scholarly journals Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Micromachines ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1021
Author(s):  
Shipeng Han ◽  
Zhen Meng ◽  
Olatunji Omisore ◽  
Toluwanimi Akinyemi ◽  
Yuepeng Yan
Keyword(s):  
Random Error ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Random Noise ◽  
Development Trend ◽  
Sensor Technology ◽  
Application Range ◽  
Mems Accelerometers ◽  
Mems Gyroscopes

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

scholarly journals A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1304 ◽  
Author(s):  
Worsey ◽  
Espinosa ◽  
Shepherd ◽  
Thiel
Keyword(s):  
Best Practice ◽  
Performance Monitoring ◽  
Ad Hoc ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Magnetic Sensors ◽  
Sensor Technology ◽  
Future Research ◽  
Rowing Performance

Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into many coaches’ training regimes. There is an emerging trend to use inertial sensors for performance monitoring in rowing; however, the use and selection of the sensor used has not been appropriately reviewed. Previous literature assessed the sampling frequency, position, and fixing of the sensor; however, properties such as the sensor operating ranges, data processing algorithms, and validation technology are left unevaluated. To address this gap, a systematic literature review on rowing performance monitoring using inertial-magnetic sensors was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominantly used for measuring stroke quality and the sensors were used to instrument equipment rather than the athlete. The methodology for both selecting and implementing technology appeared ad hoc, with no guidelines for appropriate analysis of the results. This review summarizes a framework of best practice for selecting and implementing inertial sensor technology for monitoring rowing performance. It is envisaged that this review will act as a guide for future research into applying technology to rowing.

scholarly journals Using Inertial Sensors to Determine Head Motion—A Review

2021 ◽  
Vol 7 (12) ◽  
pp. 265
Author(s):  
Severin Ionut-Cristian ◽  
Dobrea Dan-Marius
Keyword(s):  
Human Activity ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Devices ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Head Motion ◽  
Technical Literature ◽  
Advantages And Disadvantages

Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

scholarly journals High-Efficiency Wavelet Compressive Fusion for Improving MEMS Array Performance

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1662 ◽  
Author(s):  
Siyuan Liang ◽  
Weilong Zhu ◽  
Feng Zhao ◽  
Congyi Wang
Keyword(s):  
Real Time ◽  
High Efficiency ◽  
Microelectromechanical Systems ◽  
Inertial Sensor ◽  
Low Cost ◽  
Rapid Development ◽  
Experimental Results ◽  
Zero Bias ◽  
Real Time Processing ◽  
Application Range

With the rapid development of microelectromechanical systems (MEMS) technology, low-cost MEMS inertial devices have been widely used for inertial navigation. However, their application range is greatly limited in some fields with high precision requirements because of their low precision and high noise. In this paper, to improve the performance of MEMS inertial devices, we propose a highly efficient optimal estimation algorithm for MEMS arrays based on wavelet compressive fusion (WCF). First, the algorithm uses the compression property of the multiscale wavelet transform to compress the original signal, fusing the compressive data based on the support. Second, threshold processing is performed on the fused wavelet coefficients. The simulation result demonstrates that the proposed algorithm performs well on the output of the inertial sensor array. Then, a ten-gyro array system is designed for collecting practical data, and the frequency of the embedded processor in our verification environment is 800 MHz. The experimental results show that, under the normal working conditions of the MEMS array system, the 100 ms input array data require an approximately 75 ms processing delay when employing the WCF algorithm to support real-time processing. Additionally, the zero-bias instability, angle random walk, and rate slope of the gyroscope are improved by 8.0, 8.0, and 9.5 dB, respectively, as compared with the original device. The experimental results demonstrate that the WCF algorithm has outstanding real-time performance and can effectively improve the accuracy of low-cost MEMS inertial devices.

scholarly journals Assessment of Scapulothoracic, Glenohumeral, and Elbow Motion in Adhesive Capsulitis by Means of Inertial Sensor Technology: A Within-Session, Intra-Operator and Inter-Operator Reliability and Agreement Study

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 876 ◽  
Author(s):  
Liesbet De Baets ◽  
Stefanie Vanbrabant ◽  
Carl Dierickx ◽  
Rob van der Straaten ◽  
Annick Timmermans
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
External Rotation ◽  
Adhesive Capsulitis ◽  
Movement Analysis ◽  
Elbow Flexion ◽  
Sensor Technology ◽  
Flexion Extension ◽  
Agreement Study ◽  
Joint Range

