Recently, flexible pressure sensors featuring enhanced sensitivity and durability through nano/micro additive manufacturing have been employed in various fields, including medical monitoring, E-skin technology, and soft robotics. This study focuses on the fabrication and verification of an interdigitated electrode (IDE) based flexible pressure sensor that incorporates microstructures, utilizing a direct patterning-based additive process. The IDE-patterned sample was designed with a total size of 7.95 × 10 mm2, a line width of 150 µm, a spacing of 200 µm, and a probe pad measuring 1.25 × 2 mm2. It was fabricated using AgNP ink on a primed 100 µm thick polyethylene naphthalate (PEN) substrate. The electrode layer was subsequently covered with a sensing layer made of a MWCNT/Ecoflex composite material, resulting in the final pressure sensor sample. Measurements indicated that the sensor exhibited good sensitivity and response speed, and it was confirmed that further improvements in sensitivity could be achieved by optimizing the size, spacing, and height of the microstructures. Building on the flexible pressure sensor structure developed in this study, we plan to pursue future research aimed at fabricating array sensors with integrated circuits and exploring their applicability in wearable devices for pressure sensing and control functions.

In this study, we fabricated and investigated the polymer-based cylindrical flow sensor for two-dimensional (2D) detection. The flow sensor was the drag force type flowmeter which was fabricated with ecoflex. It had CNT/PDMS as the piezoresistive material and a cylindrical shape to measure the 2D flow. It also had impact resistance and ease of fabrication due to its polymer-based sensor. At first, two piezoresistive parts were applied to evaluate detection properties. Forces from various direction were applied. Results showed its potential as a sensing device. Following this, the final flow sensor was fabricated with four piezoresistive parts and its sensitivity was measured in the air flow from 0 to 30 m/s. Resistance changes were measured while rotating the sensor. Outputs showed a form of sine waves. Data were repeatedly collected under various conditions. The direction and air flow rate were then determined. To check physical impact resistance, a sudden high air flow rate with 100m/s was applied to the sensor and a stable output was obtained. These results suggest that such ecoflex-based cylindrical flow sensor can be used as a 2D flow rate sensor.

This paper extensively explores and analyzes the latest research trends in Ionic Polymer-Metal Composites (IPMC) sensors. IPMC sensors are known for their flexibility, lightness, and high responsiveness. They show great promise across different fields. They can respond sensitively to various stimuli such as mechanical deformation, humidity, and pressure, making them ideal for bio-responsive detection, health monitoring, and energy harvesting. This paper introduces actuation and sensing mechanisms of IPMCs, discusses their manufacturing processes, and explores how these processes can influence the responsiveness and stability of sensors. Moreover, through case studies of IPMC-based research that can perform self-sensing functions, it presents possibilities brought by the integration of sensors and actuators. This paper emphasizes the potential for research and development of IPMC sensors to expand into various industrial fields and explores ways to continuously improve the accuracy and reliability of sensors. IPMC-based sensors are expected to play a significant role in advancing medical devices and wearable technologies, thereby facilitating innovation in the field.

Gas sensors are crucial devices in various fields such as industrial safety, environmental monitoring, and gas infrastructure. Designed to have high-sensitivity, stability, and reliability, gas sensors are often required to be cost-effective with quick response and compactness. To meet diverse needs, we developed two types of gas sensors based on volumetric and manometric analyses. These sensors could operate by measuring gas volume and pressure changes, respectively, based on emitted gas. These sensors are capable of determining gas transport parameters such as gas uptake, solubility, and diffusivity for gas-charged polymers in a high-pressure environment. These sensors can provide rapid responses within one-second. They can measure gas concentration ranging from 0.01 wt·ppm to 1,500 wt·ppm with adjustable sensitivity and measurement ranges. As a result, such sensor system can be used to facilitate real time detection and analysis of gas transport properties in pure gases including H₂, He, N₂, O₂, and Ar.

