Flex Sensors Research Articles

Rancang Bangun Purwarupa Alat Pencitraan Gerak Model Gestur Berbasis Mikrokontroler Arduino

In the era of rapidly developing technology, the integration between body and technology becomes real, especially in the development of human and computer interaction. This research focuses on creating prototypes of motion imaging tools using microcontroller technology, responding to the need for more intuitive interactive tools in translating human movements. Using hardware and software engineering approaches, this study designed and tested a tool consisting of Gyrometer and Flex sensors. The gyrometer measures the orientation of motion, while the Flex sensor detects the curvature of the gesture. Tests include calibration between sensors, integration with the HC-05 Bluetooth transmission module as a wireless communication medium, and applications installed on smartphones as processing output information . The study introduced a method of gesture imaging of gestures, providing a solution to interact with computers more naturally. The urgency of this research answers the need to help translate human movement into digital input. The modeled tool is able to detect hand gestures with good accuracy. The smartphone as an output successfully displays information in the form of text and voice based on detected movements. This gesture model motion imaging tool successfully passed the testing stage, showing the effective results of the Gyrometer and Flex sensors in detecting and translating hand movements. The Bluetooth module as a data transmitter runs well sending data to an application on the smartphone, which then displays the information accurately.

Smart Glove for Sign Language Translation

Sign language is a vital mode of communication for deaf people, yet it presents a significant barrier when interacting with those who do not understand it. The advent of technology has paved the way for innovative solutions to bridge this communication gap. This abstract explores the development and implications of a smart glove designed for sign language translation (SLT). The primary aim of this study is to create a wearable device, the Smart Glove, capable of recognizing and translating sign language gestures into text or speech. Key objectives include designing a lightweight and ergonomic glove prototype, developing machine learning algorithms for sign language recognition, implementing real-time translation capabilities, evaluating the glove's accuracy and usability, and assessing the potential impact on facilitating communication for deaf people. The Smart Glove utilizes only one sensor, flex sensors, to capture hand movements and gestures. These data inputs are processed through a custom-built machine learning model trained on a comprehensive sign language dataset. Preliminary results indicate a high accuracy rate in recognizing sign language gestures, with an average recognition rate of over 90% across a diverse set of signs. While challenges such as expanding gesture recognition and refining translation algorithms remain, this technology offers a promising solution to break down communication barriers and enhance the quality of life for those who rely on sign language.

Design a Sign Language Translator Using Flexible Sensors

In terms of communication skills, many of us have limitations and shortcomings or what we are more familiar with is speech impairment Speaking is the ability to pronounce articulated sounds or words to express, express and convey thoughts, ideas and feelings. Communication skills can include many ways, including using verbal skills, namely verbally and non-verbally. In Indonesia, there are two sign languages used, namely Indonesian sign language (BISINDO) and Indonesian sign system (SIBI). BISINDO is a sign language that appears naturally in Indonesian culture and is practical for use in everyday life so that BISINDO has several variations in each region. The flex sensor has a thin and densely curved shape so that the flex sensor can be used as a motion detection and finger curve. Flex sensor application for human movement detection, patient monitoring. Therefore, hand-to-letter/text sign language translators using flexible sensors is a very important problem today. The method carried out is system design using tools and components used in research. This tool using the working principle in this system is to translate the sign language of alphabetic letters using flexible sensors. Design sign language translation of alphabetic letters using flexible sensors. The test to display the letters of the alphabet A-Z has a total minimum and maximum resistance at a flexible session of 1000 ohms with a voltage of 5V each. The result of this data is that there is no error in the play because the range value does not violate each other with other range values

Design and Implementation of Gesture Recognition System Based on Flex Sensors

Design and Implementation of Gesture Recognition System Based on Flex Sensors

Posture Guardian With Smart Muscle Strain Detection And Correction Using TINYML


 
 
