scholarly journals Thermal sensors improve wrist-worn position tracking

2019 ◽  
Author(s):  
Jake J. Son ◽  
Jon C. Clucas ◽  
Curt White ◽  
Anirudh Krishnakumar ◽  
Joshua T. Vogelstein ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Median Absolute Deviation ◽  
Repetitive Behaviors ◽  
The Body ◽  
Measurement Unit ◽  
Position Tracking ◽  
Thermal Sensors ◽  
Absolute Deviation ◽  
Inertial Measurement ◽  
Nail Biting

AbstractWearable devices provide a means of tracking hand position in relation to the head, but have mostly relied on wrist-worn inertial measurement unit sensors and proximity sensors, which are inadequate for identifying specific locations. This limits their utility for accurate and precise monitoring of behaviors or providing feedback to guide behaviors. A potential clinical application is monitoring body-focused repetitive behaviors (BFRBs), recurrent, injurious behaviors directed toward the body, such as nail biting and hair pulling, that are often misdiagnosed and undertreated. Here, we demonstrate that including thermal sensors achieves higher accuracy in position tracking when compared against inertial measurement unit and proximity sensor data alone. Our Tingle device distinguished between behaviors from six locations on the head across 39 adult participants, with high AUROC values (best was back of the head: median (1.0), median absolute deviation (0.0); worst was on the cheek: median (0.93), median absolute deviation (0.09)). This study presents preliminary evidence of the advantage of including thermal sensors for position tracking and the Tingle wearable device’s potential use in a wide variety of settings, including BFRB diagnosis and management.

scholarly journals A Sporting Chance

2011 ◽  
Vol 133 (07) ◽  
pp. 40-45
Author(s):  
Noel C. Perkins ◽  
Kevin King ◽  
Ryan McGinnis ◽  
Jessandra Hough
Keyword(s):  
Inertial Measurement Unit ◽  
Wireless Sensors ◽  
Microelectromechanical Systems ◽  
Angular Rate ◽  
Rigid Body Dynamics ◽  
The Body ◽  
Measurement Unit ◽  
Golf Club ◽  
Sports Training ◽  
Inertial Measurement

This article discusses using wireless sensors to improve sports training. One example of wireless sensors is inertial sensors that were first developed for automotive and military applications. They are tiny accelerometers and angular rate gyros that can be combined to form a complete inertial measurement unit. An inertial measurement unit (IMU) detects the three-dimensional motion of a body in space by sensing the acceleration of one point on the body as well as the angular velocity of the body. When this small, but rugged device is mounted on or embedded within sports gear, such as the shaft of a golf club, the IMU provides the essential data needed to resolve the motion of that equipment. This technology—and sound use of the theory of rigid body dynamics—is now being developed and commercialized as the ingredients in new sports training systems. It won’t be too long before microelectromechanical systems based hardware and sophisticated software combine to enable athletes at any level to get world-class training.

New Drift Reset Method for Pedestrian Tracking with a Low-Cost IMU

2013 ◽  
Vol 284-287 ◽  
pp. 2176-2180 ◽  
Author(s):  
Yao Tung Chuang ◽  
Cheng Ta Shen ◽  
Yi Lin Hsu ◽  
Sheng Wen Shih
Keyword(s):  
Inertial Measurement Unit ◽  
Temperature Compensation ◽  
Tracking System ◽  
Low Cost ◽  
Measurement Unit ◽  
Position Tracking ◽  
Pedestrian Tracking ◽  
Inertial Measurement ◽  
Rate Gyro ◽  
Global Reference Frame

In this work, we study the pedestrian position tracking problem using a foot-mounted inertial measurement unit (IMU). The IMU consists of a tri-axis rate-gyro, a tri-axis accelerometer and a tri-axis magnetometer. The magnetometer is used for constructing a global reference frame at the initial phase. The pedestrian orientation and position are estimated by integrating signals of the rate-gyro and the accelerometer. Since the integration operation usually introduces unbounded drift errors, a new drift reset method derived from a constant-ground-normal condition is proposed to compensate for the accumulated drift error. Furthermore, a temperature compensation (TC) method is described which can alleviate the rate-gyro error due to temperature variation. Real experiments have been conducted with six subjects to test the performance of the proposed method and the results show the promising performance of the proposed tracking system.

