Force Sensing Resistor Sensors Research Articles

Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors

In this study, we thoroughly analyzed the linearity and repeatability of force-sensing resistor (FSR) sensors through static load tests to ensure their reliability. The novelty of this research lies in its comprehensive evaluation and direct comparison of two widely used FSR sensors, i.e., Flexiforce A201-1 and Interlink FSR-402, under various loading conditions by employing a robust calibration methodology. This study provides detailed insights into the sensors’ performances, offering practical calibration equations that enhance measurement precision and reliability, which have not been extensively documented in previous studies. Our results demonstrate that the linearity of thin film FSR sensors is highly accurate, closely resembling a straight line. We employed M1 Class weights, applying loads ranging from 20 g to 300 g. The resistance of the FSR sensors, which varies with the applied load, was measured using a voltage divider circuit and an analog-to-digital converter of a microcontroller. MATLAB was used to calculate the average output voltage for each applied load and fixed resistance. Additionally, we examined the relationships among load, FSR sensor resistance, and conductivity. Our research indicates that with precise calibration, thin film FSR sensors can be highly reliable for force measurement applications.

A leap into the future: Towards an augmented reality learning environment in ski-jumping

Introduction
 Professional sports are fiercely competitive. In ski jumping, for example, even small changes in take-off and flight can make a decisive difference between victory and defeat (Elfmark et al., 2022). Within the short time of a jump, athletes must learn to solve complex motor control problems while being exposed to harsh environmental conditions, e.g., wind, snow, and low temperatures. The actual take-off occurs within the blink of an eye (~300 ms) and an aerodynamically favourable and stable flight position should be attained immediately. Fine control of the centre of gravity in the in-run favours high speeds to generate optimum momentum during take-off (Müller, 2008). In flight, athletes can voluntarily influence aerodynamics by changing their body position. However, non-optimal flight positions occur unintentionally or due to incorrect behaviour. Furthermore, as a non-cyclical sport, ski jumping suffers from low repetition rates, which impairs the effectiveness of training. Thus, increasing the learning rate for each jump is a key success factor. Biofeedback methods have been shown to accelerate motor learning in athletes (Mulder & Hulstijn, 1985). Current sensor technologies in ski jumping do not meet the requirements for a truly wearable system, which must be energy-efficient, unobtrusive and barely noticeable (so as not to interfere with natural movement behaviour and jumping technique) and, in particular, must be equipped with a wireless link (for real-time data analysis, e.g. on the trainer tower; Schulthess et al., 2023).
 Methods
 The proposed system consists of two multi-sensor nodes: One node is hidden in a modified ski jumping boot, integrating three force-sensing resistor sensors to measure the pressure distribution on the foot soles of ski jumpers. The second sensor node is located in the ski goggles and contains RGB LEDs that provide visual biofeedback in the peripheral vision.
 Results
 We have calculated the total power consumption of our systems to be 2.52 mW, meeting requirements for multi-day operation between battery recharges. Our on-device body position classification model achieves an accuracy of 92.7% in recognising body positions from data recorded in the laboratory.
 Discussion/Conclusion
 This is the first truly wearable training system in ski jumping, offering professional athletes a new augmented experience, aimed at accelerating motor learning. In addition, the real-time data transmission of biomechanically relevant characteristics facilitates the work of the training team and could in the future enable more informative and entertaining television broadcasts.
 References
 Elfmark, O., Ettema, G., & Gilgien, M. (2022). Assessment of the steady glide phase in ski jumping. Journal of Biomechanics, 139, 111139. https://doi.org/10.1016/j.jbiomech.2022.111139
 Mulder, T., & Hulstijn, W. (1985). Sensory feedback in the learning of a novel motor task. Journal of Motor Behavior, 17(1), 110–128. https://doi.org/10.1080/00222895.1985.10735340
 Müller, W. (2008). Performance factors in ski jumping. In H. Nørstrud (Ed.), Sport Aerodynamics (pp. 139– 160). Springer. https://doi.org/10.1007/978-3-211-89297-8_8
 Schulthess, L., Ingolfsson, T. M., Nölke, M., Magno, M., Benini, L., & Leitner, C. (2023). Skilog: A smart sensor system for performance analysis and biofeedback in ski jumping. https://doi.org/10.48550/arXiv.2309.14455

