scholarly journals Reference Frame Unification of IMU-Based Joint Angle Estimation: The Experimental Investigation and a Novel Method

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1813
Author(s):  
Chunzhi Yi ◽  
Feng Jiang ◽  
Chifu Yang ◽  
Zhiyuan Chen ◽  
Zhen Ding ◽  
...  
Keyword(s):  
Reference Frame ◽  
Reference Frames ◽  
Joint Angle ◽  
Human Subjects ◽  
Local Magnetic Field ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Time Varying ◽  
Angle Estimation ◽  
Novel Method

Inertial measurement unit (IMU)-based joint angle estimation is an increasingly mature technique that has a broad range of applications in clinics, biomechanics and robotics. However, the deviations of different IMUs’ reference frames, referring to IMUs’ individual orientations estimating errors, is still a challenge for improving the angle estimation accuracy due to conceptual confusion, relatively simple metrics and the lack of systematical investigation. In this paper, we clarify the determination of reference frame unification, experimentally study the time-varying characteristics of reference frames’ deviations and accordingly propose a novel method with a comprehensive metric to unify reference frames. To be specific, we firstly define the reference frame unification (RFU) and distinguish it with drift correction that has always been confused with the term RFU. Secondly, we design a mechanical gimbal-based experiment to study the deviations, where sensor-to-body alignment and rotation-caused differences of orientations are excluded. Thirdly, based on the findings of the experiment, we propose a novel method to utilize the consistency of the joint axis under the hinge-joint constraint, gravity acceleration and local magnetic field to comprehensively unify reference frames, which meets the nonlinear time-varying characteristics of the deviations. The results on ten human subjects reveal the feasibility of our proposed method and the improvement from previous methods. This work contributes to a relatively new perspective of considering and improving the accuracy of IMU-based joint angle estimation.

scholarly journals Polymer Optical Fiber Goniometer: A New Portable, Low Cost and Reliable Sensor for Joint Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4293 ◽  
Author(s):  
Andressa Rezende ◽  
Camille Alves ◽  
Isabela Marques ◽  
Marco Silva ◽  
Eduardo Naves
Keyword(s):  
Optical Fiber ◽  
Electromagnetic Interference ◽  
Joint Angle ◽  
Low Cost ◽  
Measurement Unit ◽  
Joint Analysis ◽  
Polymer Optical Fiber ◽  
Important Indicator ◽  
Feedback Information ◽  
Novel Method

The quantitative measurement of an articular motion is an important indicator of its functional state and for clinical and pathology diagnoses. Joint angle evaluation techniques can be applied to improve sports performance and provide feedback information for prostheses control. Polymer optical fiber (POF) sensors are presented as a novel method to evaluate joint angles, because they are compact, lightweight, flexible and immune to electromagnetic interference. This study aimed to characterize and implement a new portable and wearable system to measure angles based on a POF curvature sensor. This study also aimed to present the system performance in bench tests and in the measurement of the elbow joint in ten participants, comparing the results with a consolidated resistive goniometer. Results showed high repeatability of sensors between cycles and high similarity of behavior with the potentiometer, with the advantage of being more ergonomic. The proposed sensor presented errors comparable to the literature and showed some advantages over other goniometers, such as the inertial measurement unit (IMU) sensor and over other types of POF sensors. This demonstrates its applicability for joint angle evaluation.

scholarly journals Toward inertial reference frames with the SIM observatory

2009 ◽  
Vol 5 (S261) ◽  
pp. 345-349 ◽  
Author(s):  
Valeri V. Makarov
Keyword(s):  
Reference Frame ◽  
Spherical Harmonics ◽  
Large Scale ◽  
Reference Frames ◽  
Motion Field ◽  
Time Varying ◽  
Crucial Importance ◽  
Physical Motion ◽  
Physical Phenomena ◽  
The Universe

AbstractThe SIM Lite Observatory is expected to provide a global astrometric reference frame surpassing the 1-μas accuracy threshold in some spherical harmonics. A range of time-varying physical distortions of the reference frame will become observable as large-scale perturbations of the proper motion field. I consider the main sources of the apparent and physical motion of reference objects, such as the aberration of light caused by the acceleration of SIM, long gravitational waves and hypothetical rotation of the Universe, and present some estimates of the astrometric sensitivity to these effects. I argue that a global solution and covariance analysis is of crucial importance for the SIM mission to differentiate the inevitable accidental and systematic zonal errors from real physical phenomena.

scholarly journals Wireless Epidermal Six-Axis Inertial Measurement Units for Real-Time Joint Angle Estimation

2020 ◽  
Vol 10 (7) ◽  
pp. 2240 ◽  
Author(s):  
Jae Keun Lee ◽  
Seung Ju Han ◽  
Kangil Kim ◽  
Yoon Hyuk Kim ◽  
Sangmin Lee
Keyword(s):  
Real Time ◽  
Human Body ◽  
Joint Angle ◽  
The Body ◽  
Measurement Unit ◽  
Processing Unit ◽  
Solder Paste ◽  
Angle Estimation ◽  
Patch Type ◽  
Inertial Measurement

