper presents a static hand gesture recognition system using both color and depth information. Firstly, the hand region is extracted from complex background based on depth segmentation and skin-color model. The Moore-Neighbor tracing algorithm is then used to obtain hand gesture contour. The k-curvature method is used to locate fingertips and determine the number of fingers, then the angle between fingers are generated as features. The appearance-based features are integrated to the decision tree model for hand gesture recognition. Experiments have been conducted on two gesture recognition datasets. Experimental results show that the proposed method achieves a high recognition accuracy and strong robustness.

This paper proposes a method to input characters and/or character string in an augmented reality using gesture motion. The proposed method detects the region of character string using gesture motion. It consists of five phases; template generation, skin detection, hand region detection, gesture motion extraction and designation of character string region. The template image consists of two fingers because a gesture is to take hold the tips of the first and second fingers. In the skin detection, we extract the skin color on the basis of values in saturation by using threshold processing. The hand region is detected by calculating areas and detecting the area with the maximum value as a hand. The gesture motion is extracted using template matching. In order to show the effectiveness of the proposed method, we conduct experiments.

Egocentric videos, which mainly record the activities carried out by the users of wearable cameras, have drawn much research attention in recent years. Due to its lengthy content, a large number of ego-related applications have been developed to abstract the captured videos. As the users are accustomed to interacting with the target objects using their own hands, while their hands usually appear within their visual fields during the interaction, an egocentric hand detection step is involved in tasks like gesture recognition, action recognition, and social interaction understanding. In this work, we propose a dynamic region-growing approach for hand region detection in egocentric videos, by jointly considering hand-related motion and egocentric cues. We first determine seed regions that most likely belong to the hand, by analyzing the motion patterns across successive frames. The hand regions can then be located by extending from the seed regions, according to the scores computed for the adjacent superpixels. These scores are derived from four egocentric cues: contrast, location, position consistency, and appearance continuity. We discuss how to apply the proposed method in real-life scenarios, where multiple hands irregularly appear and disappear from the videos. Experimental results on public datasets show that the proposed method achieves superior performance compared with the state-of-the-art methods, especially in complicated scenarios.

This paper proposes a sign language translation system for communication among deaf and common people. It uses an algorithm about hand detection and analysis from previous studies and project assignments. The algorithm performs five steps within the limitations, which is the key that it measures a center of gravity with 4 vertexes from sign language images and sifts the latent features from calculated distance, angle for 5 determined points of hand region through specific experiments. Applying the features, success rate is improved from 23% to 100%. The system can make use of development for human-robot interface.

Background Despite the broad popularity of a numeric rating scale (NRS) its psychometric properties are not well known. The objective was to determine if there is any difference in the discrimination ability of the NRS when used for measuring pain severity separately in different body regions. Methods Cross-sectional survey study of 630 professional musicians. Item Response Theory (IRT) was used to define the psychometric properties of the NRS. Results The discrimination ability of the pain NRS was dependent on the body area to which it was applied. The discrimination was low 0.5 (95% CI 0.4. to 0.7) for the hand region and perfect for the shoulder and upper part of the neck– 3.2 (95% CI 1.2 to 5.2) and 10.5 (95% CI 10.0 to 10.9), respectively. Both shoulder and neck NRSs showed a great shift towards higher levels of pain severity meaning that the ability of the NRS to discriminate low levels of pain is poor. NRS scores obtained from all other regions did not demonstrate any discrimination ability. Conclusions The pain NRS might have different psychometric properties depending on the body area to which it is applied. Overall, the modest discrimination ability of the pain NRS implies that it should be used in screening questionnaires with some reservations.

Nowadays, hand detection and gestures recognition have become very popular in human computer interaction systems. Several methods of hand detection based on wrist localization have been proposed but the majority work only with short sleeves and they are not efficient in front of all the challenges. Hand detection based on wrist localization proposed by Grzejszczak et al. (Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013 439–449, 2013), Nelpa et al. (Man Mach Interact 3(242):123–130, 2014) [3, 4] use the property of local minima along the contour of the skin mask obtained in the first stage to detect the wrist position. Although this technique provides good results where the skin mask contains the hand and the forearm, it still sensitive to the short contour where the skin mask contains the hand region only which generate false detection of the hand. We present in this paper an assessment of this method where the skin mask contains the hand region only. The main idea is based on the 2D shape properties of the hand and its components. Using 134 color images with their ground- truth, we evaluated the method enhanced and the results obtained were very satisfactory compared to the original one.

In this paper, we introduce a two-stage Hand Posture Recognition system using a Kinect sensor. In the first stage, we propose an effective algorithm to segment hand region from complex background, which incorporates both color, depth and skeleton information, without specific requirements on uniform-colored or stable background. It is robust with skin-color noise like arms and face, and can handle both-hands gesture. In the second stage, we use a standard Nearest Neighbor to recognize the posture. Experiments shows that the proposed system achieves high recognition accuracy.

In this paper, we proposed a dynamic hand gesture recognition system by using only the depth information. The proposed system can recognize twelve different dynamic hand gestures, including swipes, scales, push, wave, rotates, circle, and drag. First, the background subtraction is used to remove the unnecessary information, and the depth information of main user can be obtained. Furthermore, hand position can be tracked, and the region of hand is extracted as an adaptive square. Once the region of hand is obtained, the hand parameters are obtained by calculating the depth information of hand region. The proposed system can recognize dynamic hand gesture by using the hand parameters. In the experiment, the performance of the proposed system is verified by two different people at 2 different depths, and both right and left hands are verified. The experimental result show that the proposed system can recognize the dynamic hand gestures with an average recognition rate of 90.08%.

We introduce a novel approach to cultural heritage experience: by means of ego-vision embedded devices we develop a system, which offers a more natural and entertaining way of accessing museum knowledge. Our method is based on distributed self-gesture and artwork recognition, and does not need fixed cameras nor radio-frequency identifications sensors. We propose the use of dense trajectories sampled around the hand region to perform self-gesture recognition, understanding the way a user naturally interacts with an artwork, and demonstrate that our approach can benefit from distributed training. We test our algorithms on publicly available data sets and we extend our experiments to both virtual and real museum scenarios, where our method shows robustness when challenged with real-world data. Furthermore, we run an extensive performance analysis on our ARM-based wearable device.

A communication tool in the form of sign language is required for deaf and dump persons as there is no oral communication possible between them. They perform the conversion of sign languages into voice/text. Recently, many algorithms are developed for this purpose. An Indian Sign Language Recognition (ISLR) system is presented in this paper. It uses curvelet transform based entropy features for the recognition, and the transform is applied only to the segmented hand region. Then, the features of each sign of English alphabets are modelled by a classier network called Hidden Markov Models (HMM). The system gives an average accuracy of 82.95% using 3rd level features which can help to reduce the communication gap between deaf-dumb and normal people in the world.