 
 Millions of dollars are spent in the healthcare business on muscle strain-related illnesses, notably low back pain. Inadequate posture is typically the cause of lower back discomfort, especially in the transverse abdominal muscle. Intelligent monitoring systems encourage proper posture and exercise on a global scale to improve quality of life and treat this frequent issue. As pervasive intelligent technology emerges, medical equipment such as pulse monitors, hearing aids, and health monitors are being developed. This research discusses the design, development, and testing of a tiny wearable interface that uses an array of sensors to monitor and notify a person's back position in real time. A good posture helps to alleviate muscle stress during physical exertion and promotes general health. As a result, a gadget capable of detecting and correcting improper posture has become important. We created a wearable electromyography (EMG) sensor and flex sensor. EMG and flex sensors are used in our technology to monitor human posture and decide if it is right or improper. TinyML (Tiny Machine Learning) is used by our technology to assess and categorize posture data in real time, offering quick feedback and remedial help. Our TinyML-powered Smart Posture technology helps people improve their posture, reduce muscle discomfort, and improve their overall health and quality of life.
 
 
 

Sign Language to Speech Conversion

Abstract: A huge population in India alone is of the dumb and deaf people. So the system is working on a glove based device which will be used for conversion of sign language (ASL) to speech .The basic system consists of a two parts; sign language recognition and conversion to text and further to speech. The sign language glove consist of a simple hand gloves fitted with flex sensors which is being used for the monitoring the amount of bend on the fingers. Flex means bend, this is the sensors that change the resistance depending on the amount of bend on the sensor. Data from the sensors is send to the Control unit which is the Arduino Nano the analog signals from the sensors are digitally converted and compared with the stored value for the recognition of sign and then displayed as a text on the 16x2 LCD. Further the text output is wirelessly transmitted to a cellular phone or a PC which consist of a test to speech conversion software.

Wireless Animatronic Hand

As a result of employees utilizing their bare hands, there are more accidents in industries every year. Animatronic hands resemble human hands and are capable of carrying out a variety of tasks that humans are unable to do. Human hands cannot readily finish difficult tasks, but animatronic hands can mimic human movements and complete them. A number of actuators and sensors make up the robotic hand, which are managed by a person's hand. The pushers control how far the fingers can move. We employ flex sensors to detect the positions. The positions and motion of the hand are reported by sensors to Arduino. The microcontroller uses a 433MHz transmitter and receiver in both transmitter and receiver side to used for transmitting the data, so it will be access in remote area. We employ servo motors as actuators by getting the position and motion from another microcontroller also attached. Two microcontrollers are interconnected with 433MHz transmitter and receiver, Transmitter and receiver for 433 MHz When sending and receiving radio signals between any two devices, RF refers to a set of electronic RF transmitters and receiver modules. The transmitter module at the transmitter end transmits the data, which the receiver module at the receiver end receives.

Development and Testing of a Soft Exoskeleton Robotic Hand Training Device.

Hand-function recovery is often a goal for stroke survivors undergoing therapy. This work aimed to design, build, and verify a pneumatic hand training device for its eventual use in post-stroke rehabilitation. The system was built considering prior research in the field of robotic hand rehabilitation as well as specifications and design constraints developed with physiotherapists. The system contained pneumatic airbag actuators for the fingers and thumb of the hand, a set of flex, pressure, and flow sensors, and software and hardware controls. An experiment with the system was carried out on 30 healthy individuals. The sensor readings were analyzed for repeatability and reliability. Position sensors and an approximate biomechanical model of the index finger were used to estimate joint angles during operation. A survey was also issued to the users to evaluate their comfort levels with the device. It was found that the system was safe and comfortable when moving the fingers of the hand into an extension.

Glove-Based Classification of Hand Gestures for Arabic Sign Language Using Faster-CNN

Recently, American Sign Language has been widely researched to help disabled people to communicate with others. However; the Arabic Sign Language “ASL” has received much less attention. This paper has proposed a smart glove which has been designed using flex sensors to collect a dataset about hand gestures applying ASL. The dataset is composed of resistance and voltage measurements for the bending of the fingers to represent alpha-numeric characters. The measurements are manipulated using normalization and zero referencing methods to create the dataset. A Convolutional Neural Network ‘CNN’ composed of twenty-one layers is proposed. The dataset is used to train the CNN, and the Accuracy and Loss parameters are used to characterize its success. The dataset is classified with an average success rate of 95% based on the classification accuracy. Loss has decreased from 3 to less than 0.5. The proposed CNN layers have classified ASL characters with a reasonable degree of accuracy.