Technological advances in wireless communications, miniaturized sensors, and low-power electronics have made it possible to implement integrated wireless body area networks (WBANs). These developments enable the applications of wireless wearable systems for diagnosis, health monitoring, rehabilitation, and dependency care. Across the current range of commercial wearable devices, the products are not firmly fixed to the human body. To minimize data error caused by movement of the human body and to achieve accurate measurements, it is essential to bring the wearable device close to the skin. This paper presents the implementation of a patch-type, six-axis inertial measurement unit (IMU) with wireless communication technology. The device comprises hard-electronic components on a stretchable elastic substrate for application in epidermal electronics, to collect precise data from the human body. Instead of the commonly used cleanroom processes of implementing devices on a stretchable substrate, a simple and inexpensive “cut-solder-paste” method is adopted to fabricate complex, convoluted interconnections. Thus, the signal distortions in the proposed device can be minimized during various physical activities and skin deformations when used in gait analysis. The inertial sensor data measured from the motion of the body can be sent in real-time via Bluetooth to any processing unit enabled with such a widespread standard wireless interface. For performance evaluation, the implemented device is mounted on a rotation plate in order to compare performance with the conventional product. In addition, an experiment on joint angle estimation is performed by attaching the device to an actual human body. The preliminary results of the device indicate the potential to monitor people in remote settings for applications in mobile health, human-computer interfaces (HCIs), and wearable robots.

Allocentric to Egocentric Spatial Switching: Impairment in aMCI and Alzheimer's Disease Patients?

2018 ◽  
Vol 15 (3) ◽  
pp. 229-236 ◽  
Author(s):  
Gennaro Ruggiero ◽  
Alessandro Iavarone ◽  
Tina Iachini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Reference Frame ◽  
Reference Frames ◽  
Memory Task ◽  
Spatial Representations ◽  
The Novel ◽  
Spatial Memory Task ◽  
Switching Condition ◽  
Normal Controls

Objective: Deficits in egocentric (subject-to-object) and allocentric (object-to-object) spatial representations, with a mainly allocentric impairment, characterize the first stages of the Alzheimer's disease (AD). Methods: To identify early cognitive signs of AD conversion, some studies focused on amnestic-Mild Cognitive Impairment (aMCI) by reporting alterations in both reference frames, especially the allocentric ones. However, spatial environments in which we move need the cooperation of both reference frames. Such cooperating processes imply that we constantly switch from allocentric to egocentric frames and vice versa. This raises the question of whether alterations of switching abilities might also characterize an early cognitive marker of AD, potentially suitable to detect the conversion from aMCI to dementia. Here, we compared AD and aMCI patients with Normal Controls (NC) on the Ego-Allo- Switching spatial memory task. The task assessed the capacity to use switching (Ego-Allo, Allo-Ego) and non-switching (Ego-Ego, Allo-Allo) verbal judgments about relative distances between memorized stimuli. Results: The novel finding of this study is the neat impairment shown by aMCI and AD in switching from allocentric to egocentric reference frames. Interestingly, in aMCI when the first reference frame was egocentric, the allocentric deficit appeared attenuated. Conclusion: This led us to conclude that allocentric deficits are not always clinically detectable in aMCI since the impairments could be masked when the first reference frame was body-centred. Alongside, AD and aMCI also revealed allocentric deficits in the non-switching condition. These findings suggest that switching alterations would emerge from impairments in hippocampal and posteromedial areas and from concurrent dysregulations in the locus coeruleus-noradrenaline system or pre-frontal cortex.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals Reference frames in spatial communication for navigation and sports: an empirical study in ultimate frisbee players

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Steven M. Weisberg ◽  
Anjan Chatterjee
Keyword(s):  
Reference Frame ◽  
Reference Frames ◽  
Spatial Information ◽  
Spatial Reference ◽  
Spatial Reference Frames ◽  
The North ◽  
Communication Task ◽  
Spatial Communication ◽  
Spatial Domains ◽  
Ultimate Frisbee

Abstract Background Reference frames ground spatial communication by mapping ambiguous language (for example, navigation: “to the left”) to properties of the speaker (using a Relative reference frame: “to my left”) or the world (Absolute reference frame: “to the north”). People’s preferences for reference frame vary depending on factors like their culture, the specific task in which they are engaged, and differences among individuals. Although most people are proficient with both reference frames, it is unknown whether preference for reference frames is stable within people or varies based on the specific spatial domain. These alternatives are difficult to adjudicate because navigation is one of few spatial domains that can be naturally solved using multiple reference frames. That is, while spatial navigation directions can be specified using Absolute or Relative reference frames (“go north” vs “go left”), other spatial domains predominantly use Relative reference frames. Here, we used two domains to test the stability of reference frame preference: one based on navigating a four-way intersection; and the other based on the sport of ultimate frisbee. We recruited 58 ultimate frisbee players to complete an online experiment. We measured reaction time and accuracy while participants solved spatial problems in each domain using verbal prompts containing either Relative or Absolute reference frames. Details of the task in both domains were kept as similar as possible while remaining ecologically plausible so that reference frame preference could emerge. Results We pre-registered a prediction that participants would be faster using their preferred reference frame type and that this advantage would correlate across domains; we did not find such a correlation. Instead, the data reveal that people use distinct reference frames in each domain. Conclusion This experiment reveals that spatial reference frame types are not stable and may be differentially suited to specific domains. This finding has broad implications for communicating spatial information by offering an important consideration for how spatial reference frames are used in communication: task constraints may affect reference frame choice as much as individual factors or culture.

scholarly journals Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

2019 ◽  
Vol 6 ◽  
pp. 205566831986854 ◽  
Author(s):  
Rob Argent ◽  
Sean Drummond ◽  
Alexandria Remus ◽  
Martin O’Reilly ◽  
Brian Caulfield
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Joint Angle ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Reference Standard ◽  
Inertial Measurement ◽  
Knee Joint Angle ◽  
Single Sensor

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

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