Flex Sensors Research Articles

Virtual Texture Generated using Elastomeric Conductive Block Copolymer in Wireless Multimodal Haptic Glove.

Haptic devices are in general more adept at mimicking the bulk properties of materials than they are at mimicking the surface properties. This paper describes a haptic glove capable of producing sensations reminiscent of three types of near-surface properties: hardness, temperature, and roughness. To accomplish this mixed mode of stimulation, three types of haptic actuators were combined: vibrotactile motors, thermoelectric devices, and electrotactile electrodes made from a stretchable conductive polymer synthesized in our laboratory. This polymer consisted of a stretchable polyanion which served as a scaffold for the polymerization of poly(3,4-ethylenedioxythiophene) (PEDOT). The scaffold was synthesized using controlled radical polymerization to afford material of low dispersity, relatively high conductivity (0.1 S cm-1), and low impedance relative to metals. The glove was equipped with flex sensors to make it possible to control a robotic hand and a hand in virtual reality (VR). In psychophysical experiments, human participants were able to discern combinations of electrotactile, vibrotactile, and thermal stimulation in VR. Participants trained to associate these sensations with roughness, hardness, and temperature had an overall accuracy of 98%, while untrained participants had an accuracy of 85%. Sensations could similarly be conveyed using a robotic hand equipped with sensors for pressure and temperature.

A proposed soft pneumatic actuator control based on angle estimation from data-driven model.

This article proposes a bending angle controller for soft pneumatic actuators, which could be implemented in soft robotic rehabilitation gloves to assist patients with hand impairment, such as stroke survivors. A data-driven model is used to estimate the angle as pneumatic pressure is applied to the actuator. Furthermore, a finite element model was used to manually optimize the dimensions of the actuator. An embedded flex sensor, which together with a custom testing rig, was used to gather input data for the data-driven model. This rig contains a pneumatic pressure control circuit as well as a camera for image acquisition. Collected data were fed into a linear regression model to predict the data-driven model. Experiments were carried out to validate model's accuracy as well as modified proportional-integral-derivative controller angle controller performance. The latter controller is designed to mitigate the non-linear response of solenoid valves at different pressures of the actuator. The data-driven model along with the used controller allows more accurate estimation and quicker response.

Electric Spark Induced Instantaneous and Selective Reduction of Graphene Oxide on Textile for Wearable Electronics.

Reduced graphene oxide (rGO) attracts great popularity as an alternative to pristine graphene because of the facile synthesis process of its precursor, graphene oxide (GO). Electrical conduction of GO is tunable, subject to the extent of reduction of oxygen functional groups in it. This work for the first time demonstrates rapid reduction of GO using spark at ambient conditions. A stream of spark generated by applying high electric potential across two electrodes, when passed through a film of GO deposited on a porous substrate, reduces it into rGO. Upon sparking, the electrical resistance of the GO film drops down by an order of six within a second, making the reduction process instantaneous. X-ray photoelectron spectroscopy and Raman spectra of spark-reduced graphene oxide (SrGO) films revealed a high C/O ratio with an increase in the domain of sp2-hybridized carbon. The electromechanical properties of SrGO were practically examined by testing it as a flex sensor by incorporating its films with commercially available gloves. It showed high sensitivity for bending and good repeatability while offering an easy route for textile integration, making an impactful statement about the potential of sparking as a cost-effective method to reduce GO on a large scale.

Low power glove for hand functioning analysis in children with cerebral palsy

In this paper, a low-cost glove has been manufactured to monitor and analyse the hand motion for the children who suffer from the cerebral palsy. Cerebral palsy (CP) is a combination of continual disorders affect the movement’s evolution due to a non-gradual disturbance in developing fetal or infant cerebrum. An Arduino Nano microcontroller with flex and force sensors are attached to soft cloth glove to form the analysis glove. The data of this study is collected from children who have cerebral palsy, non-cerebral palsy, and children who are treating by physiotherapy and then compared with each other. The results show that the analysis glove helps the physiotherapist to assess the hand functioning problem such as difficulty in hand grip and inability to fully bend the hand figures in general and thumb figure in particular. These remarks can help physiotherapists to define the required program to improve these functions and indications.

Characterization of the fine hand movement in badminton by a smart glove

In a badminton game, dexterous fine hand movements are essential for the player to perform with extraordinary skill. Thus, this paper proposes a smart glove with proper sensing functions for finger pressures and bending angles for this sport to measure the kinematics and kinetics during the forehand grip and backhand grip. Twenty-one pressure sensors and eleven flex sensors are employed to construct the smart glove, and the measured parameters are then visualized to analyze different grip movements. An athlete and an amateur were selected for the gripping experiments and the measurements was recorded by the smart glove. During forehand gripping, although the total power generated by the amateur (33 N) was higher than for the athlete (29 N), the final shot strength was still unsatisfactory due to the disorderly arranged finger flex angle. In backhand gripping, the incorrectly laid index finger reduced the thumb power of the amateur to 3 N, which weakened the control ability and led to a distorted flying trajectory of the shuttlecock. The differences in kinematics and kinetics between the athlete and the amateur can provide a reference for the badminton learners to perform the correct technical movements. By use of this approach, the proposed system may grant trainers and trainees a better approach to analyze the grip movement.