Sistem Screening Mandiri Covid-19 Berbasis Internet of Things (IoT)

The increasing need to perform Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) during the COVID-19 pandemic is time-consuming and costly. An IoT-based self-screening system of COVID-19, is expected to be a solution. In this study, instruments were created to monitor SpO2 and heart rate using the MAX30102 sensor, body temperature using the MLX90614 sensor, and breathing rate using a flex sensor as a COVID-19 detection parameter. The measured vital sign results are also transmitted to the website using the Wi-Fi module ESP32. The method used is a decision tree that has two classifications, indicated healthy and indicated COVID-19. Decision tree accuracy is 90% and recall is 100%. Temperature readings have a 98.68% accuracy, SpO2 readings and heartbeats are 98.74% and 98.64%, respectively, and breathing rate per minute readings are 100% accurate. This autonomous COVID-19 screening system can be used as an alternative solution for early detection of COVID-19.

The development of a wearable device for Sign Language Gesture Recognition (SLGR) by using Conceptual Design Approach (CDA)

In recent years, there has been a significant focus on researching and developing Sign Language Gesture Recognition (SLGR) for people with hearing and speaking impairments. This is especially important in Indonesia, where there are approximately two million people with these disabilities. However, current research and developments on SLGR devices are specifically designed to understand only particular sign language systems, such as Korean Standard Sign Language (KSDSL) and American Sign Language (ASL), each of which has its unique gestures and models. As a result, no device has been developed to recognize the gestures of the Indonesian sign language system, known as Sistem Isyarat Bahasa Indonesia (SIBI). Therefore, this study aims to develop an SLGR device that can recognize and translate SIBI gestures into output images via text and speech. The development of the SLGR device in this study is conducted by using the Conceptual Design Approach (CDA) methodology. Where in this case, previous research on SLGR devices is first observed as a benchmark for comparison. Furthermore, the benchmark is used as the basis for the function, sub-function, and specification of the proposed design. Based on these stages of benchmarking and concept development, the study concluded that the final design of the proposed SLGR device is constructed by using 5 flex and Gyroscope sensors connected wirelessly to the Raspberry microcontroller. The device is equipped with a voice system and LCR RPi as the output system for translation. Based on the combination of these sensors, the device is able to identify any particular gestures that correspond to words and phrases in SIBI and translate them into speech via the designated speaker and text display on the LCD screen. To fully understand the performance of the device, experimental tests are conducted by analyzing the input of 26 alphabets in the SIBI system. As a result, the device demonstrated an average of 92% accuracy to convert sign language into voice and text, which demonstrates the usefulness of the proposed device

A Prototype Robotics Wheelchair with An IOT Based Paralyzed Patient Health Care Monitoring System

This project presents an implementation of wearable, portable, low power consumption, real-time remote bio-signals monitoring system based on the internet of thing technology. This internet technology along with modern electronic devices could offer promising solutions in this field. Based on that, this project utilizes a web or mobile application as an IoT platform to monitor remotely the live pulse rate signal, BPM and the body temperature of patients. The signals are measured and processed by using a microcontroller-based device (Arduino). The main contribution of this paper is sending an electrocardiogram to a web or mobile phone to be watched by a doctor. If some abnormal conditions are detected, the IoT alert send to doctor and relations, This assists in heart diseases diagnosing before the worst case can happen. Finally, the obtained results of this project are illustrated on both smartphone and personal computer (PC) as well. Index Terms— Microcontroller, LCD, flex sensor, remote sensor, ultrasonic sensor ,temperature sensor , cloud connectivity, data processing ,IoT

A Smart Iot-Based Prototype System For Rehabilitation Monitoring

Smart healthcare is growing significantly in the healthcare sector due to the Internet of Things. A remote monitoring system is one of the smart healthcare implementations for rehabilitating stroke patients. Nowadays, as the COVID-19 pandemic continues tospread, patients undergoing home rehabilitation have difficulty meeting with their physicians due to movement constraints. In addition, the healthcare facilities are devoted to treating patients with COVID-19. As a result, physicians and patients could not frequently meet to gather their rehabilitation progress. This study involves developing a prototype to monitor a post-stroke patient's rehabilitation process using the Arduino Nano 33 Bluetooth Low Energy (BLE) and force-sensing resistor (FSR). The prototype analyzes critical aspects of the rehabilitation process based on handgrip, heart rate, sleep, and step tracking measurements. The results of the handgrip, heart rate, sleep, and step tracking measurements are evaluated for various types of subjects and six testing approaches showed an accurate and consistent results. However, experiments partially success with a small error is detected while tracking the steps of each subject. Several recommendations are highlighted to improve the prototype using othersensors such as force sensing resistor and flex sensor for handgrip force transducer, electromyogram (EMG) sensor for stroke-patients rehabilitation, and others.

