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

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.

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A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

Electronics ◽

10.3390/electronics8111304 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1304 ◽

Cited By ~ 3

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.

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Using Inertial Sensors to Determine Head Motion—A Review

Journal of Imaging ◽

10.3390/jimaging7120265 ◽

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.

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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 ◽

10.3390/s20030876 ◽

2020 ◽

Vol 20 (3) ◽

pp. 876 ◽

Cited By ~ 1

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.

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Research on the Taxonomy of Activity Recognition Based on Inertial Sensors

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.823.107 ◽

2013 ◽

Vol 823 ◽

pp. 107-110

Author(s):

Zi Ming Xiao ◽

Yu Long Shi ◽

Yong Xue ◽

Feng Hu ◽

Yu Chuan Wu

Keyword(s):

Activity Recognition ◽

Inertial Sensors ◽

Learning Algorithm ◽

Inertial Sensor ◽

Future Research ◽

Support Vector ◽

Bayesian Decision ◽

Classification Techniques ◽

Vector Machines ◽

Bayesian Decision Making

This paper introduces some techniques on classifying human activities with inertial sensors and point out a number of characteristics of classification algorithm. The goal of human activity recognition is to automatically analyze ongoing activities from people who wear inertial sensor. Initially, we provide introduce information about the activity recognition, such as the way of acquisition, sensors used and the steps of activity recognition using machine learning algorithm. Next, we focus on the classification techniques together with a detailed taxonomy, and the classification techniques implemented and compared in this study are: Decision Tree Algorithm (DTA), Bayesian Decision Making (BDM), Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Hidden Markov Model (HMM)[. Finally, we make a summarize about it investigate the directions for future research.

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

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217708237 ◽

2017 ◽

Vol 40 (9) ◽

pp. 2843-2854 ◽

Cited By ~ 4

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.

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Requirements for inertial sensor systems for measuring robot positions

Robotica ◽

10.1017/s0263574700007724 ◽

1990 ◽

Vol 8 (2) ◽

pp. 145-150 ◽

Cited By ~ 7

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.

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

Sensors ◽

10.3390/s21196377 ◽

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

Micromachines ◽

10.3390/mi11111021 ◽

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.

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Studi Literatur Human Activity Recognition (HAR) Menggunakan Sensor Inersia

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i6.3665 ◽

2021 ◽

Vol 5 (6) ◽

pp. 1193-1206

Author(s):

Humaira Nur Pradani ◽

Faizal Mahananto

Keyword(s):

Data Acquisition ◽

Activity Recognition ◽

Human Activity ◽

Inertial Sensors ◽

Inertial Sensor ◽

Computation Time ◽

Human Activity Recognition ◽

Sensor Data ◽

Future Research ◽

Literature Study

Human activity recognition (HAR) is one of the topics that is being widely researched because of its diverse implementation in various fields such as health, construction, and UI / UX. As MEMS (Micro Electro Mechanical Systems) evolves, HAR data acquisition can be done more easily and efficiently using inertial sensors. Inertial sensor data processing for HAR requires a series of processes and a variety of techniques. This literature study aims to summarize the various approaches that have been used in existing research in building the HAR model. Published articles are collected from ScienceDirect, IEEE Xplore, and MDPI over the past five years (2017-2021). From the 38 studies identified, information extracted are the overview of the areas of HAR implementation, data acquisition, public datasets, pre-process methods, feature extraction approaches, feature selection methods, classification models, training scenarios, model performance, and research challenges in this topic. The analysis showed that there is still room to improve the performance of the HAR model. Therefore, future research on the topic of HAR using inertial sensors can focus on extracting and selecting more optimal features, considering the robustness level of the model, increasing the complexity of classified activities, and balancing accuracy with computation time.

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A systematic review of prospective epidemiological research into injury and illness in Olympic combat sport

British Journal of Sports Medicine ◽

10.1136/bjsports-2016-097313 ◽

2017 ◽

Vol 52 (1) ◽

pp. 8-16 ◽

Cited By ~ 12

Author(s):

Sally J Bromley ◽

Michael K Drew ◽

Scott Talpey ◽

Andrew S McIntosh ◽

Caroline F Finch

Keyword(s):

Injury Severity ◽

Sport Performance ◽

Risk Of Bias ◽

Combat Sports ◽

Combat Sport ◽

Injury Rates ◽

Body Contact ◽

Lower Back ◽

Prevention Programmes ◽

Based Medicine

BackgroundCombat sports involve body contact through striking, kicking and/or throwing. They are anecdotally referred to as ‘dangerous’, yet long-term investigation into specific injury rates is yet to be explored.ObjectiveTo describe incidence and prevalence of injury and illness within Olympic combat sports and to investigate risk of bias of prospective injury and illness research within these sports.MethodsWe systematically searched literature published up until May 2016. We included prospective studies of injury/illness in elite combat athletes lasting more than 12 weeks. Risk of bias was assessed using a modified version of the Downs and Black checklist for methodological quality. Included studies were mapped to the Oxford Centre for Evidence-Based Medicine levels of evidence.ResultsNine studies were included, and most (n=6) had moderate risk of bias. Studies provided level 1/2b evidence that the most frequently injured areas were the head/face (45.8%), wrist (12.0%) and lower back (7.8%) in boxing; the lower back (10.9%), shoulder (10.2%) and knee (9.7%) in judo; the fingers (22.8%) and thigh (9.1%) in taekwondo; and the knee (24.8%), shoulder (17.8%) and head/face (16.6%) in wrestling. Heterogeneity of injury severity classifications and inconsistencies inexposure measures prevented any direct comparisons of injury severity/incidence across combat sports.ConclusionsThere is currently a lack of consensus in the collection of injury/illness data, limiting the development of prevention programmes for combat sport as a whole. However, sport-specific data that identify body areas with high injury frequency can provide direction to clinicians, enabling them to focus their attention on developing pathologies in these areas. In doing so, clinicians can enhance the practical elements of their role within the integrated combat sport performance team and assist in the regular update of surveillance records.

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