Force Sensitive Resistors Research Articles

Design and Verification of an Automated Pressure Mapping System and Modulating Air Cells for Pressure Ulcer Prevention

This research focuses on developing an intelligent air mattress prototype based on commercially available alternating pressure air mattresses to prevent pressure ulcers. Force-sensitive resistors (FSRs) are installed under each air cell to measure interface pressure values occurring at different parts of the body. These collected values are then used to create pressure maps, indicating areas of high overpressure. The prototype incorporates a smart pneumatic control system divided into seven functional areas: head, back, waist, buttocks, thighs, calves, and heels. All operations are performed using LabVIEW software.

A triangle-shape region of interest in cardiorespiratory estimation during sleep monitoring

Abstract Introduction: With advancements in sensor and communication technologies, sleep monitoring is moving out of specialized clinics and into everyday homes. Extracting sleep-related data using far less complicated tools and procedures is possible than polysomnography. Respiratory and cardiovascular data are extracted from the signals such as the electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) to identify the aberrant respiratory events of apnea/hypopnea as well as to estimate sleep parameters. However, due to the different sleeping positions, such systems lack accuracy and/or complicated sensor network topology. In this work, we proposed an optimal topology of forcesensitive resistor (FSR) sensors to simplify the system design by identifying the region of interest for estimating cardiorespiratory parameters with minimal error. The sensors are deployed under the mattress and located on the bed frame. Methods: We proposed a low-cost, unobtrusive, non-invasive, and reliable solution with robust signal processing algorithms for cardiorespiratory measurements and automatic signal validation based on signal quality. The solution is established based on a multi-physical layer (MPL) and sensor interfaces coping with the embedded system’s specifications, and signal processing is performed onboard with two independent and simultaneous pipelines for heart rate and respiratory rate using discrete wavelet transform (DWT) and bandpass filter, respectively. Results: We identified the three most contributing FSR sensors forming a triangle shape covering the left upper side of the subject (in the supine position) as the region of interest. We reduced the mean absolute error (MAE) to as low as 3.94 and 2.35 for heart rate and respiratory rate. Conclusions: The approach with the topology of triangle-shaped performs appropriately in estimating the cardiorespiratory parameters in all four regular sleeping positions, i.e. supine, prone, left lateral, and right lateral.

TRANSITION MOTION PATTERN CLASSIFICATION FOR LOWER LIMB EXOSKELETON IN STAIR SCENES BASED ON CNN AND GRU

Motion pattern classification is one of the important research fields in lower extremity exoskeleton robot, it refers to acquiring motion data from multiple sensors installed on the exoskeleton. We designed a wearable lower limb exoskeleton robot with multiple sensors mainly including force sensitive resistors (FSRs) inside smart shoes and encoders inside joints. The wearable robot was used to help people carry the heavy load in the scenes of ascending stairs and descending stairs. The experiments of stair walking were carried out by the subjects who wore the exoskeleton to ascend stairs and descend stairs for a designated time. Before or after the stair walking, the subject would turn to move on flat ground with the result that there existed four transition motions between the stair and flat ground walking. As known, there is less research focusing on the classification of transition motions. The aim of this paper is to classify these motion patterns through a learning algorithm. The convolutional neural network (CNN) and gated recurrent unit (GRU) framework were combined to improve the classification accuracy. Specifically, CNN was used to extract the features of the motion pattern, while GRU was used to extract the temporal correlation during walking. Experimental works showed that the proposed CNN-GRU possessed a significantly high prediction accuracy in motion pattern classification. Compared with CNN, GRU and LSTM-CNN models whose accuracy score does not exceed 93.22%, the proposed CNN-GRU gained a high accuracy of 95.51%.

