Force Sensitive Resistor Sensors Research Articles

AI-Based System for In-Bed Body Posture Identification Using FSR Sensor

Non-invasive sleep monitoring holds significant promise for enhancing healthcare by offering insights into sleep quality and patterns. In this context, accurate detection of body position is crucial, as it provides essential information for diagnosing and understanding the causes of various sleep disorders, including sleep apnea. The aim of this work is to develop an efficient system for sleep position detection using a minimal number of FSR (Force Sensitive Resistor) sensors and advanced machine learning techniques. A hardware setup was developed incorporating 3 FSR sensors, on-board signal processing for frequency boundary filtering and gain adjustment, an ADC (Analog-to-digital converter), and a computing unit for data processing. The collected data was then cleaned and structured before applying various machine learning models, including Logistic Regression, Random Forest Classifier, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and XGBoost. An experiment with 15 subjects in 4 different sleeping positions was conducted to evaluate the system. The SVC demonstrated notable performance with a test accuracy of 64%. Analysis of the results identified areas for future improvement, including better differentiation between similar positions. The study highlights the feasibility of using FSR sensors and machine learning for effective sleep position detection. However, further research is needed to improve accuracy and explore more advanced techniques. Future efforts will aim to integrate this approach into a comprehensive, unobtrusive sleep monitoring system, contributing to better healthcare services.

Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks

Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data.

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.

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.

Optimal mask fixation method for frameless radiosurgery with Leksell Gamma Knife IconTM.

The Leksell Gamma Knife (LGK) IconTM is used for mask-based and frame-based fixation. The mask fixation provides a noninvasive method. However, an optimal mask fixation method is yet to be established. We evaluated the characteristics of three mask fixation methods (Plain, Folded, and Wide) for the LGK IconTM . Force-sensitive resistor sensors were attached to the forehead, supraorbital, zygoma, mandible, and occipital bone of the phantom, and digital humidity and temperature sensors were attached to both temporal lobes. Cone-beam computed tomography (CBCT) and high-definition motion management (HDMM) for each mask fixation method were used to evaluate the phantom motion during the initial application. Subsequently, the mask was removed and reapplied on the second (1st reapplication) and third days (2nd reapplication). In the initial application, forces acting on most portions of the phantom were stabilized within 1.5 h. The largest force acted on the occipital bone for the Plain and Wide methods and on the mandible for the Folded method. The temperature rapidly approaches the initial temperature, whereas the humidity gradually approached the initial humidity in all fixation methods. The Folded method exhibited a significantly lower translation along the Y-axis of the Leksell coordinate system, and rotations along all axes were under 0.5°. The HDMM values remained at 0.1 mm for all fixation methods. In the reapplications, the force acting on the occipital bone was significantly greater than that during the initial application for all mask fixation methods; the temperature and humidity remained unchanged. All mask fixation methods in the 1st reapplication were not significantly different from those in the 2nd reapplication. The Folded method is recommended as an optimal mask fixation for patients who require tight fixation; the Wide method can be considered if patient comfort is a priority.

Design and Evaluation of a Low-Cost Electromechanical System to Test Dynamic Performance of Force Sensors at Low Frequencies

Piezoresistive or piezoelectric force sensors are widely available today. These sensors are preferred to loadcells because of their extremely reduced size, slimness, and low cost, which allow their easy inclusion in a large variety of devices including wearables. In particular, many applications are devoted to monitoring human body movements, such as those related to breathing, muscle contraction, walking, etc. However, such sensors offer variable performance, and they need to be individually calibrated and tested to ensure accurate measurements. An automated electromechanical system that allows simple mechanical tests of force sensors is proposed. The system by means of an electrical motor; a gear box; a connecting rod-crank mechanism; two pistons, and a coupling spring between them, impress sinusoidal axial forces onto the sensor under test. The system is designed as modular so that it can be customized: the force range to which the sensor is subjected, the frequency range, and the coupler with the sensor can be changed to resemble the actual application context. The actual force (read from a loadcell coupled to the sensor under test), a piston displacement, and the sensor output are simultaneously recorded. The electromechanical system generates nearly pure sinusoidal stresses at varying low frequencies (mean total harmonic distortion of 2.77%). The energy dissipated for a single stress cycle was 3.62 gf mm on average. The developed system was used to test a Force Sensitive Resistor (FSR)-based sensor and a piezoelectric (PZT) sensor. The tests revealed significant differences from the actual force values (particularly at very low frequencies), output drifts of the FSR sensor in measurements, and non-linear behaviors. The system was found to be able to provide dynamic performances, accurate calibration, and non-linear behavior of the individual sensor.

An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms.

