Honeywell Micro Electro Mechanical Systems (MEMS) Inertial Measurement Unit (IMU)

2012 ◽  
Author(s):  
Kristina Froyum ◽  
Scott Goepfert ◽  
Jens Henrickson ◽  
Jon Thorland
Keyword(s):  
Inertial Measurement Unit ◽  
Mechanical Systems ◽  
Measurement Unit ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement

scholarly journals A Novel Fault-Tolerant Navigation and Positioning Method with Stereo-Camera/Micro Electro Mechanical Systems Inertial Measurement Unit (MEMS-IMU) in Hostile Environment

Micromachines ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 626 ◽  
Author(s):  
Cheng Yuan ◽  
Jizhou Lai ◽  
Pin Lyu ◽  
Peng Shi ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Fault Tolerant ◽  
Mechanical Systems ◽  
Measurement Unit ◽  
Hostile Environment ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Stereo Camera ◽  
Positioning Method ◽  
Mems Imu

Visual odometry (VO) is a new navigation and positioning method that estimates the ego-motion of vehicles from images. However, VO with unsatisfactory performance can fail severely in hostile environment because of the less feature, fast angular motions, or illumination change. Thus, enhancing the robustness of VO in hostile environment has become a popular research topic. In this paper, a novel fault-tolerant visual-inertial odometry (VIO) navigation and positioning method framework is presented. The micro electro mechanical systems inertial measurement unit (MEMS-IMU) is used to aid the stereo-camera, for a robust pose estimation in hostile environment. In the algorithm, the MEMS-IMU pre-integration is deployed to improve the motion estimation accuracy and robustness in the cases of similar or few feature points. Besides, a dramatic change detector and an adaptive observation noise factor are introduced, tolerating and decreasing the estimation error that is caused by large angular motion or wrong matching. Experiments in hostile environment showing that the presented method can achieve better position estimation when compared with the traditional VO and VIO method.

scholarly journals Comparison of Seismocardiography Based Heart Rate Measurement Method

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Bayu Erfianto ◽  
Achmad Rizal ◽  
Vera Suryani
Keyword(s):  
Heart Rate ◽  
Inertial Measurement Unit ◽  
Digital Signal ◽  
Measurement Unit ◽  
Fiducial Point ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Packet Filter ◽  
Mode Decomposition ◽  
Time Period

The article describes a new alternative method of detecting the Aorta Open fiducial point based on digital signal processing formulated from the average seismocardiogram cycle obtained from the 6-degree-of-freedom Micro Electro-Mechanical Systems Inertial Measurement Unit, enabling estimation of heartbeat during heart muscle contraction without reference to electrocardiogram time period. Using the seismocardiography data obtained from the Inertial Measurement Unit, the authors then process the data using two methods: 1) Empirical Mode Decomposition and 2) Jerk signal, which is extracted as a first derivative of the Inertial Measurement Unit signal. As an example, we compare the two proposed methods to the existing method. Our Method 2 allows us to detect Aorta Open-Aorta Open value between 400ms and 450ms using Berkeley Packet Filter 5-15 Hz with dynamic peak threshold from the Hilbert envelope. Thus, the evaluation of the new method’s effectiveness is confirmed by the estimation of the Aorta Open-Aorta Open fiducial point as closer to the reference. Therefore, the result of our research, especially using jerk signal, can be considered a more accurate alternative for estimating heart rate or heartbeat based on seismocardiogram.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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