Predicting Elderly Falls Using the Roll Factor in the "Smart Walk Trainer" Equipment for Body Weight-Balance Analysis

The imbalanced posture of the body is a potential risk of accidents in all ages that can be caused by a variety of reasons, including muscle weakness, aging-related deterioration in balance, or neurological problems, possibly see more problem for the elderly. Knowing the weight-bearing condition would be helpful to improve balance of the body to prevent the risk. The “Smart Walk Trainer” equipment has been developed for analyzing the imbalance weight-bearing conditions of both standing and walking manner. It consists of "Force Sensitive Resistor" (FSR) sensors to measure the weight-bearing of the foot, the controlling and processing system of the NI USB-6009 multifunction I/O hardware, and LabVIEW software. The study using this equipment aims to examine the differences between young and elderly subjects in terms of weight-bearing and balance assessment. The result showed the roll_factor of the elderly deviated greatly from the balance point (Right: Mean = -0.72, SD = 1.4; Left: Mean=0.78, SD=1.41) when compared to the young subjects (Right: Mean = 0.01, SD = 0.56; Left: Mean=-0.19, SD = 0.49) which corresponded to the risk of falling in the elderly. The equipment also provides specialists with enhanced clarity regarding weight distribution patterns, enabling them to identify any abnormalities and then provide targeted exercise recommendations to strengthen specific muscles and promote improved balance for fall prevention.

Parametric Design and Prototyping of a Low-Power Planar Biped Robot.

This study proposes a design approach and the development of a low-power planar biped robot named YU-Bibot. The kinematic structure of the robot consists of six independently driven axes, and it weighs approximately 20 kg. Based on biomimetics, the robot dimensions were selected as the average anthropomorphic dimensions of the human lower extremities. The optimization of the mechanical design and actuator selection of the robot was based on the results of parametric simulations. The natural human walking gait was mimicked as a walking pattern in these simulations. As a result of the optimization, a low power-to-weight ratio of 30 W/kg was obtained. The drive system of the robot joints consists of servo-controlled brushless DC motors with reduction gears and additional bevel gears at the knee and ankle joints. The robot features spring-supported knee and ankle joints that counteract the robot's weight and compensate for the backlash present in these joints. The robot is constrained to move only in the sagittal plane by using a lateral support structure. The robot's feet are equipped with low-cost, force-sensitive resistor (FSR)-type sensors for monitoring ground contact and zero-moment point (ZMP) criterion. The experimental results indicate that the proposed robot mechanism can follow the posture commands accurately and demonstrate locomotion at moderate stability. The proposed parametric natural gait simulation-based design approach and the resulting biped robot design with a low power/weight ratio are the main contributions of this study.

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.

IoT-based Integrated System Portable Prayer Mat and DailyWorship Monitoring System

Muslims have various difficulties in praying, such as difficulty memorizing the number of rak’ah they have been doing and determining the direction of the Qibla. In this research, we proposed a technological device for monitoring daily worship in Islam. We presented the IoT-based integrated system as a portable prayer mat serving as a rak’ah counter, Qibla direction finder, and a mobile worship monitoring system. A prototyping approach was used to produce a portable smart prayer mat, and Rapid Application Development was used to develop a mobile daily worship system. The device comprises an Arduino AT Mega 2560 powered portable prayer mat through a force-sensitive resistor sensor and an HMC 5883L compass module. The device sends the prayer activity to the worship applications in detail. The daily worship monitoring application itself has numerous features that enable users to track their daily worship activities, including the Hijri calendar, the time of compulsory prayers, the fulfillment of sunnah prayers, and fasting. Evaluation results showed that the system detected the rak’ah correctly in each cycle with average pressure to the FSR sensor of 81.36. The average time required to connect with a smartphone was 0.862 seconds. It also functions well as a Qibla finder. The black box testing results showed that the device and application performed effectively. It can send the worship data recapitulation to the application using Bluetooth.

RANCANG BANGUN MODUL UKUR TEKANAN PIJAK TELAPAK KAKI ROBOT HUMANOID

Maintaining balance for humanoid robots is one of the core problems to be solved. Krakatau FC as a humanoid type robot performs balancing based on error information obtained from the IMU sensor. Information from the IMU alone is considered inadequate because it ignores the contours of the robot's stepping area which greatly influences the actions that must be taken to maintain balance. The focus of this research is to create a foot pressure measurement module that is compatible with the Dynamixel communication protocol. The sensor used to measure pressure is a Force Sensitive Resistor (FSR) sensor. The test results show the module's ability to measure the pressure exerted by the robot's legs and communicate with the robot's main controller using the Dynamixel protocol. From the test, it is also known that the modules made can help the robot's mechanical calibration process based on the comparison of the pressure between the right and left soles of the feet.