Footwear-integrated force sensing resistor sensors: A machine learning approach for categorizing lower limb disorders

Lower limb disorders are a substantial contributor to both disability and lower standards of life. The prevalent disorders affecting the lower limbs include osteoarthritis of the knee, hip, and ankle. The present study focuses on the use of footwear that incorporates force-sensing resistor sensors to classify lower limb disorders affecting the knee, hip, and ankle joints. The research collected data from a sample of 117 participants who wore footwear integrated with force-sensing resistor sensors while walking on a predetermined walkway of 9 meters. Extensive preprocessing and feature extraction techniques were applied to form a structured dataset. Several machine learning classifiers were trained and evaluated. According to the findings, the Random Forest model exhibited the highest level of performance on the balanced dataset with an accuracy rate of 96%, while the Decision Tree model achieved an accuracy rate of 91%. The accuracy scores of the Logistic Regression, Gaussian Naive Bayes, and Long Short-Term Memory models were comparatively lower. K-fold cross-validation was also performed to evaluate the models’ performance. The results indicate that the integration of force-sensing resistor sensors into footwear, along with the use of machine learning techniques, can accurately categorize lower limb disorders. This offers valuable information for developing customized interventions and treatment plans.

Scaphotrapeziotrapezoid joint loading during key pinch grip before and after trapeziometacarpal arthroplasty: a cadaver study

In a previous cadaver study, we directly measured the load acting on the trapeziometacarpal joint for increasingly greater key pinch forces. We noted that the joint load ranges from 2 kg to 4 kg during progressively greater key pinch from 0.5 kg to 1.5 kg. Using the same experimental approach, the aim of the current study was to measure and compare the load acting on the scaphotrapeziotrapezoid joint for the same levels of isometric key pinch force, and how it changes after trapeziometacarpal arthroplasty. We performed a cadaver study using 7 fresh-frozen, unembalmed adult forearms and hands (2 right and 5 left). Thumb pinch was simulated by loading the main actuator tendons involved in the key pinch grip (i.e., adductor pollicis, flexor pollicis longus, extensor pollicis longus, extensor pollicis brevis and abductor pollicis longus tendons). Measurements were made inside the joint using a force-sensing resistor sensor (Tekscan® FlexiForce™ force sensor). Before the trapeziometacarpal joint surgery, median load values recorded in the scaphotrapeziotrapezoid joint were 1.2 kg (IQR, 1.0–1.4), 1.6 kg (IQR, 1.6–2.5) and 2.4 kg (IQR, 2.3–3.4) during 0.5 kg, 1 kg and 1.5 kg key pinch, respectively. After the trapeziometacarpal arthroplasty, median joint contact forces did not change significantly relative to the original configuration. Our findings indicate that the loads measured in the scaphotrapeziotrapezoid joint during a simple key pinch are in fact lower than those measured inside the trapeziometacarpal joint. After trapeziometacarpal arthroplasty, the values are similar with no increase in load, suggesting that clinically asymptomatic scaphotrapeziotrapezoid radiographic involvement may not be a contraindication to arthroplasty.

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study.

Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.

Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors

Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.

Force distribution of thumb-index finger power-grasp during stable fruit grasp control

To investigate the force distribution of the thumb-index finger power-grasp during the stable grasping of fruit, four thumb-index finger power-grasping experiments were performed. Fruits implanted with force-sensing resistor sensors with seven channels were used to test the real-time normal force of each thumb-index finger region. There was a significant difference among power-grasp regions, fruit size, between power-grasp postures, and centre of mass for the percentage contribution of normal forces on some regions (P < 0.05) during the power-grasping of fruit using the thumb-index finger. The percentage contribution of normal forces on the distal phalanx of the thumb and index finger was larger than those on other regions, and gradually decreased from the distal phalanx to the proximal phalanx. There was a very weak linear association between the characteristics of the thumb and index finger and the percentage contribution of normal forces on each region. The longitudinal power-grasp is a more labour-saving posture than the horizontal power-grasp when grasping a fruit using the thumb and index finger. The normal force contribution on the distal and proximal phalanx of the index finger and the three combination regions are sensitive to changes in the centre of mass location. The normal force of the thumb and the tangential force should be increased to overcome the positive torque when the centre of mass is on the left side. Conversely, when the centre of mass of the fruit is located on the right side, the normal force of the index finger should be increased to prevent it from rotating and sliding to ensure the stable power-grasp of the fruit. It's necessary to adjust the normal force distribution on each grasping region of the humanoid end-effector based on the centre of mass location. This study reveals the force distribution mechanism considering the different characteristics of the fruit and hand and provides guidance for stable bionic two-finger robot grasp control.

Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models.

Women often wear high-heeled shoes for professional or esthetic reasons. However, high-heeled shoes can cause discomfort and injury and can change the body’s center of gravity when maintaining balance. This study developed an assessment system for predicting the maximal safe range for heel height by recording the plantar pressure of participants’ feet by using force-sensing resistor (FSR) sensors and conducting analyses using regression models. Specifically, 100 young healthy women stood on an adjustable platform while physicians estimated the maximal safe height of high-heeled shoes. The collected FSR data combined with and without personal features were analyzed using regression models. The experimental results showed that the regression model based on the pressure data for the right foot had better predictive power than that based on data for the left foot, regardless of the module. The model with two heights had higher predictive power than that with a single height. Furthermore, adding personal features under the condition of two heights afforded the best predictive effect. These results can help wearers choose maximal safe high-heeled shoes to reduce injuries to the bones and lower limbs.

CyberLimb: a novel robotic prosthesis concept with shared and intuitive control

BackgroundExisting assistive technologies attempt to mimic biological functions through advanced mechatronic designs. In some occasions, the information processing demands for such systems require substantial information bandwidth and convoluted control strategies, which make it difficult for the end-user to operate. Instead, a practical and intuitive semi-automated system focused on accomplishing daily tasks may be more suitable for end-user adoption.MethodsWe developed an intelligent prosthesis for the Cybathlon Global Edition 2020. The device was designed in collaboration with the prosthesis user (pilot), addressing her needs for the competition and aiming for functionality. Our design consists of a soft robotic-based two finger gripper controlled by a force-sensing resistor (FSR) headband interface, automatic arm angle dependent wrist flexion and extension, and manual forearm supination and pronation for a shared control system. The gripper is incorporated with FSR sensors to relay haptic information to the pilot based on the output of a neural network model that estimates geometries and objects material.ResultsAs a student team of the Munich Institute of Robotics and Machine Intelligence, we achieved 12th place overall in the Cybathlon competition in which we competed against state-of-the-art prosthetic devices. Our pilot successfully accomplished two challenging tasks in the competition. During training sessions, the pilot was able to accomplish the remaining competition tasks except for one. Based on observation and feedback from training sessions, we adapted our developments to fit the user’s preferences. Usability ratings indicated that the pilot perceived the prosthesis to not be fully ergonomic due to the size and weight of the system, but argued that the prosthesis was intuitive to control to perform the tasks from the Cybathlon competition.ConclusionsThe system provides an intuitive interface to conduct common daily tasks from the arm discipline of the Cybathlon competition. Based on the feedback from our pilot, future improvements include the prosthesis’ reduction in size and weight in order to enhance its mobility. Close collaboration with our pilot has allowed us to continue with the prosthesis development. Ultimately, we developed a simple-to-use solution, exemplifying a new paradigm for prosthesis design, to help assist arm amputees with daily activities.