Design and development of soft robotic hand for vertical farming in spacecraft

<p>For colonization in deep space we need to explore the feasibility of a bioregenerative system in microgravity or artificial gravity environments. The process has various complexities form ranging to biological obstacles to engineering limitations of the spacecraft. Concentration of microbes in the confinements of a spacecraft can be fatal for the crew. In this paper, a solution to the elevated microbial levels by farming using robots is discussed. The soft robotic arm is made up of Asymmetric Flexible Pneumatic Actuator (AFPA). The AFPA under internal pressure will curve in the direction of the side having greater thickness as the expansion of the thinner side (outside radius) will be more than thicker side (inside radius) due to differential expansion and moment induced due to eccentricity. Simulation results demonstrate that bending based on AFPA can meet the designed requirement of application. The AFPA is used for five fingers of the robotic hand. The safe, soft touch and gentle motion of the bellow (AFPA) gives the feel of real human hand. The internal pressure of the AFPA is controlled using a solenoid valve which is interfaced using an Arduino microcontroller for hand like moves. The bending of the fingers and degree of freedom (DOF) of the joints of the hand is controlled using an IMU and flex sensor. Wireless connection of the hand and the control system is implemented using XBee pro 60mW with a range of 1 miles. The pneumatic soft robotic hand is made up of solenoid valve, Mini Compressor, AFPA bellow, and Servos. This soft robotic hand has many advantages such as good adaptability, simple structure, small size, high flexibility and less energy loss. As an extension Manual control of the robot using a virtual reality environment and well as some possible aspects of an automated farming systems can be considered as future additions.</p>

Generating an Audio and Text for Indian Sign Language

Every normal people have the ability to observe and respond to the environment. Nearby are several unlucky persons who doesn’t encompass this principal blessing. Deaf and Dumb people have to communicate with normal people, it’s a challenging task. Sign Language is designed for deaf and dump people to convey message to the society. Sign Language Recognition is a technology which is used to identify human gestures with the help of Arduino uno. Gesture recognition based on the corresponding gesture symbol produces an audio & text output. The flex sensor is used to recognize the hand gestures. Recognized gesture is transferred to the Arduino uno and the output is generated. By using these techniques we can implement an efficient audio & text output for physically challenged people. Our technology consists of glove which will be worn by dumb people to communicate with normal people. The proposed prototype could also serve as an intermediate between deaf and dump people and normal people in everyday life such as bank or post office. The output will be displayed using LCD and also produces a speech signal.

Flex Sensor Based Glove to Control Wheel Chair and Sign Language Translator for Speech Impaired People

The idea of this paper is proposed for rehabilitation of people who have mobility issues particularly for the paralytic Stroke Patients and mute person with difficulty to communicate in a society. To overcome these complications, this system is developed to control the wheelchair based on finger movement and it also provides the voice output. It consists of flex sensors to control the wheelchair and in another mode it uses flex sensor and voice module to provide audio output. Person can move the wheelchair in any direction by gesture. Gesture is a good medium to give command because it can be used in a noisy condition and so he/she can communicate in public places. The patients can control the wheelchair through hand gesture, so they can survey themselves for their basic needs and need not to depend on others to guide those patients. Thus, it is multipurpose hand gloves to control motion of wheelchair and also to convert hand gestures to speech.

Apnea Monitor based on Bluetooth with Android Interface

Apnea monitor is a device that is used to give a warning if there is stop breathing. Stop breathing while sleeping is one form of obstructive sleep apnea. This cessation of breath cannot be underestimated, this is related to the main risk factors for health implications and increased cardiovascular disease and sudden death. The purpose of this study is to design an apnea monitor with the Android interface. This device allows the users to get how many times sleep apnea happens while sleeping and got data to analysis before continuing with a more expensive and advanced sleep test. This device used a flex sensor to detect the respiration rate, the sensor placed on the abdomen or belly so it can measure expand and deflate while breathing. The microcontroller uses an Arduino chip called AT-Mega328. Bluetooth HC-05 used to send respiration data to Android, MIT app inventor used for the android programmer, and on the android, there are plotting of respiration value and when the device detected apnea so the android also gives a warning to the user. Based on the results of testing and measurement then compare with another device, the results of the average% error were 3.61%. This apnea monitor design is portable but there are needs some improvement by using another sensor for detected respiration and using a module other than Bluetooth.