In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid the harms of an improper posture as it, when prolonged, is very painful and can be fatal sometimes. This study also includes a comparison of two arrangements. Arrangement 01 includes six force-sensitive resistor (FSR) sensors alone, and it is less expensive. Arrangement 02 consists of two FSR sensors and one ultrasonic sensor embedded in the back seat of a chair. The K-nearest neighbor (KNN), Naive Bayes, logistic regression, and random forest algorithms are used to augment the gain and enhanced accuracy for posture detection. The improper postures recognized in this study are backward-leaning, forward-leaning, left-leaning, and right-leaning. The presented results validate the proposed system as the accuracy of 99.8% is achieved using a smaller number of sensors that make the proposed prototype cost-efficient with improved accuracy and lower execution time. The proposed model is of a dire need for employees working in offices or even at the residential level to make it convenient to work for hours without having severe effects of improper posture and prolonged sitting.

A FSR Sensor Cuff to Measure Muscle Activation During Strength and Gait Cycle for Lower Limb

The assessment techniques used for people who experience muscular issues, regardless of the cause, are crucial to resolve the issue and fill the gap left by the deficiency in their muscles. The amputees’ demands in their rehabilitation process are made more evident by evaluating the residual limb muscles. The time-varying muscle activity of the amputees weakens the efficiency of using these fixed positions inside the socket, which leads to a great challenge for them to continue using the prosthesis in this case. Depending on the daily motion activity or routine of the amputees, a change in the level of muscle activation may occur because every motion activity has its dynamic that depends on muscle activity. For this purpose, a new reliable cuff system using a simple, non-invasive sensor based on a force-sensitive resistor (FSR) which can measure muscle contraction, is presented in this study. The cuff system was designed by Autodesk Inventor software and used Cura for slicing functions. This design was printed by Creator-Pro 3D printer as an FDM type. The cuff includes 8 FSR sensors printed with TPU and APL for the cube. Applying FSRs on the skin senses the mechanical force exerted by the underlying contracting muscles. Regarding FSR signals and data collection and display, Datastreamer by Microsoft Excel was used for this purpose. Five male non-amputee subjects were involved in this study to do two activities (strength muscles and gait cycle). Each activity was subjected to two tests, the first conducted above the knee, while test 2 was below the knee. Rectus femoris (RF) and tibialis anterior (TA) muscles were targeted as a positioning reference for above and below the knee tests, respectively. Through the experiments, there was no complaint from the subjects about the surface of the cuff touching the skin. Still, the issue revolved around the tightness of the cuff, especially at the above-knee level for both activities. As thigh circumference, the cuff stretching efficiency was achieved over maximum stretch capability at 63 cm. After more than 50 trials with the two activities mentioned above, there was no change in the cuff dimensions either for maximum or minimum size, i.e., without stretching occurring. F1- all individuals for both activities displayed no appreciable signal variations and began with high levels of values, according to the signals data. That results from the tightness of the cuff on the limb. As a result of its site being unfavorable to any muscle activity, F2 presented poor signals in both activities for all participants. The cuff size needs to be reevaluated to prevent the problem of cuff tightness. The findings of the trials generally demonstrated the muscles’ active locations and the change of activity taking place in those positions. On the other hand, the findings of the experiments also presented the inactive positions.

Quantitative Assessment of Clinician Assistance During Dynamic Rehabilitation Using Force Sensitive Resistors.

Background: Neuromodulation using epidural electrical stimulation (EES) has shown functional restoration in humans with chronic spinal cord injury (SCI). EES during body weight supported treadmill training (BWSTT) enhanced stepping performance in clinical trial participants with paraplegia. Unfortunately, tools are lacking in availability to quantify clinician assistance during BWSTT with and without EES. Force sensitive resistors (FSRs) have previously quantified clinician assistance during static standing; however, dynamic tasks have not been addressed.Objective: To determine the validity of FSRs in measurements of force and duration to quantify clinician assistance and participant progression during BWSTT with EES in participants with SCI.Design: A feasibility study to determine the effectiveness of EES to restore function in individuals with SCI.Methods: Two male participants with chronic SCI were enrolled in a pilot phase clinical trial. Following implantation of an EES system in the lumbosacral spinal cord, both participants underwent 12 months of BWSTT with EES. At monthly intervals, FSRs were positioned on participants' knees to quantity forces applied by clinicians to achieve appropriate mechanics of stepping during BWSTT. The FSRs were validated on the benchtop using a leg model instrumented with a multiaxial load cell as the gold standard. The outcomes included clinician-applied force duration measured by FSR sensors and changes in applied forces indicating progression over the course of rehabilitation.Results: The force sensitive resistors validation revealed a proportional bias in their output. Loading required for maximal assist training exceeded the active range of the FSRs but were capable of capturing changes in clinician assist levels. The FSRs were also temporally responsive which increased utility for accurately assessing training contact time. The FSRs readings were able to capture independent stance for both participants by study end. There was minimal to no applied force bilaterally for participant 1 and unilaterally for participant 2.Conclusions: Clinician assistance applied at the knees as measured through FSRs during dynamic rehabilitation and EES (both on and off) effectively detected point of contact and duration of forces; however, it lacks accuracy of magnitude assessment. The reduced contact time measured through FSRs related to increased stance duration, which objectively identified independence in stepping during EES-enabled BWSTT following SCI.