Trapeziometacarpal joint loading during key pinch grip: A cadaver study

To our knowledge, no study has directly measured the loads in the trapeziometacarpal joint during an isometric key pinch. The aim of this study was to measure the load acting on the trapeziometacarpal joint for increasingly greater key pinch forces (0.5 kg–1.5 kg). We performed a cadaver study using 10 fresh-frozen, unembalmed adult forearms and hands (5 right and 5 left). Thumb pinch was simulated by loading the main actuator tendons involved in the key pinch grip (i.e., adductor pollicis, flexor pollicis longus, extensor pollicis longus, extensor pollicis brevis and abductor pollicis longus tendons). Measurements were made inside the joint using a force-sensing resistor sensor (Tekscan® FlexiForce™ force sensor). All specimens were tested twice in a row in the same condition. The median load values recorded in the trapeziometacarpal joint were 1.9 kg (IQR 2.2–1.5), 3 kg (IQR 3.4–2.7) and 4.1 kg (IQR 4.4–3.9) during 0.5 kg, 1 kg, and 1.5 kg key pinch, respectively. For each specimen, similar load values were observed during both loading trials. Our findings indicate that the loads measured directly in the trapeziometacarpal joint during a simple key pinch are materially lower than those estimated in biomechanical models of the thumb (generally greater than 10 kg for 1 kg of applied force) probably due to intersubject variability. This pilot study will serve as a basis for further studies, for example, comparing biomechanical thumb models and experimental measurements under the same set-up conditions.

Fatigue level detection using multivariate autoregressive exogenous nonlinear modeling based on driver body pressure distribution

Prolonged driving causes symptoms of fatigue in drivers and changes their physical condition during driving. The purpose of this paper is to use a force measurement system located in the driver’s seat by force-sensitive resistance pressure sensors in order to record the received information to predict fatigue by learning regression-based models. This system is designed with 16 FSR (Force Sensing Resistor) sensors mounted on the seat and its backrest that records the driver’s body’s data, based on the force exerted by the driver on the seat in standard mode and during driving at various times. Fatigue level prediction is based on the trained nonlinear autoregressive exogenous model. In this procedure, models based on multivariate regression are first trained, and then correctness is checked. In this paper, the fatigue index is divided into five parts from 0 to 100 included fully conscious, slightly tired, moderately tired, very tired, and extremely tired, so the criterion for diagnosis is crossing the 75% of fatigue index and entering the extremely tired range. The results show that nonlinear models based on exogenous autoregressive have better performance than the linear mode, and even in the nonlinear model of NARX neural network, the fatigue of one step ahead is well predictable. A flopped state will be predictable when the body is immersed in the seat due to fatigue, so is far from the standard sitting position and will be in the extremely tired warning range.

Transfer of Learning from Vision to Touch: A Hybrid Deep Convolutional Neural Network for Visuo-Tactile 3D Object Recognition.

Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic hand. The results obtained confirm the transferability of learning from vision to touch to interpret 3D models. Due to its higher resolution, tactile data from optical tactile sensors was demonstrated to achieve higher classification rates based on visual features compared to other technologies relying on pressure measurements. Further analysis of the weight updates in the convolutional layer is performed to measure the similarity between visual and tactile features for each technology of tactile sensing. Comparing the weight updates in different convolutional layers suggests that by updating a few convolutional layers of a pre-trained CNN on visual data, it can be efficiently used to classify tactile data. Accordingly, we propose a hybrid architecture performing both visual and tactile 3D object recognition with a MobileNetV2 backbone. MobileNetV2 is chosen due to its smaller size and thus its capability to be implemented on mobile devices, such that the network can classify both visual and tactile data. An accuracy of 100% for visual and 77.63% for tactile data are achieved by the proposed architecture.

Foot Pressure Feedback Pneumatic Orthosis: Implication of Daily Life Walking Training for Knee Osteoarthritis Patients

Patients with knee osteoarthritis (KOA) usually experience pain in the medial compartment of their knee which accompany varus postures. This causes much larger lateral-side foot pressure than that of the medial side which may lead to functional limitation in their daily life. Reducing knee loading during gait could help patients with KOA alleviate pain and improve walking functions. In this study, a prototype for a new pneumatic knee orthosis (PKO) is developed to support the knee by increasing the pneumatic pressure of the PKO during the stance period of walking using foot pressure feedback in real time. Force-sensing resistor sensors in the PKO were used to detect the gait phase and monitor the foot pressures in real time. To investigate the feasibility of the system, walking tests on a treadmill were performed at 2 km/h and 3 km/h by healthy subjects. The results show that the developed PKO can reduce lateral-side foot pressure during stance phase. The PKO in this study could be used as a research tool to investigate the relationship between foot pressure and knee loading as well as therapeutic purposes to reduce medial compartment loading in the knee.

Prediction of Body Weight of a Person Lying on a Smart Mat in Nonrestraint and Unconsciousness Conditions.

