A touchless passive infrared gesture sensor

In this paper we have implemented a wireless vision based mobile robot control through hand gesture recognition based on perceptual color space such as HIS, HSV/HSB, HSL. Vision-based hand gesture recognition is an important problem in the field of human-computer interaction, since hand motions and gestures could potentially be used to interact with computers in more natural ways. The robot control was done purely based on the orientation histograms a simple and fast algorithm on the system which would recognize static hand gestures with HSV color spaces as major parameters. The wireless based mobile robot system using hand gestures is a new innovative user interface that resolves the complications of using numerous remote controls for various applications. Based on one unified set of hand gestures, this system interprets the user hand gestures into pre-defined commands to control the remote robot. The experimental results are very encouraging as the system produces real-time responses and highly accurate recognition towards various gestures under different lighting conditions.

This paper talks about the significance of uniformity in the Nritta Hastas of Kathak dance across gharanas and eras. Kathak, an Indian classical dance form, hails from North India. Nritta Hastas refer to hand gestures or movements used in Indian classical dance. Often interchanged with” “hasta mudra”, “Nritta Hastas” encompass all hand movements and gestures, whereas “mudras” are more indicative of emotions and literal depictions of objects present in our environment.Kathak has evolved to appeal and cater to the spectators of today’s times - with lightning fast spins and hand movements along with intricate footwork. However, this has caused unfavorable negotiations with the original technique and overall uniformity of Nritta Hastas, thus resulting in the deterioration of quality in dance performances. For example, prioritizing the ability to execute multiple spins in quick succession, aiming to impress the audience with their speed and agility, leads to neglecting the intricate hand movements and mudras, which are essential for conveying the nuanced emotions and storytelling aspects of the dance. The purpose of this research paper is to shed light on the importance of maintaining uniformity by adhering to techniques, methods and pedagogies mentioned in ancient scriptures pertinent to Indian classical dance.

The online version contains supplementary material available at 10.1007/s00521-022-08090-8.

Gesture Mouse is a real-time hand gesture recognition system that uses deep convolutional neural networks to mimic mouse functionalities for controlling computers. The system requires only a standard webcam, and it can detect and recognize various hand movements and gestures, such as clicking, scrolling, zooming, movements, and hovering. The aim of this project is to improve and ease the human-computer interaction, especially for users who are physically challenged or have limited mobility. The Gesture Mouse system consists of three main components: hand detection, hand landmark extraction, and gesture recognition. The hand detection component is based on the Single Shot Multi Box Detector (SSD) model, which is used to detect the hand region in each frame of the webcam stream. The hand landmark extraction component uses a convolutional neural network (CNN) to predict the 3D coordinates of 21 key hand landmarks, including fingertips, palm canter, and wrist. The gesture recognition component is based on a CNN classifier that is trained on a large dataset of hand gesture images. The system performs well in real-time and can be used to control a computer with a high degree of accuracy and reliability. The proposed system has the potential to provide a more natural and intuitive way of interacting with computers, especially for individuals with motor impairments and also in technologies such as Virtual Reality and Augmented Reality.

In this paper we propose a way to accomplish Human Computer Interface absolutely in electronic way (without mechanical sensors). The idea is to extirpate old techniques of controlling Robotic arm using joysticks, buttons and supersede with more intuitive technique ie., to control robotic arm by hand motion or gesture. Here we propound an approach to achieve the aforementioned idea employing Image processing technique using web camera. We detect the vital features of hand: fingers by computational geometry calculation enabling real time interaction between hand gestures and Robot. Our system can meticulously locate fingers even when fore-arm is involved. And the system can sustain a certain rotation of palm and fore-arm, which augments the freedom of use in palm center estimation.

It is extremely difficult for people who are deaf to communicate their message to normal people. Communication becomes extremely difficult in case of an emergency, while travelling or among unfamiliar people because most people are not trained in hand sign language. We propose a voice aid system that allows deaf persons to communicate with regular people by using hand motions and gestures. This system uses motion and flex sensors, as well as a speaker unit. The circuitry that runs this system is fuelled by batteries. The system includes various pre-programmed messages such as "need help," "where is the washroom," and other common phrases that enable deaf individuals communicate basic information. The technology analyses a person's hand motions for different variants. It also has a trigger sensor to provide the impression that the person wants to activate the device and say something.