Low Cost Inertial Measurement Unit Application on Sub-Sea Launching: Signal Processing and Trajectory Algorithm

2010 ◽  
Author(s):  
Rodrigo Sauri Lavieri ◽  
Eduardo Aoun Tannuri ◽  
Andre´ L. C. Fujarra ◽  
Celso P. Pesce ◽  
Diego Cascelli Correˆa
Keyword(s):  
Automobile Industry ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
The Body ◽  
Measurement Unit ◽  
Final Position ◽  
Test Procedures ◽  
Positioning Systems ◽  
Inertial Measurement ◽  
Offshore Industry

Many situations in the Offshore Industry require equipment to be launched to the sea floor, becoming important to measure or to estimate their final position and/or to determine the complete trajectory. Some examples are the installation of anchorage devices, manifolds or production line supports. The main problem associated with the estimation of the position and the trajectory of the equipment is related to the fact that, systems such as GPSs and magnetometers cannot be used in subsea conditions. Gyrocompass and precise inertial sensors can be used, but they are expensive equipments and there is the risk of damaging during the launch process. The solution is to develop cost-effective inertial positioning systems that reach the operational requirements related to measuring accuracy. These equipments are based on MEMS (Micro-Electrical Mechanical Systems) inertial sensors that are relatively cheap. However, without the proper care, the signals obtained by these equipments present large levels of noise, bias and poor repeatability. The aim is to show a sequence of test procedures, treatment and processing of signals that leads one to know the position, attitude and trajectory of a submarine device. Furthermore, it allows the quantification of errors and, eventually, their sources. A commercial IMU (Inertial Measurement Unit) was chosen as a case study. It is equipped with MEMS sensors, usually adopted by the automobile industry. Tests with IMU were carried out intending to find the sensors scale factors, their bias and temperature sensitivity. Thereafter, the data were processed by two distinct algorithms. The first one is a simple algorithm that computes the attitude, azimuth at the final position and calculates the terminal velocity during the launch. The second one integrates the signal along all the movement by using quaternions algebra, resulting in the complete trajectory of the body. Discussions about the accuracy, applicability and limitations of each method are presented.

scholarly journals Inertial measurement unit-based pose estimation: Analyzing and reducing sensitivity to sensor placement and body measures

2019 ◽  
Vol 6 ◽  
pp. 205566831881345 ◽  
Author(s):  
Rezvan Kianifar ◽  
Vladimir Joukov ◽  
Alexander Lee ◽  
Sachin Raina ◽  
Dana Kulić
Keyword(s):  
Pose Estimation ◽  
Inertial Measurement Unit ◽  
Kinematic Model ◽  
Sensor Placement ◽  
Estimation Method ◽  
Calibration Procedure ◽  
The Body ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Inertial Measurement

Introduction Inertial measurement units have been proposed for automated pose estimation and exercise monitoring in clinical settings. However, many existing methods assume an extensive calibration procedure, which may not be realizable in clinical practice. In this study, an inertial measurement unit-based pose estimation method using extended Kalman filter and kinematic chain modeling is adapted for lower body pose estimation during clinical mobility tests such as the single leg squat, and the sensitivity to parameter calibration is investigated. Methods The sensitivity of pose estimation accuracy to each of the kinematic model and sensor placement parameters was analyzed. Sensitivity analysis results suggested that accurate extraction of inertial measurement unit orientation on the body is a key factor in improving the accuracy. Hence, a simple calibration protocol was proposed to reach a better approximation for inertial measurement unit orientation. Results After applying the protocol, the ankle, knee, and hip joint angle errors improved to [Formula: see text], and [Formula: see text], without the need for any other calibration. Conclusions Only a small subset of kinematic and sensor parameters contribute significantly to pose estimation accuracy when using body worn inertial sensors. A simple calibration procedure identifying the inertial measurement unit orientation on the body can provide good pose estimation performance.

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.

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