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.

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review

Hand ◽  
2021 ◽  
pp. 155894472110146
Author(s):  
Francisco R. Avila ◽  
Rickey E. Carter ◽  
Christopher J. McLeod ◽  
Charles J. Bruce ◽  
Davide Giardi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Range Of Motion ◽  
Health Care Professionals ◽  
Inertial Sensors ◽  
Measurement Techniques ◽  
Infrared Camera ◽  
Wearable Devices ◽  
Sensor Technology ◽  
Accuracy Evaluation ◽  
Wearable Sensor

Background Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one. Methods We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry. Results Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices. Conclusion Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera–based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

scholarly journals Detection of Cardiac Arrhythmia using Machine Learning Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 11704-11707
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmia ◽  
Sinus Node ◽  
Research Work ◽  
Heart Rhythm ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Medical Attention ◽  
Advantages And Disadvantages

Cardiac Arrhythmia is a type of condition a human being suffers from abnormal heart rhythm. This is experienced due to the malfunctioning of electrical impulses that coordinate the heartbeat. When this happens the heartbeats slow/ fast more precisely irregularly. The rhythm of the heart is controlled by a major node called the sinus node which is present at the top of the heart, triggers the electrical pulses which make the heart to beat and pumping of blood to the body. Some of the symptoms of Cardiac Arrhythmia are fainting, unconsciousness, shortness of breath, unexpected functioning of the heart. It leads to death in minutes if medical attention is not provided. To diagnose this doctor, require to study the heart recordings evaluate heartbeats from different parts of the body accurately. It takes a lot of time to evaluate so based on the research work contributed in this field we try to propose a different approach to the same. In this paper, we compare different machine learning techniques and algorithms proposed by different authors and understand the advantages and disadvantages of the system and to bring a new system in place of the existing system where all have used the same ECG recordings from the same database of MIT-BIH. With the initial research work done by us we found out that the use of Phonocardiogram Recordings (PCG) provides more fidelity and accurate compared to ECG recordings. With the initial stage of work, we take the PCG recordings dataset and convert it to a spectrogram image and apply a convolutional neural network to predict the normal or abnormal heartbeat

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.

Can You See It?

2021 ◽  
Vol 25 (2) ◽  
pp. 38-42
Author(s):  
Hyeokhyen Kwon ◽  
Catherine Tong ◽  
Harish Haresamudram ◽  
Yan Gao ◽  
Gregory D. Abowd ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensors ◽  
Human Subjects ◽  
Wearable Devices ◽  
Human Activity Recognition ◽  
Training Data ◽  
Considerable Time ◽  
Administrative Work ◽  
Everyday Activities

Today's smartphones and wearable devices come equipped with an array of inertial sensors, along with IMU-based Human Activity Recognition models to monitor everyday activities. However, such models rely on large amounts of annotated training data, which require considerable time and effort for collection. One has to recruit human subjects, define clear protocols for the subjects to follow, and manually annotate the collected data, along with the administrative work that goes into organizing such a recording.

Human Motion Capture with Sparse Inertial Sensors and Video

2020 ◽  
Author(s):  
Timo von Marcard
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Hybrid Approach ◽  
Human Motion ◽  
The Body ◽  
Sensor Data ◽  
Discrete Optimization Problem ◽  
Global Optimization Problem

This thesis explores approaches to capture human motions with a small number of sensors. In the first part of this thesis an approach is presented that reconstructs the body pose from only six inertial sensors. Instead of relying on pre-recorded motion databases, a global optimization problem is solved to maximize the consistency of measurements and model over an entire recording sequence. The second part of this thesis deals with a hybrid approach to fuse visual information from a single hand-held camera with inertial sensor data. First, a discrete optimization problem is solved to automatically associate people detections in the video with inertial sensor data. Then, a global optimization problem is formulated to combine visual and inertial information. The propose  approach enables capturing of multiple interacting people and works even if many more people are visible in the camera image. In addition, systematic inertial sensor errors can be compensated, leading to a substantial in...

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 Feature Selection for Work Recognition and Working Motion Measurement

2018 ◽  
Vol 30 (5) ◽  
pp. 706-716 ◽  
Author(s):  
Saori Miyajima ◽  
Takayuki Tanaka ◽  
Natsuki Miyata ◽  
Mitsunori Tada ◽  
Masaaki Mochimaru ◽  
...  
Keyword(s):  
Feature Selection ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Feature Selection Method ◽  
The Body ◽  
Support Vector ◽  
Body Parts ◽  
Care Services ◽  
Physical Burden ◽  
New Feature

As the demand for nursing care services is growing, the physical burden involved in caregiving has drawn widespread attention. To mitigate the physical burden in caregiving, we have to recognize what kind of work and problems are involved in each caregiving task. To identify the problems involved in caregiving, we need to recognize the work and analyze its workload. Aiming to reduce the burden on the waist during caregiving tasks, we are developing inertial sensor suits for measuring the working motions. With the developed method, the burden on the waist is estimated from the waist posture. Considering its use in practical caregiving sites, the number of inertial sensors should be the minimum necessary, which depends on the number of body parts where to measure the posture. In this study, we select the body parts to achieve the two above-mentioned goals: to recognize the work involved in caregiving and capture the waist posture. A support vector machine (SVM) is used to recognize the work. Its conventional method of selecting the features on which to recognize the work only considers the recognition accuracy and does not sufficiently meet the needs for measuring the postures. Therefore, we propose a new feature-selection method, which can evaluate the waist-posture measuring accuracy and can make forward feature selections in the same manner as the conventional wrapper method. We have verified the effectiveness of the proposed method by measuring simple simulated work motions.

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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