Turbine effect on dental students' lateral pinch modulation and performances.

Dental turbines can generate significant vibrations that may be transmitted to dentists' hands. The vibrations contribute to neural impairment and decrease dexterity over the long duration. It is unclear whether such vibration effects would be detected in the short time during the pre-clinical year of dentistry study. To investigate the effect of vibrations on lateral pinch modulation and manual performance of dentistry students. Four lateral pinch modulation tests by a square-shaped force sensing resistor (FSR) were administered to 30 dental students during their phantom course. Tests were performed with dominant and non-dominant hands at two time points: T0 (2 months after commencing phantom training) and T1 (end of phantom training). At each time point, students accomplished two manual performance tests on plastic teeth. Comparisons were made between lateral pinch modulation test scores at different times, considering the differences between hands and gender. The relationships between the FSR tests and manual performance were also determined. The participating 19 female and 11 male students had a combined 50% response rate. At T1, the lateral pinch modulation for all tests significantly decreased for the dominant hand (P < 0.05) and non-dominant hand (P < 0.05). For the non-dominant hand at T1, the lateral pinch modulation of females significantly decreased as compared to males (P < 0.05). No significant correlations were found between lateral pinch modulation and manual tasks at T0 and T1. The lateral pinch modulation of the fingers of dental students deteriorated after only 8 months of training at phantom (short-time exposure), with females at higher risk and possibly earlier symptoms manifestation.

Gait Abnormality Detection in Unilateral Trans-Tibial Amputee in Real-Time Gait Using Wearable Setup

The presented study proposes a novel approach to detect gait abnormalities in unilateral trans-tibial amputees using a wearable setup. The system uses force sensitive resistors and potentiometers to collect data on the user’s gait patterns. A machine learning algorithm based on Extreme Learning Machines is utilized to classify the gait patterns as normal or abnormal. The system is evaluated on a dataset of healthy and unilateral trans-tibial amputees, and the results reveal that the ELM-based classification technique achieved high accuracy, sensitivity, specificity, and F1 score. The proposed wearable gait setup is tested by conducting a standard six-meter walk test, and the collected data is segmented into stance and swing phases. The study also compares various gait parameters of healthy and amputated subjects, and the results show significant asymmetry in the amputated subjects. The proposed setup also detects asymmetry in force distribution under each foot. The study’s findings reveal that the proposed wearable gait setup is a reliable and effective tool for gait analysis in unilateral trans-tibial amputees, and the results are comparable with those obtained using a Vicon gait measurement system.

Data-Driven Robotic Tactile Grasping for Hyper-Personalization Line Pick-and-Place

Industries such as the manufacturing or logistics industry need algorithms that are flexible to handle novel or unknown objects. Many current solutions in the market are unsuitable for grasping these objects in high-mix and low-volume scenarios. Finally, there are still gaps in terms of grasping accuracy and speed that we would like to address in this research. This project aims to improve the robotic grasping capability for novel objects with varying shapes and textures through the use of soft grippers and data-driven learning in a hyper-personalization line. A literature review was conducted to understand the tradeoffs between the deep reinforcement learning (DRL) approach and the deep learning (DL) approach. The DRL approach was found to be data-intensive, complex, and collision-prone. As a result, we opted for a data-driven approach, which to be more specific, is PointNet GPD in this project. In addition, a comprehensive market survey was performed on tactile sensors and soft grippers with consideration of factors such as price, sensitivity, simplicity, and modularity. Based on our study, we chose the Rochu two-fingered soft gripper with our customized force-sensing resistor (FSR) force sensors mounted on the fingertips due to its modularity and compatibility with tactile sensors. A software architecture was proposed, including a perception module, picking module, transfer module, and packing module. Finally, we conducted model training using a soft gripper configuration and evaluated grasping with various objects, such as fast-moving consumer goods (FMCG) products, fruits, and vegetables, which are unknown to the robot prior to grasping. The grasping accuracy was improved from 75% based on push and grasp to 80% based on PointNetGPD. This versatile grasping platform is independent of gripper configurations and robot models. Future works are proposed to further enhance tactile sensing and grasping stability.

