Inertial Sensor Module Research Articles

Design and test of the actuation circuit of the inertial sensor for space gravitational wave detection based on hardware-in-the-loop simulation

The key scientific performance of the inertial sensor used for space gravitational wave detection is the residual acceleration noise of the test mass (TM), which is caused by the noise of inertial sensor components and external environmental noise. As the actuator of the inertial sensor, the performance of the actuator circuit affects the residual acceleration noise, but it is difficult to test the residual acceleration noise with a single actuation circuit. In this study, the actuator circuit is designed and a low-cost hardware-in-the-loop simulation system combined actuation circuit and other parts of the inertial sensor are established to test the residual acceleration noise. The system is composed of simulator electronics and software simulator, which establishes the connection between the actuation circuit with the Simulink model. The displacement sensing, probe, and drag-free model were built in Simulink to form a closed loop with the actuation circuit through the serial port. The simulation and test results show that the hardware-in-the-loop simulation system can preliminarily test the residual acceleration of TM, and the residual acceleration introduced by the actuation circuit is 2.5 × 10−13 m s−2 Hz−1/2. Compared with the conventional test method, the system has the advantages of low cost, simple implementation, and easy extension to test any single inertial sensor module.

A Novel Low-Cost ZMP Estimation Method for Humanoid Gait using Inertial Measurement Devices: Concept and Experiments

Estimation and control of zero-moment point (ZMP) is a widely used concept for planning the locomotion of bipedal robots and is commonly measured using integrated joint angle encoders and foot force sensors. Contemporary methods for ZMP measurement involve built-in contact sensors such as joint encoders or instrumented foot force sensors. This paper presents a novel approach for computing ZMP for a humanoid robot using inertial sensor-based wireless foot sensor modules (WFSMs). The developed WFSMs, strapped at different limb segments of a bipedal robot, measure lower limb joint angles in real time. The joint angle trajectories, further transformed into Cartesian position coordinates, are used for estimating the ZMP positions of humanoid robots using the planar biped model. The whole framework is presented through experimental studies for different real-life walking scenarios. Since the modules work based on the limb motion and inclination, any ground unevenness would be automatically reflected in the module output. Hence, this measurement process can be a convenient method for applications requiring humanoid control on uneven surfaces/outdoor terrains. To compare the performance of the proposed model, ZMP is simultaneously measured from inbuilt foot force sensors and joint encoders of the robot. Statistical tests exhibit a high linear correlation between the proposed method with integrated encoders and foot force sensors (Pearson’s coefficient, [Formula: see text]). Results indicate that ZMP estimated by WFSM is a viable method to monitor the dynamic gait balance of a humanoid robot and has potential application in outdoor and uneven terrains.

Development of an Inertial Sensor Module for Categorizing Anomalous Kicks in Taekwondo and Monitoring the Level of Impact.

In this study, an inertial measurement unit (IMU) sensor module and software algorithm were developed to identify anomalous kicks that should not be given scores in Taekwondo competitions. The IMU sensor module was manufactured with dimensions of 3 cm × 3 cm × 1.5 cm and consists of a high-g sensor for high acceleration measurement, a 9-DOF sensor, and a Wi-Fi module for wireless communication. In the experiment, anomalous kicks and normal kicks were collected by the IMU sensor module, and an AI model was trained. The anomalous kick determination accuracy of the trained AI model was found to be 97.5%. In addition, in order to check whether the strength of a blow can be distinguished using the IMU sensor module, an impact test was performed with a pendulum under the same test conditions as the impact sensor installed in the impact test setup, and the correlation coefficient was 0.99. This study is expected to contribute to improving scoring reliability by suggesting the possibility of discriminating anomalous kicks, which were difficult to judge in Taekwondo competitions, through the analysis of Taekwondo kicks using inertial data and impulses.

Towards Human Motion Tracking Enhanced by Semi-Continuous Ultrasonic Time-of-Flight Measurements.

Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson’s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab-settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of-flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking.

A Smart Parametric 3D Printing Hand Assistive Device With a Practical Physically Intervention Feedback Based on the Embedded IMU Sensor

Assistive devices are one of the crucial medical instruments for rehabilitation. Customized designs are required to match the geometry and dimensions of the affected sites of human bodies. The existing practice of assistive device development remains mostly a manual process, in which an experienced physiotherapist employs simple two-dimensional measurements and visual estimation to handcraft each device. The resulting devices not only vary substantially in fit to wearers, but also are not compatible to modern developmental technologies such as three-dimensional (3D) printing. This study incorporated substantial quantities of anthropometric data to construct a parametric 3D hand model. This study employed a customized assistive device - splint for carpal tunnel syndrome as a target object for testing the feasibility of the proposed method and verified the practicability through 3D printing implementation. Moreover, a wearable device embedded with inertial measurement unit sensor and vibration module will be integrated into the customized assistive device to offer a smart way for rehabilitation. With the help of this developed wearable device, continuously tracking user’s activity wirelessly and provide the real-time physically intervention feedback of vibration module based on back-end monitoring system platform. This new concept by integrating customized design and the IMU sensor and vibration module open a new window for the field of customized assistive device and improve its practical values.