Adhesive capsulitis (AC) is a glenohumeral (GH) joint condition, characterized by decreased GH joint range of motion (ROM) and compensatory ROM in the elbow and scapulothoracic (ST) joint. To evaluate AC progression in clinical settings, objective movement analysis by available systems would be valuable. This study aimed to assess within-session and intra- and inter-operator reliability/agreement of such a motion capture system. The MVN-Awinda® system from Xsens Technologies (Enschede, The Netherlands) was used to assess ST, GH, and elbow ROM during four tasks (GH external rotation, combing hair, grasping a seatbelt, placing a cup on a shelf) in 10 AC patients (mean age = 54 (±6), 7 females), on two test occasions (accompanied by different operators on second occasion). Standard error of measurements (SEMs) were below 1.5° for ST pro-retraction and 4.6° for GH in-external rotation during GH external rotation; below 6.6° for ST tilt, 6.4° for GH flexion-extension, 7.1° for elbow flexion-extension during combing hair; below 4.4° for GH ab-adduction, 13° for GH in-external rotation, 6.8° for elbow flexion-extension during grasping the seatbelt; below 11° for all ST and GH joint rotations during placing a cup on a shelf. Therefore, to evaluate AC progression, inertial sensors systems can be applied during the execution of functional tasks.

scholarly journals Inertial Sensors for Performance Analysis in Combat Sports: A Systematic Review

Sports ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 28 ◽  
Author(s):  
Matthew Worsey ◽  
Hugo Espinosa ◽  
Jonathan Shepherd ◽  
David Thiel
Keyword(s):  
Performance Analysis ◽  
Best Practice ◽  
Performance Monitoring ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Sport Performance ◽  
Sensor Technology ◽  
Future Research ◽  
Combat Sports ◽  
Combat Sport

The integration of technology into training and competition sport settings is becoming more commonplace. Inertial sensors are one technology being used for performance monitoring. Within combat sports, there is an emerging trend to use this type of technology; however, the use and selection of this technology for combat sports has not been reviewed. To address this gap, a systematic literature review for combat sport athlete performance analysis was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominately used for measuring strike quality. The methodology for both selecting and implementing technology appeared ad-hoc, with no guidelines for appropriately analysing the results. This review summarises a framework of best practice for selecting and implementing inertial sensor technology for evaluating combat sport performance. It is envisaged that this review will act as a guide for future research into applying technology to combat sport.

Requirements for inertial sensor systems for measuring robot positions

Robotica ◽  
1990 ◽  
Vol 8 (2) ◽  
pp. 145-150 ◽  
Author(s):  
H. Janocha ◽  
D. Schmidt
Keyword(s):  
Autonomous Navigation ◽  
Moving Objects ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Sensor Technology ◽  
Inertial Measurement ◽  
Measurement Systems ◽  
Land Water ◽  
Intense Research

SummaryInertial Measurement Systems (IMS) allow the position calculation of moving objects without requiring outside information. For years the inertial 3-D coordinate measuring technique has been subject to intense research in geodesy and autonomous navigation of land-, water-and airborne vehicles. Because of these areas of application inertially-based systems have been designed for long term measuring only. Here we discuss the requirements that are imposed on inertial sensors in order for them to be used for the calculation of positions of robots. The use of modern sensor technology, combined with strategies for error correction, can result in substantial advantages when calculating robot positions independently from load and environment.

scholarly journals Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6377
Author(s):  
Roger Lee ◽  
Carole James ◽  
Suzi Edwards ◽  
Geoff Skinner ◽  
Jodi L. Young ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Upper Body ◽  
Sensor Technology ◽  
Vibrotactile Feedback ◽  
Level Of Evidence ◽  
Work Related ◽  
Wearable Inertial Sensors ◽  
Effects Of Feedback

Background: Wearable inertial sensor technology (WIST) systems provide feedback, aiming to modify aberrant postures and movements. The literature on the effects of feedback from WIST during work or work-related activities has not been previously summarised. This review examines the effectiveness of feedback on upper body kinematics during work or work-related activities, along with the wearability and a quantification of the kinematics of the related device. Methods: The Cinahl, Cochrane, Embase, Medline, Scopus, Sportdiscus and Google Scholar databases were searched, including reports from January 2005 to July 2021. The included studies were summarised descriptively and the evidence was assessed. Results: Fourteen included studies demonstrated a ‘limited’ level of evidence supporting posture and/or movement behaviour improvements using WIST feedback, with no improvements in pain. One study assessed wearability and another two investigated comfort. Studies used tri-axial accelerometers or IMU integration (n = 5 studies). Visual and/or vibrotactile feedback was mostly used. Most studies had a risk of bias, lacked detail for methodological reproducibility and displayed inconsistent reporting of sensor technology, with validation provided only in one study. Thus, we have proposed a minimum ‘Technology and Design Checklist’ for reporting. Conclusions: Our findings suggest that WIST may improve posture, though not pain; however, the quality of the studies limits the strength of this conclusion. Wearability evaluations are needed for the translation of WIST outcomes. Minimum reporting standards for WIST should be followed to ensure methodological reproducibility.

scholarly journals Monolithic Multi Degree of Freedom (MDoF) Capacitive MEMS Accelerometers

Micromachines ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 602 ◽  
Author(s):  
Zakriya Mohammed ◽  
Ibrahim Elfadel ◽  
Mahmoud Rasras
Keyword(s):  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Electrical Contact ◽  
High Sensitivity ◽  
Low Noise ◽  
Degree Of Freedom ◽  
Noise Floor ◽  
Mems Accelerometers ◽  
Capacitive Mems ◽  
Cross Axis

With the continuous advancements in microelectromechanical systems (MEMS) fabrication technology, inertial sensors like accelerometers and gyroscopes can be designed and manufactured with smaller footprint and lower power consumption. In the literature, there are several reported accelerometer designs based on MEMS technology and utilizing various transductions like capacitive, piezoelectric, optical, thermal, among several others. In particular, capacitive accelerometers are the most popular and highly researched due to several advantages like high sensitivity, low noise, low temperature sensitivity, linearity, and small footprint. Accelerometers can be designed to sense acceleration in all the three directions (X, Y, and Z-axis). Single-axis accelerometers are the most common and are often integrated orthogonally and combined as multiple-degree-of-freedom (MDoF) packages for sensing acceleration in the three directions. This type of MDoF increases the overall device footprint and cost. It also causes calibration errors and may require expensive compensations. Another type of MDoF accelerometers is based on monolithic integration and is proving to be effective in solving the footprint and calibration problems. There are mainly two classes of such monolithic MDoF accelerometers, depending on the number of proof masses used. The first class uses multiple proof masses with the main advantage being zero calibration issues. The second class uses a single proof mass, which results in compact device with a reduced noise floor. The latter class, however, suffers from high cross-axis sensitivity. It also requires very innovative layout designs, owing to the complicated mechanical structures and electrical contact placement. The performance complications due to nonlinearity, post fabrication process, and readout electronics affects both classes of accelerometers. In order to effectively compare them, we have used metrics such as sensitivity per unit area and noise-area product. This paper is devoted to an in-depth review of monolithic multi-axis capacitive MEMS accelerometers, including a detailed analysis of recent advancements aimed at solving their problems such as size, noise floor, cross-axis sensitivity, and process aware modeling.

Detection of Gait Event and Supporting Leg during Overground Walking with Mediolateral Swing Angle

2011 ◽  
Vol 145 ◽  
pp. 567-573 ◽  
Author(s):  
Je Nam Kim ◽  
Mun Ho Ryu ◽  
Yoon Seok Yang ◽  
Seong Hyun Kim
Keyword(s):  
Gait Analysis ◽  
Motion Tracking ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Tracking System ◽  
Reference Method ◽  
Vertical Acceleration ◽  
Elastic Band ◽  
Swing Angle

Walking is one of the basic human activities. Several well-defined, motion tracking systems have been used for gait analysis. However, these systems such as the optical motion tracking system are very expensive and limited to laboratory usage. Recently, microelectromechanical systems (MEMS)-based inertial sensors have made it possible to overcome these disadvantages. The aim of this study was to identify gait events and the supporting leg by measuring the mediolateral swing angle. An inertial sensor unit with a 3-axis accelerometer and 2-axis gyroscope was attached to the subject’s lower trunk using an elastic band. Five, healthy and young (20–29 yrs.) subjects participated in this experiment. Each walked twice along a straight, 25-m path at three different speeds. During each trial, the sensor transmitted signals to a PC via Bluetooth technology. In this study, gait events and the supporting leg were identified using the peak and sign of the mediolateral swing angle. The mediolateral swing angle was calculated using the integrated gyroscope signal. For comparison, a well-defined spatiotemporal gait analysis technique was also applied. In this reference method, the gait event was identified with the last peak of the vertical acceleration before the sign change from positive to negative. The supporting leg was identified using the sign of the mediolateral acceleration double integration. Identification of the supporting leg was difficult in the reference method because of the offset and gravity components in the mediolateral acceleration. However, the proposed method reported here, showed stable identification of gait events and the supporting leg. This study could be expanded to more detailed gait analysis with the additional fusion of a 3-axis acceleration, gyroscope and magnetometer.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

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