In this study, a module combining various types of sensors was developed to increase search efficiency inside collapsed buildings. It was designed to be less than 70 mm in diameter so that it can be put into narrow spaces, and is equipped with a small & high-performance processor to process multiple sensor data. To increase sensor data processing efficiency, multi thread based software was configured, and the images were combined and transmitted to ensure time synchronization of multi-channel video data. A human detection function based on sound source detection using two microphones was implemented. The developed multi-sensor module was tested for operation by mounting it on a snake-type robot in a test bed simulating a disaster site. It was confirmed that the visible range of the robot to which the multi-sensor module was applied was expanded, and the ability to detect human and low-light human detect was secured.

In this study, a highly sensitive analysis device for hydrogen sulfide that could be used quickly and easily on site was developed using a colorimetric paper sensor. To optimize analysis conditions, tests were performed for each function. Performances of the method using laboratory equipment and tools and the method using the developed device for hydrogen sulfide analysis were compared. The trend line of changes in parameter b of the image acquired by the on-site analytical device for hydrogen sulfide was calculated as y = 0.517x - 0.141 with a coefficient of determination (R2) of 0.9874. It was comparable to the method performed at the laboratory level, showing an excellent linearity. Using the calculated trend line as a calibration curve, the detection limit and quantification limit were found to be 2.386 μM and 7.952 μM, respectively. A reproducibility test showed a relative standard deviation of 5.7%, indicating a low dispersion of results.

This study reports an autonomous fine dust source tracking system of a water spray robot for high-rise building demolition. The core function of this system is performing a self-controlled fine dust tracking of the endpoint of the excavator, which is the fine dust generation point. The water spray robot has a lift with a parallelogram-shaped linkage to lift the water spray drum to 10 m from the ground. The sensor network system is connected to the robot and the excavator to calculate the relative position of the water spray drum and excavator endpoint using forward kinematics. RTK-GPS is attached to the robot and the excavator to calculate the relative distance. By sensor network, forward kinematics, and RTK-GPS, the water spray robot can autonomously track fine dust generation point and spray water to the endpoint of the excavator. The experiment was conducted to confirm the accuracy of kinematics calculation and tracking performance of the robot. The first experiment showed that the calculation result of forward kinematics was accurate enough to fulfill tracking operations. The second experiment showed that the tracking accuracy was precise enough, meaning that the robot could autonomously track fine dust generation point.

Recently, the estimation of joint kinetics such as joint force and moment using wearable inertial sensors has received great attention in biomechanics. Generally, the joint force and moment are calculated though inverse dynamics using segment kinematic data, ground reaction force, and moment. However, this approach has problems such as estimation error of kinematic data and soft tissue artifacts, which can lead to inaccuracy of joint forces and moments in inverse dynamics. This study aimed to apply a recurrent neural network (RNN) instead of inverse dynamics to joint force and moment estimation. The proposed RNN could receive signals from inertial sensors and force plate as input vector and output lower extremity joints forces and moments. As the proposed method does not depend on inverse dynamics, it is independent of the inaccuracy problem of the conventional method. Experimental results showed that the estimation performance of hip joint moment of the proposed RNN was improved by 66.4% compared to that of the inverse dynamics-based method.

The purpose of this study was to check the position classification prediction rate based on the movement data of field hockey players using the random forest algorithm. In order to achieve the purpose of this study, movement data were collected using wearable devices in 15 practice matches. The collected information was then analyzed using the Random Forest algorithm, one of the ensemble techniques, with Python, a high-level, general-purpose programming language. As a result of this study, first, the position classification prediction rate was 52.4±3.3% when data measured by GPS sensors were used. Second, when using the data measured by an inertial measurement unit (IMU) sensor, the position classification prediction rate was 50.8±2.4%. Third, when both Global Positioning System (GPS) and IMU data were used, the position classification prediction rate was 55.6±2.0%. As a result of the study, it showed that the prediction rate was the highest when both GPS and IMU data were used.

In this study, the Inertial Measurement Unit (IMU) signals and clinical evaluation scales for Parkinson"s disease were correlated. The study included 16 patients diagnosed with Parkinson"s disease. Each subject was evaluated based on Korean Mini-Mental State Examination (KMMSE), Unified Parkinson"s Disease Rating Scale (UPDRS) part 3, New Freezing of Gait Questionnaire (NFOGQ) parts 2 & 3, and Hoehn & Yahr Scale (H&Y). All subjects performed the Time Up and Go test by attaching IMU sensors to both ankles and torso. Based on the tilting angle of torso and the time of first step, the freezing and non-freezing windows were determined. Seven IMU features involving the ankle signals were calculated in the specific window. Spearman’s correlation analysis of clinical evaluation scales was performed. As a result, the freezing index and power of locomotion band (0.3-3 Hz) were recommended to determine UPDRS part 3. Also, the intensity of the locomotion band facilitated evaluation of NFOGQ part 3 regardless of freezing of gait.