We want to predict body weight while lying in bed for an elderly patient who is unable to move by himself/herself. To this end, we have implemented a prototype system that estimates the body weight of a person lying on a smart mat in nonrestraint and unconsciousness conditions. A total of 128 FSR (force sensing resistor) sensors were placed in a 16 × 8-grid structure on the smart mat. We formulated three methods based on the features to be applied: segmentation, average cumulative sum of pressure, and serialization. All the proposed methods were implemented with four different machine-learning models: regression, deep neural network (DNN), convolutional neural network (CNN), and random forest. We compared their performance using MAE and RMSE as evaluation criteria. From the experimental results, we chose the serialization method with the DNN model as the best model. Despite the limitations of the presence of dead space due to the wide spacing between the sensors and the small dataset, the MAE and the RMSE of the body weight prediction of the proposed method was 4.608 and 5.796, respectively. That is, it showed an average error of ±4.6 kg for the average weight of 72.9 kg.

Development of Gaiting Analysis using Footwear

An intelligent hybrid project is developed for assisting the partially impaired and physically challenged people. The Fall Detection is proposed to ensure the safety of these people. Force Sensing Resistor (FSR) sensors are attached to the footwear. For each in a pair of shoes four FSR sensors are attached at the precise locations in the shoe. In this method, the user’s pressure from the specified points is calculated in real time using an integrated force sensory system, which comprises FSR sensors in the shoe. When the pressure from these specified points cross the boundary limit of the specified safety value, i.e., the maximum pressure, it is assessed that the user is going to fall down. The data retrieved from these FSR sensors is used for computing the gaiting analysis. We have used Arduino microcontrollers for data collection from the pair of shoes incorporated with sensors. The data gathered here is transmitted via Bluetooth protocol to the core FPGA where the main Gaiting Analysis is performed and fall detection is achieved here. The novelty in this paper is we have used the FPGA board for Fall Detection because it has high performance, less power consumption and parallel processing support. So using FPGA is the best option in this project. This project can be broadly applied across Healthcare and Military applications. The detected fall can be further improved and it can be prevented by Fall Prevention prototypes. In this study, we have successfully achieved the desired results, and all the proposed methods were finally verified through simulations and experiments.

Correlation of Temporal Parameters of Laryngeal Excursion by Using Force-Sensing Resistor Sensors with Hyoid Motion in Videofluoroscopic Swallowing Study

Small flexible force-sensing resistor (FSR) sensors can detect laryngeal excursion during swallowing, but the detected laryngeal excursion has not been correlated with videofluoroscopic swallowing study (VFSS) results. Here, we tested the correlation of temporal parameters between the laryngeal excursion recording by FSR sensor and the hyoid motion recording by VFSS under simultaneously swallowing test recordings. Swallowing measurements were recorded in a radiological suite by simultaneously using VFSS and FSR sensors to detect hyoid motion and laryngeal excursion, respectively. Volunteers sat with their head vertical to the Frankfort plane. Two FSR sensors, each for detecting thyroid cartilage excursion and thumb pressing, were placed. VFSS images and FSR sensor signals during single 5-mL barium liquid (30% wt/volume %) bolus swallowing were collected and analyzed for four swallows per participant. In total, 15 men (28.0 ± 4.1 years old); 14 women (28.4 ± 4.2 years old) were recruited. Temporal parameters between VFSS and noninvasive system demonstrated a strong correlation by Pearson’s correlation analysis: in men (R = 0.953–0.999) and in women (R = 0.813–0.982), except for VT1–V1 compared with FT1–F1, which demonstrated a moderate correlation in women (R = 0.648; all p < 0.001). Only VT1–V1 and FT1–F1 in women displayed a significant difference (p = 0.001). Therefore, this is the first study to simultaneous record VFSS and noninvasive signals by FSR sensor. The correlation of temporal parameters between these two tests was strong. This finding is valuable for future applications of this noninvasive swallowing study tool.