Design a custom resistive force sensors to optimize sensorized insoles

PurposeCurrently, several studies have been published using sensorized insoles for estimating ground reaction force using plantar pressure. However, information on design parameters, manufacturing techniques and guidelines for developing insoles is scarce, often leaving gaps that do not allow reproducing the insole. This study aims to empirically investigate the main parameters of constructing a sensorized insole for application in human gait.Design/methodology/approachTwo devices were built to evaluate the force sensors. The first focuses on the construction of the sensors with different settings: the density of the sensor’s conductive trails (thickness and distance of the trails) and the inertia of the sensors (use of spacers to prevent unwanted readings). The second device focuses on the data capture and processing system: resolution of the analog–digital converter, acquisition rate and sensor activation level.FindingsThe resolution increase of the analog–digital converter and acquisition rate do not contribute to noise increase. Reducing the sensors’ coverage area can increase sensorized insole capacity. The inertia of the sensors can be adjusted using spacers without changing the electrical circuit and acquisition system.Originality/valueMost sensorized insoles use commercial sensors. For this reason, it is not possible a full customization. This paper maps the main variables to manufacture custom sensors and data acquisition systems. This work also presents a case study where it is possible to see the influence of the parameters in the correlation between the sensorized insole and an instrumented treadmill with a force platform.

Calibration and optimization of FSR based smart walking assistance device

The movement of the legs is a very important activity and routine exercise in human life for the fundamental necessity of mobility. Few smart walking assistance (SWA) devices have recently been developed to support walking for older people and osteoarthritis patients. A smart walking assistance device was developed using force-sensitive resistors (FSRs) to support mobility. Precise motion control of the walker wheel is necessary to direct the SWA device in the intended direction. Due to the inaccuracy in motor manufacturing and its internal resistances, the motors and respective attached wheels run at different speeds even though the electrical input signal is the same. The asynchronization in the motors attached to the wheels leads to deviation in the intended path of the user. It is very important to follow the exact path by SWA as per the requirement and safety of the user. In this paper, the speed asynchronization issue of the SWA device is addressed. The asynchronization problem is solved using mathematical modeling followed by implementing effective speed control techniques to provide speed synchronization in all the driving motors of SWA. Further, the power consumption of SWA is also analyzed. It has been found that the proposed SWA device, after calibration and optimization with pulse width modulation (PWM) mapping performed a successful operation with a power-saving of 27.62% at no-load and 77.33% at load conditions.

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study.

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

FABRICATION OF A DIGITAL PLANTAR PRESSURE ESTIMATING DEVICE WITH INTERNET OF THINGS ABILITY

Digitally estimating the magnitude of the Plantar Pressures (PP) of the foot is an important aspect in the biomechanical management of diabetic foot and is not yet adopted in our clime and many low income countries mostly due to high cost and complexity of existing designs, as well as poor awareness. This study aimed to produce a cheaper, easier to use and internet of things compliant digital plantar pressure estimating device (DPPE) which should be able to detect, estimate and output in-shoe plantar pressure distribution. 10 Force Sensitive Resistor (FSR) were anatomically spread across an ambipedal insole. ESP8266 board was utilized as the microcontroller. Web applications were designed to ensure a smooth communication between the sensors, the microcontroller, the internet and the display units (Computers and Phones). The device achieved ambipedal fitting, Wi-Fi to internet signal transmissions were successful, and the sensors ability to detect and estimate pressure was achieved. The DPPE as produced in this study can help in achieving digital assessment of the PP estimation and its distribution on the foot for clinical evaluations, hence, it is an important tool in the clinical assessment of the DF managements. Use of WiFi as a mode of data transmission is ideal in a post-COVID world of ours as it supports virtual communication.