Smart Glove Berbasis IoT dengan Output Teks dan Suara

Stroke in Indonesia was the second leading cause of death and third cause of disability in the world. In Indonesia, the proportion of disability adjusted life-years (DALYs) contribution from stroke is around 9.7% and is the number one cause of disability. People with disabilities will have difficulty doing daily activities, especially communication. Therefore it is necessary to develop communication aids that can help stroke sufferers communicate daily by utilizing palm movements. In this study, two flex sensors were placed on the fingers. Communication aids for stroke sufferers function to detect four basic movements, namely: "I want to eat", "I want to drink", "I want to urinate/poop",and “rest”. The four sentences are also displayed on a display and also on the Smartphone. The detection of these four movements is obtained by bending the two fingers which are detected using a flex sensor placed on a glove. This tool is expected to be the development of communication aids for stroke sufferers.

Flex Sensor and Machine Learning-Based Mute Communication System

Individuals facing speech impairments encounter significant communication challenges. To address this, we have developed a groundbreaking system that seamlessly translates custom hand gestures into written text and spoken language. Our system combines flex sensors and state-of-the-art machine learning techniques, empowering individuals with speech impairments to express themselves effectively and improve their overall communication experience. By connecting flex sensors to an Arduino board and utilizing MATLAB for robust data collection, analysis, and processing, we achieve a highly accurate gesture recognition system. Through meticulous steps of data labeling, rigorous training of a machine learning model, and thorough accuracy evaluations, our system recognizes a wide range of gestures, including numbers 0-9, letters A-Z, and 10 Kannada language gestures conveying meaningful phrases. The hand gesture recognition system achieved notable performance with an accuracy of 83.5%, precision of 88.6%, recall of 95.1%, and F1 Score of 91.7%. Key Words: speech impairments, hand gestures, gesture recognition, flex sensors,

Assistive robotic hand with bi-directional soft actuator for hand impaired patients.

Soft wearable robotic hand can assist with hand function for the performance of activities of daily living (ADL). However, existing robotic hands lack a mathematical way to quantify the grip force generated for better controlling the grasp of objects during the performance of ADL. To address this issue, this article presents a soft wearable robotic hand with active control of finger flexion and extension through an elastomeric-based bi-directional soft actuator. This actuator bends and extends by pneumatic actuation at lower air pressure, and a flex sensor embedded inside the actuator measures the angles of the fingers in real-time. Analytical models are established to quantify the kinematic and tip force for gripping of the actuator in terms of the relationship between the input pressure and the bending angle, as well as the output force, and are validated experimentally and by the finite element method. Furthermore, the ability of the soft robotic hand to grasp objects is validated with and without being worn on a human hand. The robotic hand facilitates hand opening and closing by the wearer and successfully assists with grasping objects with sufficient force for ADL-related tasks, and the grip force provided by the actuator is further estimated by the analytical models on two healthy subjects. Results suggest the possibility of the soft robotic hand in providing controllable grip strength in rehabilitation and ADL assistance.

Development electrically conductive PAAm/Alg/CNC/rGO/PANI hydrogel composites and investigation their bioelectronic properties