Measurement of flexible components in composite material by a 3-2-1 locating scheme

Thin components in composite material are of great interest in many fields, but the component deformation, known as spring-in and warpage, arise during the manufacturing process; so, it is really important to verify if deformations are within the indicated tolerances. However, the inspection of thin components can be difficult if it is carried out by a coordinate measuring machine because of their high flexibility. The aim of the present work is to present a method to inspect flexible components in composite material as rigid ones by means of a coordinate measuring machine with a touch probe. The proposed method foresees to acquire the probing forces and the clamping forces, due to the fixturing equipment, by force sensing resistor sensors and uses them in a finite element analysis to evaluate the corrections to be applied to the coordinates of points acquired through a coordinate measuring machine. The proposed method was applied to a L-shaped component and all results were compared with those due to a laser measurement system.

Evaluation of driver stress level with survey, galvanic skin response sensor data, and force-sensing resistor data

Thousands of lives are lost in traffic accidents every year, and most traffic accidents are caused by driver errors. Causes and impairments such as fatigue, inattentiveness, alcohol usage, stress, and drugs are the main factors of these accidents. When a driver is subject to changing and complicated driving tasks in traffic, he or she should be able to assure driving authority to prevent potential hazards and accidents. In this context, the purpose of this study is to determine the stress level of the driver when driving in urban traffic in such situations requiring delegation of driving authority. Thus, the work combines stress questionnaire and galvanic skin response sensor to validate results and fuses with a force-sensing resistor. In this study, a prototype electric vehicle is equipped with sensors providing various drivers’ data including the responses of a force-sensing resistor sensor while galvanic skin is being collected on a specified route. At the end of the trip, the stress level of the drivers is determined by the collected data. Results indicate that the galvanic skin sensor stress results are consistent with the results of the survey with an average accuracy of 87.5%. The force-sensing resistor sensor is only used to determine gender stress. And the force-sensing resistor sensor gender-stress results are consistent with results of the survey with an accuracy of 100%. These results are used to validate the results of post-driving stress survey evaluated by SPSS 23.0 windows statistics software. Data analysis is particularly focused on demographic properties of participators, factor analysis, reliability tests, correlation, T-test, and one-way analysis of variance.

Fingertip Tactile Sensor With Single Sensing Element Based on FSR and PVDF

The fingertip sensor is essential for the closed loop control of prosthetic hand, while the development of a tactile sensor with a balance in the cost and performance is still a challenge. This paper presents the development of an anthropomorphic fingertip sensor for prosthetic hands. The structure of our sensor mainly consists of a force sensing resistor (FSR) sensor module and a polyvinylidene fluoride (PVDF) sensor module. The PVDF sensor module is made up of a circular truncated cone shaped PVDF film that wrapped in a polydimethylsiloxane (PDMS) substrate. The PVDF sensor module plays two roles in the tactile sensing task. First, the dynamic low frequency response of the PVDF is used for dynamic contact point estimation. Second, the high frequency response of the PVDF is used for texture recognition. An FSR sensor module is used for static force measuring. Experimental results show that the sensor has a good performance in contact force measuring, contact point estimation, and texture recognition. The force measurement error of normal force is less than 5% at the most sensitive subareas, and the sensor is capable of distinguishing five subareas of the sensitive part at an accuracy over 96%. It also can recognize five different textures with an accuracy of 94.1%±0.7%.

Capture Point-Based Controller Using Real-Time Zero Moment Point Manipulation for Stable Bipedal Walking in Human Environment.

For collaboration of humans and bipedal robots in human environments, this paper proposes a stability control method for dynamically modifiable bipedal walking using a capture point (CP) tracking controller. A reasonable reference CP trajectory for the CP tracking control is generated using the real-time zero moment point (ZMP) manipulation without information on future footstep commands. This trajectory can be modified at any time during the single support phase according to a given footstep command. Accordingly, this makes it possible for the robot to walk stably with dynamically modifiable walking patterns, including sudden changes in navigational commands during the single support phase. A reference CP trajectory during the double support phase is also generated for continuity. The CP of the robot is controlled to track the reference trajectory using a ZMP-based CP tracking controller. The ZMP while walking is measured by the force-sensing resistor sensors mounted on the sole of each foot. A handling method for infeasible footstep commands is utilized so that the manipulated ZMP satisfies the allowable ZMP region for stability. The validity of the proposed method is verified through simulations and experiments.