Design and Implementation of Wireless Robotic Arm Model using Flex and Gyro Sensor

In this day and age there is an expanding need to make artificial arms for different inhuman situations where human communication is troublesome or inconceivable. They may include taking readings from a functioning spring of gushing lava to diffusing a bomb. Here we propose to assemble a mechanical arm constrained by characteristic human arm developments whose information is procured using accelerometers. For appropriate control instrument and to lessen the measure of commotion rolling in from the sensors, legitimate averaging calculation is utilized for smoothening the yield of the accelerometer. The advancement of this arm depends on Arduino ATmega328 and which will all be interfaced with one another utilizing sequential correspondence. The automated arm structured here has six positions like both ways revolution, arm open and close, 90 and 180 degree tendencies working at 433Hz and can pick 150gms of weight . At last, the structured arm model pictures alongside table containing particulars is featured in results that might be required to conquer the issue, for example, putting or picking risky articles or non-dangerous items that are far away from the client.

Development of wearable IoT-based front kicking's angle monitoring device

Kicking prowess is subjected to flexibility of leg. The coach is able to improve the athlete’s kicking competency if the leg flexibility is known. In this paper, a wearable leg flexibility-monitoring device using flex sensor and Internet of Things (IoT) platform has been successfully developed and tested. The Arduino NodeMCU microcontroller will process and convert the input received from the flex sensor and transmit the real time kicking angle and corresponding resistance data to the two main outputs; the ThingSpeak IoT platform and the LCD display for real monitoring. Thirty participants were recruited from two different backgrounds; silat athletes (n=20) and normal participants (n=10). The participants were distributed into six weight categories; (50-55 kg), (55-60 kg), (60-65 kg), (65-70 kg), (70-75 kg) and (75-80 kg). Based on the average angle measured from three trials, it can be observed that different participant presents different average and standard deviation of leg flexibility independently of weight category. Moreover, the background factor of the subjects involved did not greatly contribute in this research as the participant from normal background has the highest mean leg flexibility (73.89 ± 17.41°). This situation is probably due to no standard of kicking styles that had been set for all participants at the beginning of the experiment. Nonetheless, one conclusive remark that can be deduced from the findings is the flexibility of an individual is greatly influenced by body weight since (75-80 kg) weight category has achieved the lowest mean angle of kicking for both backgrounds; normal (14.00± 1.33°) and silat athlete (23.89± 6.44°) subjects. In the future, additional sensors such as accelerometer can be used to predict the stability of the body for better evaluation of leg flexibility.

Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

This paper introduces an adaptable, human-computer interaction method to control multi-rotor aerial vehicles in unsupervised, multi-subject environments. A region-based convolutional neural network (R-CNN) first detects subjects in a frame and their faces’ regions of interest (RoIs), which are then fed to a facial recognition module to search for the main subject within the frame. The R-CNN model supplies the right-hand RoI of the main subject to a convolutional neural network (CNN) that classifies the right-hand gesture. A motion processing unit (MPU) and four flex sensors are embedded in the left hand’s smart glove to produce discrete and continuous signals. Those signals are generated based on the bending of left-hand fingers and the left hand’s roll angle and then fed to a support vector machine (SVM) to classify the left-hand gesture. Three validation layers have been implemented, including a human-based validation, classification validation, and the system validation. The comprehensive experimental results have validated the proposed method.

Flexible freestanding all-MXene hybrid films with enhanced capacitive performance for powering a flex sensor

Different few-layer MXene nanosheets are combined to fabricate freestanding all-MXene hybrid films with high gravimetric capacitance and excellent rate performance.

Hand Gesture Recognition using Deep Feature Fusion Network based on Wearable Sensors

Hand gesture recognition is an important way for human machine interaction, and it is widely used in many areas, such as health care, smart home, virtual reality as well as other areas. While many valuable efforts have been made, it still lacks efficient ways to capture fine grain hand gesture as well as track data of long distance dependency in complex gesture. In this paper, we firstly design a novel data glove with two arm rings and a specially integrated three-dimensional flex sensor to capture fine grain motion from full arm and all knuckles. Secondly, an improved deep feature fusion network is proposed to detect long distance dependency in complex hand gestures. In order to track detailed motion features, a convolutional neural network based feature fusion strategy is given to fuse data from multi-sensors by extracting both shallow and deep features. Moreover, a residual module is introduced to avoid over fitting and gradient vanishing during deepening the neural network. Thirdly, a long short term memory (LSTM) model with fused feature vector as input is introduced to classify complex hand motions into corresponding categories. Results of comprehensive experiments demonstrate that our work performs better than related algorithms, especially in America sign language (with the precise of 99.93%) and Chinese sign language (with the precise of 96.1%).