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.

FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

PurposeThe purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent.Design/methodology/approachGait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis.FindingsThe effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains.Practical implicationsThis study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains.Originality/valueThe noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

Development of a Safety Management System Tracking the Weight of Heavy Objects Carried by Construction Workers Using FSR Sensors

It has been pointed out that the act of carrying a heavy object that exceeds a certain weight by a worker at a construction site is a major factor that puts physical burden on the worker’s musculoskeletal system. However, due to the nature of the construction site, where there are a large number of workers simultaneously working in an irregular space, it is difficult to figure out the weight of the object carried by the worker in real time or keep track of the worker who carries the excess weight. This paper proposes a prototype system to track the weight of heavy objects carried by construction workers by developing smart safety shoes with FSR (Force Sensitive Resistor) sensors. The system consists of smart safety shoes with sensors attached, a mobile device for collecting initial sensing data, and a web-based server computer for storing, preprocessing and analyzing such data. The effectiveness and accuracy of the weight tracking system was verified through the experiments where a weight was lifted by each experimenter from +0 kg to +20 kg in 5 kg increments. The results of the experiment were analyzed by a newly developed machine learning based model, which adopts effective classification algorithms such as decision tree, random forest, gradient boosting algorithm (GBM), and light GBM. The average accuracy classifying the weight by each classification algorithm showed similar, but high accuracy in the following order: random forest (90.9%), light GBM (90.5%), decision tree (90.3%), and GBM (89%). Overall, the proposed weight tracking system has a significant 90.2% average accuracy in classifying how much weight each experimenter carries.

Foot Plantar Pressure Measurement System for Static and Dynamic Condition

The purpose of this paper is to measure human foot pressure at various important points and to provide the proper diagnosis for treating the post-operative orthopedic patients. Measurement of the human foot pressure distribution can provide vital information and is of great value for medical diagnostics. The distribution of human plantar pressure data depends on the conditions of the foot such as foot structure, function, the posture control of the entire body and gait. This device data will contribute greatly to the diagnosis, estimation of surgery and a better understanding of the pathogenesis of diseased foot with abnormal pressure distribution. The device will measure the foot pressure with the help of FSR, force sensitive resistor sensors which are placed on the upside of the sole of shoe. These sensors are connected to the Arduino Mega 2560. A custom-made software is created using LabVIEW. The patient’s details can be fed in and are saved in this software and when executed, the pressure data values collected is converted into digital pattern and a pedobarography is generated. This pedobarography image is displayed and saved in a word document and thus, enables the doctors to examine the foot pressure based on the deduced results.

Semi-automatic Knob System for Assisting Flexible Endoscope Steering

In this paper, a semi-automatic knob system for assisting control of flexible endoscope is introduced. For conventional flexible endoscope, the torque to bend the bending part is increased proportionally to the curvature of the insertion tube. As the bending angle increases, the required torque is increased. This characteristic makes a surgeon tired. The proposed semi-automatic knob system consists of a knob controller, a power generation unit, and a bending part. The knob controller includes four FSR (force sensitive resistor) sensors to measure external force applied by the operator. The power generation unit generates the power depending on the signal level of FSR sensors. Using the semi-automatic knob system, a user can control the steering of the flexible endoscope with less power as compared to conventional flexible endoscope. The usefulness of the proposed system is verified through experiments.

Wearable Finger Tracking and Cutaneous Haptic Interface with Soft Sensors for Multi-Fingered Virtual Manipulation

Multi-Fingered haptics is imperative for truly immersive virtual reality experience, as many real-world tasks involve finger manipulation. One of the key lacking aspect for this is the absence of technologically and economically viable wearable haptic interfaces that can simultaneously track the finger/hand motions and display multi-degree-of-freedom (DOF) contact forces. In this paper, we propose a novel wearable cutaneous haptic interface (WCHI), which consists of 1) finger tracking modules (FTMs) to estimate complex multi-DOF finger and hand motion; and 2) cutaneous haptic modules (CHMs) to convey three-DOF contact force at the finger-tip. By opportunistically utilizing such different types of sensors as inertial measurement units, force sensitive resistor sensors, and soft sensors, the WCHI can track complex anatomically consistent multi-DOF finger motion while avoiding FTM-CHM electromagnetic interference possibly stemming from their collocation in the small form-factor interface; while also providing the direction and magnitude of three-DOF finger-tip contact force, the feedback of which can significantly enhance the precision of contact force generation against variability among users via their closed-loop control. Human subject study is performed with a virtual peg insertion task to show the importance of both the multi-DOF finger tracking and the three-DOF cutaneous haptic feedback for dexterous manipulation in virtual environment.