Pyroelectric infrared detectors have been of interest for many years because of their wide wavelength response, good sensitivity and lack of need for cooling. They have achieved a wide market acceptance for such applications as people sensing, IR spectrometry (especially for environmental protection) and flame/fire protection. Arrays of such detectors, comprising a pyroelectric material interfaced to an application specific integrated circuit for signal amplification and read out, provide an attractive solution to the problem of collecting spatial information on the IR distribution in a scene. Over the past 15 years, this has been particularly explored for uncooled thermal imaging. However, pyroelectric arrays have the property that they are only sensitive to changes in the IR flux. This means that they are particularly-well suited to the monitoring of movements of people in applications such as retail outlets and in safety and healthcare applications. The applications of low cost arrays with limited (few hundred elements) for people sensing and imaging radiometry will be illustrated. The performances and costs of uncooled pyroelectric arrays are ultimately driven by the materials used. For this reason, continuous improvements in materials technology are important. In the area of bulk ceramics, it is possible to obtain significant improvements in both production costs and performance though the use of tape-cast, functionally-gradient materials. The use of directly-deposited ferroelectric thin films on silicon ASIC's is offering considerable potential for low cost high performance pyroelectric arrays. The challenges involved in developing such materials will be discussed, especially from the aspect of low temperature deposition and other fabrication issues, such as patterning. Sol gel deposition provides an excellent technique for thin film growth and will be illustrated with an example of a Mn-doped PZT film that can be grown at 560°C with a FoM FD exceeding that of many bulk materials. The ability to grow such high-performance thin films onto silicon brings with it the ability to fabricate novel MEMS-based pyroelectric device structures. A new concept that will be presented here is the use of arrays of thin film pyroelectric detector elements with integrated radiation collectors designed to greatly enhance the intensity of the radiation falling on the element. The collectors' design is based on the Compound Parabolic Concentrator. These are non-imaging collectors, originally developed for solar cells technology in the 1960's, that yield a concentration ratio near to the thermodynamic maximum. The CPC structure is etched in a silicon wafer, upon which has been defined a pyroelectric IR sensor with low thermal conductance (spiral leg structure) fabricated in a high sensitivity PZT thin film. To our knowledge, this is the first time that CPCs have been applied to microfabricated IR sensors. First experimental assessment of the performance of these structures will be presented. (11 pages)

Decoding finger and hand movements from sEMG electrodes placed on the forearm of transradial amputees has been commonly studied by many research groups. A few recent studies have shown an interesting phenomenon: simple correlations between distal phantom finger, hand and wrist voluntary movements and muscle activity in the residual upper arm in transhumeral amputees, i.e., of muscle groups that, prior to amputation, had no physical effect on the concerned hand and wrist joints. In this study, we are going further into the exploration of this phenomenon by setting up an evaluation study of phantom finger, hand, wrist and elbow (if present) movement classification based on the analysis of surface electromyographic (sEMG) signals measured by multiple electrodes placed on the residual upper arm of five transhumeral amputees with a controllable phantom limb who did not undergo any reinnervation surgery. We showed that with a state-of-the-art classification architecture, it is possible to correctly classify phantom limb activity (up to 14 movements) with a rather important average success (over 80% if considering basic sets of six hand, wrist and elbow movements) and to use this pattern recognition output to give online control of a device (here a graphical interface) to these transhumeral amputees. Beyond changing the way the phantom limb condition is apprehended by both patients and clinicians, such results could pave the road towards a new control approach for transhumeral amputated patients with a voluntary controllable phantom limb. This could ease and extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.

This paper describes a miniature, wearable wireless hand- gesture recognition (HGR) system called HANDIO. It obtains its input from not only traditional inertial sensors but also a muscle tension sensor (MTS). The addition of MTS enables recognition of a much broader range of intuitive hand gestures, particularly those involving the wrist, that would otherwise be difficult to distinguish by traditional inertial-only HGRs. Among MTSs, we choose an optical MTS over the conventional surface electromyography(sEMG) for the small size, low power consumption, wearing comfort, and good detection rate. This novel miniaturized design enables the whole system to be easily patched on the wrist area or integrated into a wearable device such as a wristband or watch without extra wiring. Experimental results show that a total of 8 hand gestures involving the wrist can be recognized with a detection rate over 93%. The average power consumption of the optical sensor is only around 258μW. This versatile system can also be used to detect other joint activities such as the elbow and knee joint.