Hybrid Stacking Integration of Multi-Sensor Navigation Module with WLFO and Thick Film Process

A hybrid stacking integration of multi-sensor navigation module with WLFO and thick film process was demonstrated. With wafer level fan-out packaging and stacked LTCC substrates, the GNSS, MEMS IMU, magnetometer and altimeter were integrated in one single module, the MEMS inertial sensors and ASIC chips were overlapping integrated with WLFO process as a multilayer wafer and diced, then the diced MEMS-ASIC modules, as well as the accessory circuits were pattered and mounted on several LTCC substrates as a drawer structure to accomplish the final system. The size of inertial sensor module is 10mm×5mm, and the entire navigation module is with size of 26.7cm3 and weight of 36g, meanwhile, with a high performance, the multi-sensor module can achieve low cost and high performance simultaneously. This module can be utilized for reliable navigation and gesture perception of micro UAV/UUV, automotive and other minimized platforms.

Robust Special Strategies Re sampling for Mobile Inertial Navigation Systems

The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.

Internet of Things (IoT) Fall Detection using Wearable Sensor

The IoT fall detection system detects the fall through the data classification of falling and daily living activity. It includes microcontroller board (Arduino Mega 2560), Inertial Measurement Unit sensor (Gy-521 mpu6050) and WI-FI module (ESP8266-01). There total ten (10) subjects in this project. The data of falling and non-falling (daily living activity) can be identified. The falling is the frontward fall, while the daily living activity includes standing, sitting, walking and crouching. K-nearest neighbour (k-NN) classifiers were used in the data classification. The accuracy of k-NN classifiers were 100% between falling and non-falling class. The feature was selected based on the percentage of accuracy of the k-NN classifier. The features of the Aareal.z (97.14%) and Angle.x (97.24%) were selected due to the good performance during the classification of the falling and non-falling class. The performance of the Aareal.z (58.41%) and Angle.x (57.78%) were satisfactory during the sub-classification of the non-falling class. Hence, the feature of Aareal.z and Angle.x were selected as the features which were implemented in the IoT fall detection device.

Evaluation of inertial Sensors and optical measurement methods for head and shoulder angle motion analysis

Abstract This paper describes the advantages and disadvantages of inertial sensors and stationary camera-based measuring methods for the application of detecting head and shoulder angles. For this purpose three inertial sensor modules from Shimmer Sensing and a Kinect 2 camera system are used. The recorded data of both systems are converted into a comparable form. Based on an evaluation of the recorded measurement data, the inertial sensor and the camera-based methods are compared with regard to their suitability for detecting movements in the head and shoulder area.

MEMS Inertial Sensor for Strata Stability Monitoring in Underground Mining: An Experimental Study

To investigate the fracture and deformation characteristics of the strata in underground mining as well as the effectiveness and sensitivity of the MEMS inertial sensor for strata stability monitoring, a low cost, small size, and easy implementation inertial MEMS sensor module was redeveloped. Sensor modules were installed on roof strata in an underground mining equivalent material simulation experiment. Then, monitoring signal of two modules near the middle and end section of caving strata was processed. The processed signal presents stepped change, and each step consists a vibration stage and a stable stage. Further analysis of each stage, a strategy to estimate the deformation and stability of strata, can be reached: the duration of each vibration stage and complete stage with rising trend indicates that the deformation of strata is growing to the ultimate state. In this study, this method could recognize the destructive deformation of strata at least 1 hour before the strata caving.

DISEÑO E IMPLEMENTACIÓN DE SISTEMA DE PLANTILLA PARA MEDICIÓN DE PRESIONES Y CINEMÁTICA DEL PIE

Este es un trabajo donde se desarrolló e implementó un sistema integrado que combina una plantilla instrumentada con sensores de presión y un módulo de sensores inerciales compuesto por un acelerómetro y un giroscopio, los cuales permitio la medición de variables baropodométricas (presiones plantares) y variables cinemáticas (aceleración, velocidad angular y desplazamiento angular) del pie. El sistema resultante le deja al especialista realizar un estudio biomecánico del pie a través del análisis estático y/o dinámico del movimiento y la visualización en pantalla de datos cuantitativos e información cualitativa, para la evaluación e intervención de los deportistas.

Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking

The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy. In the proposed method, a simple stance phase detector is designed to identify the stance phase of a gait cycle based on gyroscope measurements. For the non-stance phase during a gait cycle, an acceleration control variable derived from ZUPT information is introduced in the process model, while vector map information is taken as binary pseudo-measurements to further enhance position estimation accuracy and reduce uncertainty of walking trajectories. A particle filter is then designed to fuse ZUPT information and binary pseudo-measurements together. The proposed human position estimation method has been evaluated with closed-loop walking experiments in indoor and outdoor environments. Results of comparison study have illustrated the effectiveness of the proposed method for application scenarios with useful map information.

스노보드 시뮬레이터 운동 시 전문가와 비전문가의 하지 운동특성 분석

Abstract: In this study, joint angles of the lower extremity and tibial acceleration and angular velocity were measuredduring a snowboard simulator exercises in order to evaluate the skill of snowboarders. Ten unskilled and ten expertsnowboarders were recruited for the study. A three-dimensional motion capture system and two inertial sensor mod-ules were used to acquire joint movements, acceleration and angular velocity of the lower extremities during snow-board simulator exercises. Pattern variations were calculated to assess variations in the snowboard simulator motionof unskilled and expert snowboarders. Results showed that expert snowboarders showed greater range of motionin joint angles and greater peak to peak amplitude in acceleration and angular velocity for tibia than unskilled snow-boarders. The unskilled snowboarders did not show symmetrical shape(same magnitude but opposite direction) intibial angular velocity during two edge turns in snowboard simulator exercises. The expert snowboarders showedsmaller pattern variations for joint angle of lower extremity, tibial acceleration and tibial angular velocity thanunskilled snowboarders. Inertial sensor data and pattern variations during the snowboard simulator exercises couldbe useful to evaluate the skill of snowboarders.Key words: snowboard simulator, inertial sensor, kinematics, pattern variation

Use of an Inertial/Magnetic Sensor Module for Pedestrian Tracking During Normal Walking

The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot's orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method.

Use of Inertial Sensors to Predict Pivot-Shift Grade and Diagnose an ACL Injury During Preoperative Testing

Background: The pivot-shift (PS) examination is used to demonstrate knee instability and detect anterior cruciate ligament (ACL) injury. Prior studies using inertial sensors identified the ACL-deficient knee with reasonable accuracy, but none addressed the more difficult problem of using these sensors to determine whether a subject has an ACL deficiency and to correctly assign a PS grade to a patient’s knee. Hypothesis: Inertial sensor data recorded during a PS examination can accurately predict ACL deficiency and the PS score assigned by the examining physician. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A total of 32 patients with unilateral ACL deficiency and 29 with intact ACLs in both knees had inertial sensor modules strapped to the tibia and femur of each limb for preoperative PS testing under anesthesia. Support vector machine (SVM) methods assessed PS grades on the basis of these data, with the examiner’s clinical grading shift used as ground truth. A fusion of regression and SVM classification techniques diagnosed ACL deficiency. Results: The clinically determined PS grades of all 122 knees were as follows: 0 (n = 69), +1 (n = 23), +2 (n = 27), and +3 (n = 3). The SVM classification analysis was 77% accurate in correctly classifying these grades, with 98% of computed PS grades falling within ±1 grade of the clinically determined value. The system fusion algorithm diagnosed ACL deficiency in an individual with an overall accuracy of 97%. This method yielded 6% false negatives and 0% false positives. Conclusion: This study used inertial sensor technology with SVM algorithms to accurately determine clinically assigned PS grades in ACL-intact and ACL-deficient knees. By extending the assessment to a separate group of patients without ACL injury, the inertial sensor data demonstrated highly accurate diagnosis of ACL deficiency.

Self-Contained Pedestrian Tracking During Normal Walking Using an Inertial/Magnetic Sensor Module

This paper proposes a novel self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity updates, but also applies the stride information to further correct the acceleration double integration drifts and thus improves the tracking accuracy. In our method, a velocity control variable is designed in the process model, which is set to the average velocity derived from stride information in the swing (nonzero velocity) phases or zero in the stance (zero-velocity) phases. Stride-based position information is also derived as the pseudomeasurements to further improve the accuracy of the position estimates. An adaptive Kalman filter is then designed to fuse all the sensor information and pseudomeasurements. The proposed pedestrian tracking method has been extensively evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors, and have been shown to perform effectively for pedestrian tracking.

