Design and Development of IoT Device that Recognizes Hand Gestures using Sensors

A hand gesture is a non-verbal means of communication involving the motion of fingers to convey information. Hand gestures are used in sign language and are a way of communication for deaf and mute people and also implemented to control devices too. The purpose of gesture recognition in devices has always been providing the gap between the physical world and the digital world. The way humans interact among themselves with the digital world could be implemented via gestures using algorithms. Gestures can be tracked using gyroscope, accelerometers, and more as well. So, in this project we aim to provide an electronic method for hand gesture recognition that is cost-effective, this system makes use of flex sensors, ESP32 board. A flex sensor works on the principle of change in the internal resistance to detect the angle made by the user’s finger at any given time. The flexes made by hand in different combinations amount to a gesture and this gesture can be converted into signals or as a text display on the screen. A smart glove is designed which is equipped with custom-made flex sensors that detect the gestures and convert them to text and an ESP32 board, the component used to supplement the gestures detected by a flex sensor. This helps in identifying machines the human sign language and perform the task or identify a word through hand gestures and respond according to it.

Design and Performance Evaluation of a Hybrid Hand Exoskeleton for Hand Opening/Closing

Finger extensor muscle weakness and flexor hypertonia are the most commonly reported issues among patients suffering from amyotrophic lateral sclerosis (ALS). Moreover, the relative hyperflexion of the wrist and the fingers has limited their ability to open the hand and interact with the external environment voluntarily. In this work, a hybrid hand exoskeleton is developed to prevent the relative hyperflexion of the fingers and wrist and facilitate the users in their functional hand opening by compensating the flexor hypertonia. This exoskeleton, combining a passive device with the soft extra muscle (SEM) glove, assists users in normal hand opening/closing required for some basic activities of daily living. The paper presents kinematic and static models of passive hand exoskeleton design. Moreover, the proposed design is tested and evaluated by comparing the volunteer hand opening with the exoskeleton assistance using the flex sensors attached on the dorsal side of the middle finger, ring finger, and thumb with both healthy subjects and patients.

Design of grip strength measuring system using FSR and flex sensors using SVM algorithm

This paper proposes a design of a complete system to identify weak grip strength that is caused by multiple factors like ageing, diseases, or accidents. This paper presents a grip measurement system that comprises of force sensing resistor and flex sensor to evaluate the condition of the hand. The system is tested by gripping a pencil and a cylindrical object using the glove, to determine the condition of the hand. Force sensitive resistor (FSR) evaluates the force applied by the different parts of the palm on the object being grasped. Flex sensor evaluates the bending of the fingers and thumb. The data from the sensors is then compared with existing data to evaluate the state of the hand. The data from the sensors is stored on the personal computer (PC) through serial communication. A model is trained using the data from the sensors, which determine if the grip strength of the user is weak or strong. The model is also trained to differentiate between two modes that are pen mode and object mode. The model achieved an accuracy of 90.8 percent using support vector machine (SVM) algorithm. This glove can be deployed in medical centers to assist in grip strength measurement.

Smart Hand Glove for Hearing and Speech Impaired

With the advancement of technology, we can implement a variety of ideas to serve mankind in numerous ways. Inspired by this, we have developed a smart hand glove system which will be able to help the people having hearing and speech disabilities. In the world of sound, for those without it, sign language is a powerful tool to make their voices heard. The American Sign Language (ASL) is the most frequently used sign language in the world, with some differences depending on the nation. We created a wearable wireless gesture decoder module in this project that can transform the basic set of ASL motions into alphabets and sentences. Our project utilizes a glove that houses a series of flex sensors on the metacarpal and interphalange joints of the fingers to detect the bending of fingers, through piezoresistive (change in electrical resistance when the semiconductor or metal is subjected to mechanical strain) effect. The glove is attached with an accelerometer as well, that helps to detect the hand movements. Simple classification algorithms from machine learning are then applied to translate the gestures into alphabets or words. Keywords: Arduino; MPU6050; Flex sensor; Machine learning; SVM classifier

Speaking System for Mute Peoples

This interacting device is a microcontroller based system which is basically outline for lessening the communication space between dumb and normal people. This system can be accordingly configured to work as a “smart device”. In this paper, Atmega 328 microcontroller, voice module, LCD display and flex sensors are utilize. The device considered is basically residing of a glove and a microcontroller based system. Data gloves are used to detect the hand motion and microcontroller based system will interpret those few manoeuvre into human place voice. The data glove is furnished with four flex sensors placed on the glove. This system is helpful for dumb people and their hand manoeuvre will be converted into speech signal because of the date glove worn on the hands.

Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications

Technology plays a vital role in patient rehabilitation, improving the quality of life of an individual. The increase in functional independence of disabled individuals requires adaptive and commercially available solutions. The use of sensor-based technology helps patients and therapeutic practices beyond traditional therapy. Adapting skeletal tracking technology could automate exercise tracking, records, and feedback for patient motivation and clinical treatment interventions and planning. In this paper, an exoskeleton was designed and subsequently developed for patients who are suffering from monoparesis in the upper extremities. The exoskeleton was developed according to the dimensions of a patient using a 3D scanner, and then fabricated with a 3D printer; the mechanism for the movement of the hand is a tendon flexion mechanism with servo motor actuators controlled by an ATMega2560 microcontroller. The exoskeleton was used for force augmentation of the patient’s hand by taking the input from the hand via flex sensors, and assisted the patient in closing, opening, grasping, and picking up objects, and it was also able to perform certain exercises for the rehabilitation of the patient. The exoskeleton is portable, reliable, durable, intuitive, and easy to install and use at any time.

Indonesian Sign Language Interpreter Device Based on ATMega328 Microcontroller for Bali Deaf Community Denpasar

This study aims to design and build a Sign Language Interpreter Device with Voice Output in the form of an ATMega328 Microcontroller-Based Voice Speaker Module so that in its implementation and later in designing this device the writer focuses on the translation of 16 words that have been predetermined in Indonesian Sign Language especially in Denpasar City by using a Flex Sensor and a Gyro Sensor based on the ATMega328 Microcontroller with Arduino IDE programming. This device is also equipped with a 4GB SD card memory for storing voice recordings, using an ATMega328 microcontroller, four analog Flex sensors, a Gyro sensor, a buzzer and an 8 ohm speaker, and using a 7.4 volt Li-Po battery. The application of this device is aimed for thehearing impaired people who fall into the adult category who can understand writing and understand sign language. The output of this device uses an MP3 player module that is already included in the Sign Language Interpreter Device. The flex sensor readings range from 998-1005 ADC (analog digital converter) in open conditions and the sensor ranges from 1006-10018 ADC in closed conditions. The reading for the gyro pitch (Y axis) ranges from -10º to 76º then on the reading of the gyro Roll (X axis) ranges from -100º to 90º. Keywords: ATMega328 microcontroller; Buzzer; Flex Sensor; Gyro Sensor

Design of Effective Smart Communication System for Impaired People

In communication medium, sharing a conversation dialogue between the normal person and deaf and dumb person is one of the challenging tasks still. The dumb person can practice hand gesture language in their community but not to others. This research article focuses to minimize the difficulty level between these two communities with smart glove devices. Besides, the author believes that result of the proposed model provides a good impact on the dump community. The smart glove contains input, control, and output module to get, process, and display the data respectively. Our proposed model is used to help these communities to interact with each other continuously without any error. The proposed model is constructed with good specification flex sensors. Little change of resistance in flex sensor is providing changes in their gesture language. So this orientation direction is calculated well and gives better results over existing methods. The wireless set can be made with Bluetooth technologies here. Here the gestures are assigned based on the alphabet letter. The sign language performs and gives audible output in the display section of the proposed model. It gives good results in our experimental setup. This research work focuses on good recognition rate, accuracy, and efficiency. The good recognition rate shows the continuous conversation between the two persons. Moreover, this research article compares the recognition rate, accuracy, and efficiency of the proposed model with an existing model.