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.

A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

2013 ◽  
Vol 117 (1188) ◽  
pp. 111-132 ◽  
Author(s):  
T. L. Grigorie ◽  
R. M. Botez
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Minimum Variance ◽  
Measurement Unit ◽  
Time Step ◽  
Accelerometer Sensor ◽  
Sensor Model ◽  
Input Acceleration

Abstract This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions. A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal. To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

scholarly journals Reliability and Agreement of 3D Trunk and Lower Extremity Movement Analysis by Means of Inertial Sensor Technology for Unipodal and Bipodal Tasks

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 141 ◽  
Author(s):  
Rob Van der Straaten ◽  
Amber K. B. D. Bruijnes ◽  
Benedicte Vanwanseele ◽  
Ilse Jonkers ◽  
Liesbet De Baets ◽  
...  
Keyword(s):  
Lower Limb ◽  
Sagittal Plane ◽  
Inertial Sensor ◽  
Movement Analysis ◽  
Sensor Technology ◽  
Inertial Measurement ◽  
Sit To Stand ◽  
Movement Strategies ◽  
Measurement Units ◽  
Healthy Participants

This study evaluates the reliability and agreement of the 3D range of motion (ROM) of trunk and lower limb joints, measured by inertial measurement units (MVN BIOMECH Awinda, Xsens Technologies), during a single leg squat (SLS) and sit to stand (STS) task. Furthermore, distinction was made between movement phases, to discuss the reliability and agreement for different phases of both movement tasks. Twenty healthy participants were measured on two testing days. On day one, measurements were conducted by two operators to determine the within-session and between-operator reliability and agreement. On day two, measurements were conducted by the same operator, to determine the between-session reliability and agreement. The SLS task had lower within-session reliability and agreement compared with between-session and between-operator reliability and agreement. The reliability and agreement of the hip, knee, and ankle ROM in the sagittal plane were good for both phases of the SLS task. For both phases of STS task, within-session reliability and agreement were good, and between-session and between-operator reliability and agreement were lower in all planes. As both tasks are physically demanding, differences may be explained by inconsistent movement strategies. These results show that inertial sensor systems show promise for use in further research to investigate (mal)adaptive movement strategies.

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 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 Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach

2016 ◽  
Vol 2 (1) ◽  
pp. 715-718 ◽  
Author(s):  
David Graurock ◽  
Thomas Schauer ◽  
Thomas Seel
Keyword(s):  
Sensor Networks ◽  
Lower Limb ◽  
Healthy Subjects ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Lower Limbs ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Correct Pairing ◽  
Measurement Units

AbstractInertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.

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.

Wireless Input Technology Based on MEMS Inertial Measurement Unit - A Survey

2017 ◽  
Vol 870 ◽  
pp. 79-84
Author(s):  
Zhen Xian Fu ◽  
Guang Ying Zhang ◽  
Yu Rong Lin ◽  
Yang Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Propagation Model ◽  
Measurement Unit ◽  
Input Device ◽  
Operational Environment ◽  
Inertial Measurement ◽  
Mems Imu

Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

2017 ◽  
Vol 40 (9) ◽  
pp. 2843-2854 ◽  
Author(s):  
Renu Bhardwaj ◽  
Neelesh Kumar ◽  
Vipan Kumar
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Mechanical Systems ◽  
Stochastic Modelling ◽  
Future Research ◽  
Mems Devices ◽  
Specific System ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Calibration Methods

Micro-electro-mechanical systems (MEMS) technology-based accelerometers and gyroscopes are small size, mass produced, low cost inertial sensors, which are now being used in aerospace, underwater vehicles, automotive, robotics, mobiles, gaming consoles, prosthetic devices and many other applications. MEMS inertial sensors are available in many grades in market and selecting the appropriate grade sensor is very important. Owing to interaction of different types of energies, different noises are generated in MEMS devices; these noises cause significant change in output and the first section of this paper illustrates that. In application, where MEMS inertial sensors are used, the accuracy, repeatability and reproducibility of inertia measurement is probed primarily by complex testing, using extensive range of physical stimuli. Noises in inertial measurement are generally dealt by designing a unit measurement model. Noises are treated as additive error in linear unit model and are modelled using various techniques so that errors can be compensated to improve the accuracy. This paper reviews the theory, framework and methodology used in the error model of a MEMS inertial sensor and stochastic modelling of measurement. Experimental results from the most commonly used Allan variance techniques are discussed. Error modelling methodology, consisting of testing and calibration methods, designing thermal model, stochastic modelling and parameter estimation techniques, is illustrated. Figures and tables under each section summarize features, merits, limitation and future research scope. This paper should serve as a single reference for researchers and engineers working on application specific system design and instrumentation using MEMS inertial sensors. Conclusion from the study should help in selecting the appropriate grade of sensor as well as the best error modelling as per the trade-off existing between accuracy and development cost of error modelling.

A Comprehensive Overview of Inertial Sensor Calibration Techniques

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Shashi Poddar ◽  
Vipan Kumar ◽  
Amod Kumar
Keyword(s):  
High Precision ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Comprehensive Overview ◽  
Inertial Measurement ◽  
Calibration Techniques

Inertial measurement unit (IMU) comprising of the accelerometer and gyroscope is prone to various deterministic errors like bias, scale factor, and nonorthogonality, which need to be calibrated carefully. In this paper, a survey has been carried out over different calibration techniques that try to estimate these error parameters. These calibration schemes are discussed under two broad categories, that is, calibration with high-end equipment and without any equipment. Traditional calibration techniques use high-precision equipment to generate references for calibrating inertial sensors and are generally laboratory-based setup. Inertial sensor calibration without the use of any costly equipment is further studied under two subcategories: ones based on multiposition method and others with Kalman filtering framework. Later, a brief review of vision-based inertial sensor calibration schemes is also provided in this work followed by a discussion which indicates different shortcomings and future scopes in the area of inertial sensor calibration.

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.

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