A new calibration method for MEMS inertial sensor module

The lighting of the operating/surgical site depends on the quality of the lighting from the overhead light source and the reflection from the curtain. Light measurement on the operating table is very necessary because it generates light that is irradiated into the cutting wound without dazzling the cutting surface so that pathological conditions can be recognized and must provide depth contrast and anatomical relationships, to ensure this proper calibration method is needed. Long-term use of medical devices can cause changes in accuracy. Therefore, the author makes a tool to measure the intensity of light which is equipped with a distance meter. The purpose of this study was to develop a measuring instrument for measuring the intensity of light in operating lamps, namely a luxmeter by making Luxmeter equipped with a TFT Display Distance Sensor. This tool uses an ultrasonic sensor HC-SR04 to measure the distance between the light source and the sensor module and the MAX44009 sensor to measure the light intensity of the operating lamp displayed on the TFT screen. Based on the module distance setting to the roll meter, the distance error value for the measurement of the Surabaya electromedical engineering workshop lamp at the 75 cm roll meter distance setting is 0.0127% for the 100 cm roll meter distance setting is 0.0045%. The error rate of the light intensity module on the results of the measurement of light intensity on the luxmeter by setting the roll meter distance of 75 cm between the tool and the lamp of the electromedical engineering workshop is getting an error value of 0.082% lux and for the light intensity on the results of the measurement of light intensity on the luxmeter with a roll meter distance setting of 100 cm between the tool and the lamp in the electromedical engineering workshop, that is, the error value of lux is 0.055%. The design of a luxmeter equipped with a proximity sensor can measure the intensity of light and the distance between the tool and the light source and can assist in the learning process with a more effective Luxmeter design that will assist electromedics in testing operating lamps in hospitals to be more efficient.

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Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Sensors ◽

10.3390/s19112474 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2474 ◽

Cited By ~ 7

Author(s):

Sébastien Cordillet ◽

Nicolas Bideau ◽

Benoit Bideau ◽

Guillaume Nicolas

Keyword(s):

Knee Joint ◽

Inertial Sensor ◽

External Rotation ◽

Calibration Method ◽

Calibration Procedure ◽

Angle Measurement ◽

Measurement Unit ◽

Joint Angles ◽

Calibration Methods ◽

Flexion Extension

This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the ‘cycling’ method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis.

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Online IMU Self-Calibration for Visual-Inertial Systems

Sensors ◽

10.3390/s19071624 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1624 ◽

Cited By ~ 6

Author(s):

Yao Xiao ◽

Xiaogang Ruan ◽

Jie Chai ◽

Xiaoping Zhang ◽

Xiaoqing Zhu

Keyword(s):

Inertial Sensor ◽

Low Cost ◽

Optimization Methods ◽

Calibration Method ◽

Measurement Unit ◽

Intrinsic Parameters ◽

Self Calibration ◽

Calibration Problem ◽

System States ◽

Inertial Systems

Low-cost microelectro mechanical systems (MEMS)-based inertial measurement unit (IMU) measurements are usually affected by inaccurate scale factors, axis misalignments, and g-sensitivity errors. These errors may significantly influence the performance of visual-inertial methods. In this paper, we propose an online IMU self-calibration method for visual-inertial systems equipped with a low-cost inertial sensor. The goal of our method is to concurrently perform 3D pose estimation and online IMU calibration based on optimization methods in unknown environments without any external equipment. To achieve this goal, we firstly develop a novel preintegration method that can handle the IMU intrinsic parameters error propagation. Then, we frame IMU calibration problem into general factors so that we can easily integrate the factors into the current graph-based visual-inertial frameworks and jointly optimize the IMU intrinsic parameters as well as the system states in a big bundle. We evaluate the proposed method with a publicly available dataset. Experimental results verify that the proposed approach is able to accurately calibrate all the considered parameters in real time, leading to significant improvement of estimation precision of visual-inertial system (VINS) compared with the estimation results with offline precalibrated IMU measurements.

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

Shock and Vibration ◽

10.1155/2018/4895862 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Kaizhi Zhang ◽

Songtao Ji ◽

Yang Zhang ◽

Jie Zhang ◽

Ruikai Pan

Keyword(s):

Underground Mining ◽

Inertial Sensor ◽

Low Cost ◽

Equivalent Material ◽

Ultimate State ◽

Stability Monitoring ◽

Mems Sensor ◽

Sensor Module ◽

Roof Strata ◽

Stable Stage

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.

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Research on Wearable Human Motion Capture and Virtual Control

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.686.121 ◽

2014 ◽

Vol 686 ◽

pp. 121-125

Author(s):

Fei Jiang ◽

Ying Jie Yu ◽

Da Wei Yan

Keyword(s):

Real Time ◽

Motion Capture ◽

Inertial Sensor ◽

Calibration Method ◽

Human Motion ◽

Skeleton Model ◽

Body Attitude ◽

Human Limb ◽

Coordinate Conversion ◽

Skeletal Model

This paper designed the posture initialization calibration method by the inertial sensor in human limb movement with any attitude toward. By initializing the target specific actions can be implemented to identify timing corresponding sensors and joint, and calculate the coordinate transformation relation of human skeletal coordinates corresponding to each inertial sensor's coordinate system and the 3D human skeleton model. Then through the coordinate conversion of inertial sensor attitude coordinates and depth first traversal calculation on human skeletal tree, real-time update of human motion body attitude data, driven simulation of human skeletal model by human motion, realize the real-time tracking of motion capture.

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