Hand gesture acquisition refers to the process whereby a computer is able to detect and record human hand movements. Vision-based hand gesture detection and a data glove-based approach can do this. In the realm of vision-based methods, we find both static hand and real-time hand gesture detection approaches. Five flex sensors are sewn onto the glove for the data glove method. Compared to the data glove-based method, the vision-based method is more consistent and robust. Designing a user-friendly vision-based interface, however, is a formidable challenge. The collected hand motions will need to be manually categorised by the user once again. So, it's no surprise that deciphering data is a laborious process that eats up plenty of time. This analysis should ideally be done mechanically. Relating the electrical nature of the human nervous system to computers presents a significant challenge in the field of Human Computer Interaction (HCI). Electromyographic (EMG) impulses, which change depending on the sort of hand motion utilised, are used to establish a brain link with computers. Because of this, an EMG-based system for recognising hand gestures can be created. The goal of this research is to create signal processing methods and Artificial Neural Networks (ANN) that can automatically recognise hand motions. The recorded Electromyographic (EMG) signals for a wide variety of hand motions are analysed using signal processing techniques, from which a number of features are extracted. Characteristics are extracted using Wavelet Transform (WT) based techniques including Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT). Better performance was achieved with the recommended methods, as shown by the simulation results.

A planar multi target sputtering approach was used to deposit PbTiO3 (PT) and Pb(Zr, Ti)O3 (PZT) films on TiO2/Pt bottom electrodes for the use in thin film pyroelectric IR detector arrays. PZT films with a Zr content of 28 at% (PZ28T) exhibited the best pyroelectric coefficient of typically 2×10−4 Cm−2K−1. The PZ28T films have been used for fabricating a two dimensional 11×6 pixel pyroelectric detector array on Si wafers. The array pixels with a sensitive area of 280 ×280 μm2 have a noise lequivalent power NEP of less than 0.7 nW at 1 Hz. It is planned to use the detector array in systems for motion detection.

Hand gestures aid the user in unspoken communication and help interpret sign language. Modern nonverbal communication detection employs gesture recognition systems and allows us to recognize shapes and signals made by hand movements using numerous machine learning techniques. Gestures have a very high value in human-computer interaction and sign language detection systems. To implement the gesture recognition and detection the existing methodologies rely on the application of bodily sensors. Due to this, the effectiveness in communication becomes challenging and cumbersome. Hence targeting on the major fallacies of the existing approaches, the primary aspiration of this research work is to recognize hand gestures in real-time using depth sensors that support natural communication avoiding on body sensor dependency. A novel approach is presented to detect hand gestures using minimalistic training and the efficiency of other algorithms concerning this proposed technique has been compared. Experimental investigations on the proposed methodology have successfully confirmed that, the research exertion has obtained an overall gesture detection accuracy of 90.86% and a high precision of 98.25%.

In this paper, a multimodal hand gesture detection and recognition system using differential Pyroelectric Infrared (PIR) sensors and a regular camera is described. Any movement within the viewing range of the differential PIR sensors are first detected by the sensors and then checked if it is due to a hand gesture or not by video analysis. If the movement is due to a hand, one-dimensional continuous-time signals extracted from the PIR sensors are used to classify/recognize the hand movements in real-time. Classification of different hand gestures by using the differential PIR sensors is carried out by a new winner-take-all (WTA) hash based recognition method. Jaccard distance is used to compare the WTA hash codes extracted from 1-D differential infrared sensor signals. It is experimentally shown that the multimodal system achieves higher recognition rates than the system based on only the on/off decisions of the analog circuitry of the PIR sensors(1).

One of the non-verbal communication techniques utilized in sign language is the hand gesture. Deaf persons who struggle with speech or hearing most frequently use it to converse with each other and hearing people. Several makers around the world have created various sign language systems, but they are neither adaptable nor cost-effective for end users. The majority of programs only provide text- based results. So, we are going to offer a system prototype in an application that solves the aforementioned issue. Hand gestures can be translated into text and then spoken by the system. Several algorithms will be used by the system, including CNN for classifying hand movements and SVM for detection of hand movement & TTS for conversion of Text To Speech.

Individuals with speaking disabilities, particularly people suffering from dysarthria, often use a TTS synthesizer for speech communication. Since users always have to type sound symbols and the synthesizer reads them out in a monotonous style, the use of the current synthesizers usually renders real-time operation and lively communication difficult. This is why dysarthric users often fail to control the flow of conversation. In this paper, we propose a novel speech generation framework which makes use of hand gestures as input. People usually use tongue gesture transitions for speech generation but we develop a special glove, by wearing which, speech sounds are generated from hand gesture transitions. For development, GMM-based voice conversion techniques (mapping techniques) are applied to estimate a mapping function between a space of hand gestures and another space of speech sounds. In this paper, as an initial trial, a mapping between hand gestures and Japanese vowel sounds is estimated so that topological features of the selected gestures in a feature space and those of the five Japanese vowels in a cepstrum space are equalized. Experiments show that the special glove can generate good Japanese vowel transitions with voluntary control of duration and articulation. Index Terms: Dysarthria, speech production, hand motions, media conversion, arrangement of gestures and vowels