Exploration and Comparison of the Pre-impact Lead Time of Active and Passive Falls Based on Inertial Sensors

Research on falls in elderly people has a great social significance because of the rapidly growing of the aging population. The pre-impact lead time of fall (PLT) is an important part of the human fall theory. PLT is the longest time for a person who is going to fall to take action in order to prevent the fall or to reduce bodily injuries from the fall impact. However, there is no clear definition of PLT so far. There is also no comparative study for active and passive falls. In this study, we proposed a theoretical definition of the PLT, based on a new method of fall event division. We also compared the differences of PLT and the related angles between active and passive falls. Eight healthy adult subjects were arranged to perform three kinds of activities of daily living (sitting, walking and lying), and two kinds fall activities (active and passive) in three directions (forward, backward and lateral fall). Nine inertial sensor modules were used to measure the body segmental kinematic characteristics of each subject in our experimental activities. In this paper, a fall event was suggested to divide into three or four phases and then the critical phase could be divided into three periods (pre-impact, impact, and post-impact). Two fall models were developed for active and passive falls using acceleration data. The average value of PLT for active falls is about 514 ± 112 ms and it is smaller than the value for passive falls, which is 731 ± 104 ms. The longest PLTs were measured on the chest or waist instead of other locations, such as the thigh and shank. The PLTs of the three kinds of fall activities were slightly different, but there was a significant difference between two fall modes. The PLT showed the correlation to the body angle at the start of PLT, but it was uncorrelated at the end of PLT. The angles at the start of PLT had slight variations (<10 degrees) from the steady standing state except in passive forward falls (max 16 degrees) due to the self-control. The landing angles were significantly different in the both fall modes in all the three directions of fall, indicating the state of the trunk was uncertain when the hip contacted the ground. It can be concluded that it is feasible to prevent falls by using an early pre-impact fall alarm device; the present study provides important reference for development of pre-impact fall alarm devices.

Design and Realization of a Wearable Hip-Airbag System for Fall Protection

Nowadays falls are a serious problem for elderly people with the coming of aged society in the world. According to statistics,hip fracture accounts for the most of the deaths and costs of all the fall-induced injury. This paper presented an airbag system of hip protection, which included air source, sensors, microcontroller, gas circuit and airbags. A six-axial inertial sensor module that integrated an embedded three-axis MEMS accelerometer and three-axis MEMS gyroscope was used to collect human motion data, and a one-axis obliquity sensor was used to collect human angle data. The microcontroller was employed to recognize the activities of daily living (ADL) and falls based on fall detection algorithm and the collected data from sensors. The gas circuit was triggered once the event that the fall would be inevitable was confirmed by the microcontroller, then the compressed gas would fill into airbags through the gas circuit rapidly. Therefore, a buffer would appear between the human body and the ground before the body impacting the ground, which would reduce the impact of the human body. Compressed CO2 was selected as the air source after we tested several kinds of gas. A 16g CO2 pressurized cylinder could provide enough pressure and volume to inflate quickly the airbags. In order to improve the reliability of the gas circuit, a needle valve was optimized from the several designed structures by the experimental optimization methods. Finally, the airbag system was tested in various designed trials. The results indicated that the system gained the satisfaction for the design requirements and would be potential to apply to the protection of hip joint in the fall high-risk people in the future.

보행시점 검출을 위한 단일 각속도 센서모듈 시스템

The purpose of this study was to develop the inertial sensor module system to detect gait event using single angular rate sensor(gyroscope), and evaluate the accuracy of this system. This sensor module is attached at the heel and gait events such as heel strike, foot flat, heel off, toe off are detected by using proposed automatic event detection algorithm. The developed algorithm detect characteristics of pitch data of the gyroscope to find gait event. To evaluate the accuracy of system, 3D motion capture system was used and synchronized with sensor module system for comparison of gait event timings. In experiment, 6 subjects performed 5 trials level walking with 3 different conditions such as slow, preferred and fast. Results showed that gait event timings by sensor module system are similar to that by kinematic data, because maximum absolute errors were under 37.4msec regardless of gait velocity. Therefore, this system can be used to detect gait events. Although this system has advantages of small, light weight, long-term monitoring and high accuracy, it is necessary to improve the system to get other gait information such as gait velocity, stride length, step width and joint angles.

Physical sensor difference-based method and virtual sensor difference-based method for visual and quantitative estimation of lower limb 3D gait posture using accelerometers and magnetometers

An approach using a physical sensor difference-based algorithm and a virtual sensor difference-based algorithm to visually and quantitatively confirm lower limb posture was proposed. Three accelerometers and two MAG3s (inertial sensor module) were used to measure the accelerations and magnetic field data for the calculation of flexion/extension (FE) and abduction/adduction (AA) angles of hip joint and FE, AA and internal/external rotation (IE) angles of knee joint; then, the trajectories of knee and ankle joints were obtained with the joint angles and segment lengths. There was no integration of acceleration or angular velocity for the joint rotations and positions, which is an improvement on the previous method in recent literature. Compared with the camera motion capture system, the correlation coefficients in five trials were above 0.91 and 0.92 for the hip FE and AA, respectively, and higher than 0.94, 0.93 and 0.93 for the knee joint FE, AA and IE, respectively.