In this paper, we describe the development of a 5-axis force/moment sensor of an intelligent gripper designed to grasp the weight of an unknown object and the position of the object in the gripper. The 5-axis force/moment sensor consists of an Fx force sensor, Fy force sensor, and Fz force sensor to measure weight, along with an Mx moment sensor and Mz moment sensor to determine the position of an object in the gripper. These sensors are all built within a single body. Each sensor sensing part of the 5-axis force/moment sensor was newly modeled and custom designed using software, and each sensor was manufactured by attaching a strain gauge. The results of the characteristic test of the fabricated 5-axis force/moment sensor showed that the rated output error was within 0.1%, the reproducibility error was within 0.05%, and the nonlinearity error was within 0.04%. Therefore, the 5-axis force/moment sensor developed in this paper can be attached to an intelligent gripper and be used to grasp the weight of an unknown object as well as the position of the object in the gripper.

Carrying heavy objects in agricultural and industrial sites is the most basic labor, which requires a lot of energy. Many equipment such as crane, chain block, elevator, and forklift truck has been developed to reduce human power. Nevertheless, many tasks require human labor. In addition, rapid aging is increasing musculoskeletal diseases in industrial workers. Consequently, various muscle auxiliary wear robots and devices are being developed. In this study; a passive upper limbs exoskeleton (H-Frame) was developed to help carry over 20 kg of weight in industrial and agricultural sites. For the functional test of the developed H-Frame, tests were carried out for 20, 30, and 40 kg of each box. To measure the objective and numerical data of the H-Frame, various sensor values such as EMG (Electromyography), harness compression force sensor, and load cell value of side support and rope were measured. EMG and metabolic experiments were also performed on 8 subjects before and after wearing the device. The average value of the upper extremity muscle showed a 44% reduction effect after wearing. The device helped the wearer when carrying heavy objects. It could help prevent musculoskeletal diseases in industrial and agricultural fields.

Inertial measurement unit (IMU)-based 3D joint angle estimation have a wide range of important applications, among them, in gait analysis and exoskeleton robot control. Conventionally, the joint angle was determined via the estimation of 3D orientation of each body segment using 9-axis IMUs including 3-axis magnetometers. However, a magnetometer is limited by magnetic disturbance in the vicinity of the sensor, which highly affects the accuracy of the joint angle. Accordingly, this study aims to estimate the joint angle using the 6-axis IMU signals composed of a 3-axis accelerometer and a 3-axis gyroscope without a magnetometer. This paper proposes a recurrent neural network (RNN) model, which indirectly utilizes the joint kinematic constraint and thus estimates joint angles based on 6-axis IMUs without using a magnetometer signal. The performance of the proposed model was validated for a mechanical joint and human elbow joint, under magnetically disturbed environments. Experimental results showed that the proposed RNN approach outperformed the conventional approach based on a Kalman filter (KF), i.e., RNN 3.48° vs. KF 10.01° for the mechanical joint and RNN 7.39° vs. KF 21.27° for the elbow joint.

Tire-related crashes account for a large proportion of all types of car accidents. The causes of tire-related accidents are inappropriate tire temperature, pressure, and wear. Although temperature and pressure can be monitored easily with TPMS, there exists no system to monitor tire wear regularly. This paper proposes a system that can estimate tire wear using a 3-axis accelerometer attached to the tread inside the tire. This system utilizes axial acceleration, extracts feature from data acquired with the accelerometer and estimates tire wear by feature classification using machine learning. In particular, the proposed tire wear estimation method is designed to estimate tread depth in four types (7, 5.6, 4.2, and 1.4 mm) at speeds of 40, 50, and 60 kmph. Based on the data obtained during several runs on a test track, it has been found that this system can estimate the tread depth with reasonable accuracy.