Segmentation of Human Body Movement Using Inertial Measurement Unit

2013 ◽  
Author(s):  
Takashi Aoki ◽  
Gentiane Venture ◽  
Dana Kulic
Keyword(s):  
Human Body ◽  
Inertial Measurement Unit ◽  
Body Movement ◽  
Measurement Unit ◽  
Inertial Measurement

scholarly journals Stretchable, Patch-Type, Wireless, 6-axis Inertial Measurement Unit for Mobile Health Monitoring

2020 ◽  
Vol 14 ◽  
pp. 16-21 ◽  
Author(s):  
Jae Keun Lee ◽  
Kangil Kim ◽  
Sangmin Lee
Keyword(s):  
Mobile Health ◽  
Human Body ◽  
Health Monitoring ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Biometric Data ◽  
Patch Type ◽  
Inertial Measurement ◽  
Dependency Care ◽  
Mobile Health Monitoring

Wearable devices which measure and transfer signals from the human body can provide useful biometric data for various biomedical applications. In this paper, we present an implementation of the advanced Inertial Measurement Unit (IMU) with wireless communication technology for mobile health monitoring. The device consists of rigid silicon-based components on a flexible/stretchable substrate for applications in epidermal electronic devices to collect precise data from the human body. Using the Bluetooth Low Energy (BLE) System-on-a-chip (SoC), the device can be miniaturized and portable, and the collected data can be processed with low power consumption. The dimensions of the implemented system are approximately 40 mm × 40 mm × 100 mm. Also, the device can be attached closely to human skin, which results in minimized signal distortion due to body movements or skin deformations. In order to achieve device flexibility and stretch ability, the interconnection wires are designed as serpentine-shaped structures on a stretchable substrate. The previously reported “cut-and-paste” method is utilized to fabricate the device that produces complex, twisty interconnections with thin metal sheets. The implemented patch-type, wireless, 6-axis IMU is expected to have potential in various applications, such as health monitoring, dependency care, and daily lifelogging.

scholarly journals Postural Stability Evaluation of Patients Undergoing Vestibular Schwannoma Microsurgery Employing the Inertial Measurement Unit

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Patrik Kutilek ◽  
Zdenek Svoboda ◽  
Ondrej Cakrt ◽  
Karel Hana ◽  
Martin Chovanec
Keyword(s):  
Vestibular Schwannoma ◽  
Postural Stability ◽  
Inertial Measurement Unit ◽  
Balance Control ◽  
Body Movement ◽  
Quiet Standing ◽  
Measurement Unit ◽  
Major Drawback ◽  
Inertial Measurement ◽  
Physical Quantities

The article focuses on a noninvasive method and system of quantifying postural stability of patients undergoing vestibular schwannoma microsurgery. Recent alternatives quantifying human postural stability are rather limited. The major drawback is that the posturography system can evaluate only two physical quantities of body movement and can be measured only on a transverse plane. A complex movement pattern can be, however, described more precisely while using three physical quantities of 3-D movement. This is the reason why an inertial measurement unit (Xsens MTx unit), through which we obtained 3-D data (three Euler angles or three orthogonal accelerations), was placed on the patient’s trunk. Having employed this novel method based on the volume of irregular polyhedron of 3-D body movement during quiet standing, it was possible to evaluate postural stability. To identify and evaluate pathological balance control of patients undergoing vestibular schwannoma microsurgery, it was necessary to calculate the volume polyhedron using the 3-D Leibniz method and to plot three variables against each other. For the needs of this study, measurements and statistical analysis were made on nine patients. The results obtained by the inertial measurement unit showed no evidence of improvement in postural stability shortly after surgery (4 days). The results were consistent with the results obtained by the posturography system. The evaluated translation variables (acceleration) and rotary variables (angles) measured by the inertial measurement unit correlate strongly with the results of the posturography system. The proposed method and application of the inertial measurement unit for the purpose of measuring patients with vestibular schwannoma appear to be suitable for medical practice. Moreover, the inertial measurement unit is portable and, when compared to other traditional posturography systems, economically affordable. Inertial measurement units can alternatively be implemented in mobile phones or watches.

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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