scholarly journals Measuring the difference in natural head position between the standing and sitting positions using an inertial measurement unit

2021 ◽  
Author(s):  
Kelly Billiaert ◽  
Mustafa Al‐Yassary ◽  
Gregory S. Antonarakis ◽  
Stavros Kiliaridis
Keyword(s):  
Inertial Measurement Unit ◽  
Head Position ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
The Difference

scholarly journals STUDY ON IMPROVEMENT OF ACCURACY IN INERTIAL PHOTOGRAMMETRY BY COMBINING IMAGES WITH INERTIAL MEASUREMENT UNIT

2016 ◽  
Vol XLI-B5 ◽  
pp. 501-505 ◽  
Author(s):  
Hideaki Kawasaki ◽  
Shojiro Anzai ◽  
Toshio Koizumi
Keyword(s):  
Calculation Method ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Feature Points ◽  
Error Growth ◽  
Actual Length ◽  
Inertial Measurement ◽  
Point Groups ◽  
The Difference ◽  
Time Accumulation

Inertial photogrammetry is defined as photogrammetry that involves using a camera on which an inertial measurement unit (IMU) is mounted. In inertial photogrammetry, the position and inclination of a shooting camera are calculated using the IMU. An IMU is characterized by error growth caused by time accumulation because acceleration is integrated with respect to time. <br><br> This study examines the procedure to estimate the position of the camera accurately while shooting using the IMU and the structure from motion (SfM) technology, which is applied in many fields, such as computer vision. <br><br> When neither the coordinates of the position of the camera nor those of feature points are known, SfM provides a similar positional relationship between the position of the camera and feature points. Therefore, the actual length of positional coordinates is not determined. If the actual length of the position of the camera is unknown, the camera acceleration is obtained by calculating the second order differential of the position of the camera, with respect to the shooting time. The authors had determined the actual length by assigning the position of IMU to the SfM-calculated position. Hence, accuracy decreased because of the error growth, which was the characteristic feature of IMU. In order to solve this problem, a new calculation method was proposed. Using this method, the difference between the IMU-calculated acceleration and the camera-calculated acceleration can be obtained using the method of least squares, and the magnification required for calculating the actual dimension from the position of the camera can be obtained. The actual length can be calculated by multiplying all the SfM point groups by the obtained magnification factor. This calculation method suppresses the error growth, which is due to the time accumulation in IMU, and improves the accuracy of inertial photogrammetry.

scholarly journals STUDY ON IMPROVEMENT OF ACCURACY IN INERTIAL PHOTOGRAMMETRY BY COMBINING IMAGES WITH INERTIAL MEASUREMENT UNIT

2016 ◽  
Vol XLI-B5 ◽  
pp. 501-505
Author(s):  
Hideaki Kawasaki ◽  
Shojiro Anzai ◽  
Toshio Koizumi
Keyword(s):  
Calculation Method ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Feature Points ◽  
Error Growth ◽  
Actual Length ◽  
Inertial Measurement ◽  
Point Groups ◽  
The Difference ◽  
Time Accumulation

Inertial photogrammetry is defined as photogrammetry that involves using a camera on which an inertial measurement unit (IMU) is mounted. In inertial photogrammetry, the position and inclination of a shooting camera are calculated using the IMU. An IMU is characterized by error growth caused by time accumulation because acceleration is integrated with respect to time. <br><br> This study examines the procedure to estimate the position of the camera accurately while shooting using the IMU and the structure from motion (SfM) technology, which is applied in many fields, such as computer vision. <br><br> When neither the coordinates of the position of the camera nor those of feature points are known, SfM provides a similar positional relationship between the position of the camera and feature points. Therefore, the actual length of positional coordinates is not determined. If the actual length of the position of the camera is unknown, the camera acceleration is obtained by calculating the second order differential of the position of the camera, with respect to the shooting time. The authors had determined the actual length by assigning the position of IMU to the SfM-calculated position. Hence, accuracy decreased because of the error growth, which was the characteristic feature of IMU. In order to solve this problem, a new calculation method was proposed. Using this method, the difference between the IMU-calculated acceleration and the camera-calculated acceleration can be obtained using the method of least squares, and the magnification required for calculating the actual dimension from the position of the camera can be obtained. The actual length can be calculated by multiplying all the SfM point groups by the obtained magnification factor. This calculation method suppresses the error growth, which is due to the time accumulation in IMU, and improves the accuracy of inertial photogrammetry.

scholarly journals A System on Intelligent Driver Drowsiness Detection Method

2018 ◽  
Vol 7 (3.4) ◽  
pp. 160
Author(s):  
Ugra Mohan Kumar ◽  
Devendra Singh ◽  
Sudhir Jugran ◽  
Pankaj Punia ◽  
Vinay Negi
Keyword(s):  
Machine Vision ◽  
Principle Component Analysis ◽  
Inertial Measurement Unit ◽  
Detection Method ◽  
Component Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Principle Component ◽  
Driver Drowsiness ◽  
The Difference

We actualized a fatigue driver recognition framework utilizing a mix of driver's state and driving conduct pointers. For driver's express, the framework observed the eyes' blinking rate and the flickering span. Fatigue drivers have these qualities higher than ordinary levels. We utilized a camera with machine vision procedures to find out and watch driver's blinking behavior. Harr's feature classifier was utilized to first find the eye's range, and once found, a layout coordinating was utilized to track the eye for fast preparing. For driving conduct, we gained the vehicle's state from inertial measurement unit and gas pedal sensors. The principle component analysis was utilized to choose the components that have high change. The difference esteems were utilized to separate weakness drivers, which are accepted to have higher driving exercises, from typical drivers.  

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.

scholarly journals Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2246
Author(s):  
Scott Pardoel ◽  
Gaurav Shalin ◽  
Julie Nantel ◽  
Edward D. Lemaire ◽  
Jonathan Kofman
Keyword(s):  
Early Detection ◽  
Plantar Pressure ◽  
Inertial Measurement Unit ◽  
Binary Classification ◽  
Freezing Of Gait ◽  
Pressure Sensors ◽  
Measurement Unit ◽  
Distribution Data ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

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