Characterizing the effects of parameters such as the swelling ratio, composition, and applied current frequency of hydrogels is crucial for the development of flexible, stretchable, and electrically conductive hydrogels that are of importance in a variety of biomedical applications. In this study, poly(Acrylamide) (PAAm), alginate (Alg) and crystalline nanocellulose (CNC) based stretchable PAAm/Alg/CNC/rGO hydrogels containing different amounts of reduced graphene oxide (rGO) in their structure were synthesized. To increase the electrical conductivity of these hydrogels, their composites with polyaniline (PANI) were prepared. The chemical composition and morphological characterizations were performed using FT-IR and SEM analysis techniques. Since the amount of PANI formed in the structure of hydrogel composites was directly proportional to the amount of rGO in the structure, swelling, mechanical and electrical conductivity properties changed depending on the amount of rGO. The swelling ratio and mechanical strength of the PAAm/Alg/CNC/rGO/PANI hydrogel composite series varied between 38 and 50 g water/ g polymer and 76.02–375.95 kPa, respectively. The electrical conductivities of their 25% swollen states at 10−4 MHz ranged from 15.4 to 20.20 S/m. Flex sensor, smart hydrogel fingers and electrocardiogram (ECG) electrode applications were tested. The synthesized hydrogel composite systems were quite successful in these biomedical applications as bioelectronic materials.

AUTOMATIC BELT CARDIOPULMONARY RECISUTATION (CPR) DEVICE AND TEST SYSTEM DESIGN

The test system was created by adding flex and force sensors to this manikin to evaluate the results of heart massage applied on an artificial adult manikin, which has a spring that can give the chest stiffness of an adult human. This test system monitors and analyzes cardiopulmonary resuscitation (CPR) applications performed by the automated CPR device or manually by the person. The signals received from the sensor are transferred to the computer via the serial port with the Arduino Uno card and displayed in real-time in MATLAB graphical user interface (GUI). This GUI, designed with Matlab 2021a software, analyzes the sensor signals resulting from CPR. It gives a graph of the repeats of compression per minute, the depth of each compression, and the compressions rate variable performed by the user or the automatic CPR device during the CPR application. This created test system can evaluate the accuracy of both the automatic CPR device performed in this study and the manual CPR application. The test system designed in this respect can be used in the training and evaluation of cardiac massage applications, which is included in the first aid courses in secondary education, associate degree, undergraduate, and vocational education courses.

Aerosol mediated localized dissolution to enhance the electrical behavior and sensitivity of piezoresistive nanofiber-based flexible sensors

This work proposes the use of solvents in the form of small size droplets to improve the connections among nanofibers (NFs) in electrospun composite nanofibers with carbon nanotube multiwalled (MWCNT) by improving the electrical and piezoresistive behavior of such electrically conductive polymer composites. The here proposed Aerosol Mediated Localized Dissolution (AMLD) process has been shown to be effective in improving the 3D microporous NF mat by inducing local dissolution that is effective in improving the connections among fibers within the mat. The AMLD process is demonstrated here for polyethylene oxide (PEO) / MWCNTs composite nanofibers, showing that the electrical conductivity is particularly improved in those samples with low content of MWCNTs, even below the original percolation threshold. The improved electrical conductivity is coupled with exceptional sensitivity of the flex sensor for low MWCNTs contents, this is particularly due to the ability of the AMLD process to preserve the high surface area of the 3D mat by inducing better fiber-to-fiber contacts in few regions only. In addition, this work demonstrates the effectiveness of applying an electrical potential difference during the AMLD process to improve the alignment of MWCNTs within the 3D microporous NF mat. The combination of voltage and AMLD allow to obtain a gauge factor as high as 571.9 with a MWCNTs loading of 1 wt%.

Converting South African sign language to verbal

There is a significant population of hearing-impaired people who reside in South Africa; however, South African Sign Language (SASL) has not yet been recognized as South Africa's 12 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> official language, resulting in slow uptake of this important language. Since most people do not know SASL, there is a need for gesture recognition systems that convert Sign Language (SL) to verbal and/or text to reduce the communication barriers between the hearing and the hearing-impaired. This study presents an application for gesture recognition in converting SASL to both a verbal format and a textual format. By using gesture recognition from a single wearable glove, hand gestures were quantified, categorized, and then converted into an auditory format and played on a speaker, as well as the equivalent textual information displayed on an LCD screen. The complete prototype consists of a wearable glove with a transmitter and an associated receiver box which were all designed to cost less than $150. The glove consists of five flex sensors that measure the handshape and an inertial measurement unit which measures the hand motion. The handshape and motion data are processed and wirelessly transmitted to a receiver box. This then displays the associated English character on an LCD while also playing the audio on a speaker. The SL converter can convert the 26 letters of the SASL manual alphabet with an overall accuracy of 69%, It can also convert common words and phrases, as well as proper names when fingerspelled.