Sleeping posture recognition using fuzzy c-means algorithm

BackgroundPressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet.MethodWe designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures.ResultsThe experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy.ConclusionsThe proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.

Center of Pressure Feedback for Controlling the Walking Stability Bipedal Robots using Fuzzy Logic Controller

This paper presents a sensor-based stability walk for bipedal robots by using force sensitive resistor (FSR) sensor. To perform walk stability on uneven terrain conditions, FSR sensor is used as feedbacks to evaluate the stability of bipedal robot instead of the center of pressure (CoP). In this work, CoP that was generated from four FSR sensors placed on each foot-pad is used to evaluate the walking stability. The robot CoP position provided an indication of walk stability. The CoP position information was further evaluated with a fuzzy logic controller (FLC) to generate appropriate offset angles to be applied to meet a stable situation. Moreover, in this paper designed a FLC through CoP region's stability and stable compliance control are introduced. Finally, the performances of the proposed methods were verified with 18-degrees of freedom (DOF) kid-size bipedal robot.<br /><br />

A Piezoresistive Sensor to Measure Muscle Contraction and Mechanomyography.

Measurement of muscle contraction is mainly achieved through electromyography (EMG) and is an area of interest for many biomedical applications, including prosthesis control and human machine interface. However, EMG has some drawbacks, and there are also alternative methods for measuring muscle activity, such as by monitoring the mechanical variations that occur during contraction. In this study, a new, simple, non-invasive sensor based on a force-sensitive resistor (FSR) which is able to measure muscle contraction is presented. The sensor, applied on the skin through a rigid dome, senses the mechanical force exerted by the underlying contracting muscles. Although FSR creep causes output drift, it was found that appropriate FSR conditioning reduces the drift by fixing the voltage across the FSR and provides voltage output proportional to force. In addition to the larger contraction signal, the sensor was able to detect the mechanomyogram (MMG), i.e., the little vibrations which occur during muscle contraction. The frequency response of the FSR sensor was found to be large enough to correctly measure the MMG. Simultaneous recordings from flexor carpi ulnaris showed a high correlation (Pearson’s r > 0.9) between the FSR output and the EMG linear envelope. Preliminary validation tests on healthy subjects showed the ability of the FSR sensor, used instead of the EMG, to proportionally control a hand prosthesis, achieving comparable performances.

Effect of Radial Stress on the Adhesive Force of a Wound Roll in Industrial Roll-to-Roll Manufacturing System

There is a correlation between the radial stress and the adhesive force of a wound roll in an industrial scale roll-to-roll manufacturing system. The system considered in this study is used for the fabrication of wide-width (2235 mm) adhesive film of 30 μm thickness. This manufactured adhesive film is used for protection in one manufacturing step of a liquid crystal display line. The radial stress, which is measured in this study using force sensitive resistor sensors, is a major factor influencing the degradation of adhesive force. The adhesive forces were measured based on the tape method with the direct pull-off test. It is confirmed that the radial stress has an inversely proportional effect on the adhesive forces of the film. To improve the resulting adhesive value of the film, adjustments to operating tension and taper value are employed. Statistical analysis shows that low tension conditions result in less adhesive deviation, and that low taper values at high tensions can reduce the deviation of adhesive forces. The tension control guidelines contribute to an improvement in mass production of roll type adhesive film products used in displays and other applications.

Compact Hip-Force Sensor for a Gait-Assistance Exoskeleton System.

In this paper, we propose a compact force sensor system for a hip-mounted exoskeleton for seniors with difficulties in walking due to muscle weakness. It senses and monitors the delivered force and power of the exoskeleton for motion control and taking urgent safety action. Two FSR (force-sensitive resistors) sensors are used to measure the assistance force when the user is walking. The sensor system directly measures the interaction force between the exoskeleton and the lower limb of the user instead of a previously reported force-sensing method, which estimated the hip assistance force from the current of the motor and lookup tables. Furthermore, the sensor system has the advantage of generating torque in the walking-assistant actuator based on directly measuring the hip-assistance force. Thus, the gait-assistance exoskeleton system can control the delivered power and torque to the user. The force sensing structure is designed to decouple the force caused by hip motion from other directional forces to the sensor so as to only measure that force. We confirmed that the hip-assistance force could be measured with the proposed prototype compact force sensor attached to a thigh frame through an experiment with a real system.