Flat-Feet Prediction Based on a Designed Wearable Sensing Shoe and a PCA-Based Deep Neural Network Model

Gait is a significant factor that affects human health, and monitoring a person's gait with sensing devices during daily life can detect abnormal gait events that affect numerous physical health problems. In particular, flat feet can cause changes in alignment conditions of the foot, ankle, leg, pelvis and spine. The primary problem with previous studies of wearable devices for measuring gait have focused on quantitatively monitoring the degree of gait rather than the limited gait ability. The existing method of feeding back the degree of gait or activity does not consider the severity of the subject and is insufficient for qualitative evaluation or training of gait. The significance of this study is development of convenient detecting and long-term tracking tools that can be used by both patients and clinicians for prescreening flat feet and monitoring the progress of flat feet treatment. For wearable devices for flatfoot detection to be most effective, detection systems and algorithms must be accurate, robust, reliable and computationally-efficient. In this paper, we developed an integrated smart wearable gait-monitoring device comprised of three sensors: front force, rear force, and an ankle flex sensor. We propose a new flat feet detection methodology based on a dynamic sensing window and a deep neural network with scaled principal component analysis (PCA). We tested 24 subjects, including both those with healthy gait and flat-feet-affected gait. Our study shows that the proposed sensing devices could be worn comfortably. The proposed deep neural network (DNN) model outperformed the other five classifier algorithms considered, and the area under the curve (AUC) value of the method was 87.1%. This wearable device can thus be easily and simply used both by patients and doctors to monitor the progress of flat feet and prescreen for possible gait problems in daily life.

Development of 4 Axis Shadow Robot Arm Integrated With Flex Sensor Mechanism

Development of 4 Axis Shadow Robot Arm Integrated With Flex Sensor Mechanism

A Prototype of Flex Sensor Based Data Gloves to Track the Movements of Fingers

In this paper, we propose a flex sensor-based data glove to track the movements of human fingers for virtual reality education. By putting flex sensors and utilizing an accelerometer, this data glove allows people to enjoy applications for virtual reality (VR) or augmented reality (AR). With the maximum and minimum values of the flex sensor at each finger joint, it determines an angle corresponding to the bending value of the flex sensor. It tracks the movements of fingers and hand gestures with respect to the angle values at finger joints. In order to prove the effectiveness of the proposed data glove, we implemented a VR classroom application.

Data Analysis and Accuracy Evaluation of a Continuous Glucose-Monitoring Device

This study was aimed at analyzing data and evaluating the accuracy of a new subcutaneous continuous glucose-monitoring device by referring to finger-pricking measurement. The data were obtained from 7 diabetic patients. An improved implanted flex sensor was used to measure the interstitial glucose concentration every 3 min within 6 days, and five finger-pricking samples were collected every day for comparison. A periodic glucose change happened every day. 2.45% of CGM values were in the hypoglycemic range (<70 mg/dl), 55.4% were in the normal range (70-180 mg/dl), and 42.15% were in the hyperglycemic range (>180 mg/dl). The interstitial glucose concentrations (n=204) were well linearly correlated with the capillary glucose concentrations (r=0.94, P<0.01), but a delay occurred between 10 and 40 minutes within two measurements (CGM later), and the individual average delay times were relatively close to 24.6 minutes. Clarke’s error grid analysis showed that 86.7% of the points fell in zone A, 12.7% fell in zone B, and only 0.6% fell in zone D. An overall MARD was 10.22%. This study demonstrated that the CGM was accurate and highly reliable; thus, it constituted a new device for continuous glucose monitoring in diabetic patients.

Integrated Earthquake and Landslide Monitoring Over Wireless Sensor Network

Natural disasters have mercilessly devastated our lives in so many different terms. The impact of Earthquake and landslides are very severe because of their unpredictability. To alleviate the problem early warning system plays an important role. In this paper, we proposed an integrated earthquake and landslide monitoring system overthewireless sensor network. The Wireless Sensor Network is programmed to acquire the data, which is monitored and controlled centrally or independently and can be distributed widely in a random or planned deployment. By detecting suspicious indications such as tremor or landslides through sensor nodes, the system provides information to the monitoring and warning station. The processing unit is composed of arduino and Sensors such as 4.5 Flex Sensor, Capacitive Soil Moisture Sensor and a3-Dimensional Accelerometer are used in the proposed system to monitor landmass displacement/movement, the moisture level of the soil and the vibrations for earthquakes respectively. An RF module, XBee S2C is used which provides wireless communication with a range of about a 1 km line of sight and 60 meters indoor. The real-time data can be accessed via the internet. It will be powered from a 12V 20W solar panel with rechargeable Li-Ion Battery and fitted with necessary protection and charging circuit boards. The proposed system can be used in earthquake and landslide prone area to avoid damages to life and property in the area of operation by providing crucial information and further warning for any disastrous development. The proposed system is implemented through experiments and proved to be effective.

Retracted] Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed, 3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are used coherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus, the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require additional lift to provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper.