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.

Using Inertial Sensor System to Measure the Workspace of the Surgeon's Upper Limbs during Operations

2015 ◽  
Vol 772 ◽  
pp. 329-333
Author(s):  
Ali Soroush ◽  
Farzam Farahmand
Keyword(s):  
Upper Limb ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Kinematic Model ◽  
Experimental Results ◽  
Upper Body ◽  
Upper Limbs ◽  
Surgical Robots ◽  
Raw Data ◽  
Wearable Inertial Sensors

The aim of this study was to determine the workspace of surgeon's body for designing more efficient surgical robots in the operation rooms. Five wearable inertial sensors were placed near the wrist and elbow joints and also on the thorax of surgeons to track the orientation of upper limb. Assuming that the lengths of five segments of an upper limb were known, measurements of the inertial sensors were used to determine the position of the wrist and elbow joints via an established kinematic model. subsequently, to assess the workspace of surgeon upper body, raw data were collected in the arthroscopy and laparoscopy operations. Experimental results demonstrated that the workspaces of surgeon's joints are limited and predefined. The results can be used for designing surgical robots and surgeon body supports.

scholarly journals Upper Body Pose Estimation Using Wearable Inertial Sensors and Multiplicative Kalman Filter

2020 ◽  
Vol 20 (1) ◽  
pp. 492-500 ◽  
Author(s):  
Tommaso Lisini Baldi ◽  
Francesco Farina ◽  
Andrea Garulli ◽  
Antonio Giannitrapani ◽  
Domenico Prattichizzo
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Inertial Sensors ◽  
Upper Body ◽  
Wearable Inertial Sensors ◽  
Body Pose Estimation

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 An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 360-381
Author(s):  
Matthew T. O. Worsey ◽  
Hugo G. Espinosa ◽  
Jonathan B. Shepherd ◽  
David V. Thiel
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Significant Statistical Difference ◽  
Sensor Configuration ◽  
Wearable Inertial Sensors ◽  
Machine Learning Models

Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing.

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 Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Abeer A. Badawi ◽  
Ahmad Al-Kabbany ◽  
Heba A. Shaban
Keyword(s):  
Feature Selection ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Real Life ◽  
Human Gait ◽  
Body Sensor Networks ◽  
Sequential Feature Selection ◽  
Wearable Inertial Sensors ◽  
Robust Systems ◽  
Remote Health

This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.

Tools for the Care of Elderly People Applying Information and Communications Technology to Fall Risk Assessment

2014 ◽  
Vol 96 ◽  
pp. 73-77
Author(s):  
Toshiyo Tamura ◽  
Takumi Yosimura
Keyword(s):  
Angular Velocity ◽  
Fall Risk ◽  
Inertial Sensors ◽  
Information And Communications Technology ◽  
Information And Communication ◽  
The Subject ◽  
Care Of Elderly ◽  
Wearable Inertial Sensors ◽  
And Robotics

Assistive devices have been used to improve the quality of life in elderly society, and information and communication technology (ICT) and robotics have been applied extensively to this end. Falls are a common problem and fall risk assessments are created. This study involved assessment related to an application of information technology. First, to monitor and record falls during daily activities, wearable inertial sensors were used . The threshold of acceleration was used to detect falls. To prevent injury during falls, we also developed a wearable airbag system using an accelerometer, angular velocity, and airbags. The subjects wore the airbag vest with a motion detection belt. When the subject fell, the combination of acceleration and angular velocity signals detected the fall and inflated the airbag.

scholarly journals KINEMATIC ANALYSIS BY GENDER IN DIFFERENT JUMP TESTS BASED ON A SMARTPHONE INERTIAL SENSOR

2018 ◽  
Vol 24 (4) ◽  
pp. 263-267
Author(s):  
Mateos-Angulo Alvaro ◽  
Galán-Mercant Alejandro ◽  
Cuesta-Vargas Antonio Ignacio
Keyword(s):  
Motor Development ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Vertical Jump ◽  
Linear Acceleration ◽  
Cross Sectional Study ◽  
Knee Extensors ◽  
Level Of Evidence ◽  
Cross Sectional ◽  
Squat Jump

ABSTRACT Introduction: Vertical jump tests can be used as estimators of muscular power, physical capacity, motor development and functional capacity. The ability to jump can be analyzed with different methods, including the use of inertial sensors. Objective: To describe and analyze kinematic characteristics using the inertial sensor integrated into the iPhone 4S® and jump contact mat variables in the squat jump (SJ) and countermovement jump (CMJ) tests, and to determine the interaction between kinetic and kinematic variables. Methods: A cross-sectional study was conducted with 27 healthy young adults. The primary outcome measures were linear acceleration, flight time, contact time, jump height and dynamometry of the knee extensors. Spearman's rho was used to investigate the correlation between variables. The Mann–Whitney U rank-sum test was used for the analysis of intergender variance. Results: The greatest difference between groups (gender) was in the dynamometry variables (p<0.001) and contact mat variables (p<0.001). Between the jump tests, the greatest difference between groups (gender) was in the CMJ test (p<0.001). Conclusion: The inertial sensor embedded in the smartphone demonstrated a correlation with the jump mat and the dynamometry. Finally, the higher kinetic and kinematic scores observed in the jumps performed by male participants than in those performed by female participants suggest that they can be used to better characterize their jumping profile. Level of Evidence IV; Diagnostic Studies - Investigating a Diagnostic Test.

scholarly journals Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2660 ◽  
Author(s):  
Fabio Alexander Storm ◽  
Ambra Cesareo ◽  
Gianluigi Reni ◽  
Emilia Biffi
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Pathological Condition ◽  
Low Cost ◽  
Walk Test ◽  
Female Ratio ◽  
Number Of Patients ◽  
Wearable Inertial Sensors ◽  
6 Minute Walk Test ◽  
Minute Walk

Wearable sensors are becoming increasingly popular for complementing classical clinical assessments of gait deficits. The aim of this review is to examine the existing knowledge by systematically reviewing a large number of papers focusing on the use of wearable inertial sensors for the assessment of gait during the 6-minute walk test (6MWT), a widely recognized, simple, non-invasive, low-cost and reproducible exercise test. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 28 full-text articles. Then, the available knowledge was summarized regarding study design, subjects enrolled (number of patients and pathological condition, if any, age, male/female ratio), sensor characteristics (type, number, sampling frequency, range) and body placement, 6MWT protocol and extracted parameters. Results were critically discussed to suggest future directions for the use of inertial sensor devices in the clinics.

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.

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