Hybrid genetic algorithm-system identification approach to model force sensing resistors

Force sensing resistor (FSR) is a passive component that is composed of polymer thick films that change resistance between its terminals due to force applied at its surface. FSRs inherently exhibit many nonlinear behaviors. This work employs a Genetic Algorithm agent to navigate the search space to identify the optimal modeling systems for five circular FSRs of comparable sizes. The Hybrid GA-System Identification allows the globally optimized models for the original systems to be identified without the need of a differentiable measure function or linearly separable parameters. The GA searches for the order of the linear model (zeros and poles), the input and output nonlinearities, and the order and the interval of these nonlinearities. Meanwhile, the system identification optimizes the locations of the poles and zeros as well as the parameters of the input and output nonlinearities. The synergy between the two agents allows the entire space to be evaluated for a global solution using the heuristic search advantage of the GA coupled with the fine-tuning of the parameters using the localized search advantage of the system identification. Results show that using the GA agent expedited the search process and allowed for reaching a globally optimized model.

Carbon nanotube fiber-based high-performance force and position sensors for smart fabrics and the Internet of things

Carbon nanotube (CNT) fibers are used in various applications, such as electrical cables, supercapacitors, physical sensors, artificial muscles, and electronic devices, due to their excellent mechanical, thermal, and electrical properties. Herein, the orientation-dependent electrical characteristics of a CNT fiber array were investigated. A force resistive sensor with good sensitivity and reliable operation was developed using the array and integrated with a real-time data storage and wireless monitoring system. In addition, a CNT fiber-based position sensor was developed for application in smart fabrics. This study introduces an easy-to-manufacture, low-cost, robust CNT fiber-based sensing platform that can be used with an open-source microcontroller for integration with the Internet of things.

A 3D-Printed Soft Haptic Device with Built-in Force Sensing Delivering Bio-Mimicked Feedback.

Haptics plays a significant role not only in the rehabilitation of neurological disorders, such as stroke, by substituting necessary cognitive information but also in human-computer interfaces (HCIs), which are now an integral part of the recently launched metaverse. This study proposes a unique, soft, monolithic haptic feedback device (SoHapS) that was directly manufactured using a low-cost and open-source fused deposition modeling (FDM) 3D printer by employing a combination of soft conductive and nonconductive thermoplastic polyurethane (TPU) materials (NinjaTek, USA). SoHapS consists of a soft bellow actuator and a soft resistive force sensor, which are optimized using finite element modeling (FEM). SoHapS was characterized both mechanically and electrically to assess its performance, and a dynamic model was developed to predict its force output with given pressure inputs. We demonstrated the efficacy of SoHapS in substituting biofeedback with tactile feedback, such as gripping force, and proprioceptive feedback, such as finger flexion-extension positions, in the context of teleoperation. With its intrinsic properties, SoHapS can be integrated into rehabilitation robots and robotic prostheses, as well as augmented, virtual, and mixed reality (AR/VR/MR) systems, to induce various types of bio-mimicked feedback.

IOT BASED SMART HELMET WITH BIKE SECURITY

The objective of this paper is to address the safety concerns that parents have when their child is riding a two-wheeler. To achieve this, this topic includes two units, the Helmet Unit (HU) and the Motorbike Unit (MU), which communicate with each other via radio frequency. The HU is equipped with sensors that measure the rider's pulse rate, alcohol content in their breath, and intensity of vibration. The pulse rate sensor detects whether the rider is wearing the helmet or not. The MU has a GPS and GSM module that sends location messages to emergency contacts in case of an accident. The accelerometer on both units detects accidents, and the MU's sensor ensures that the rider is sitting correctly, driving safely, and not over sspeeding. If the alcohol level is too high or an accident is detected, the MU turns off the ignition, sends an emergency message with location, and displays emergency contact details on the OLED screen. A rider safe push button is also provided. The LIDAR sensor on the MU alerts the rider of vehicles approaching from behind. Force-sensitive resistors are installed on the handle grip and footrest to ensure proper seating. The accelerometer in the MU detects tilt along different axes, and if it exceeds the threshold, the buzzer starts beeping and a safety message is displayed on OLED. Finally, the MU's ESP8266 Wi-Fi module sends bike speed and tilt information to the Thing Speak server, which allows parents to monitor their child's safety.

Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.