scholarly journals Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Juri Taborri ◽  
Justin Keogh ◽  
Anton Kos ◽  
Alessandro Santuz ◽  
Anton Umek ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Wearable Sensors ◽  
Inertial Force ◽  
Sport Performance ◽  
Performance Outcomes ◽  
Human Motion ◽  
Force Sensors ◽  
Technological Developments ◽  
Insight Into ◽  
Sport Biomechanics

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

scholarly journals High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1238 ◽  
Author(s):  
Irvin López-Nava ◽  
Angélica Muñoz-Meléndez
Keyword(s):  
Daily Living ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Research Problem ◽  
Human Motion ◽  
Lower Limbs ◽  
Smart Devices ◽  
Joint Orientation ◽  
Human Actions ◽  
Living Environments

Action recognition is important for various applications, such as, ambient intelligence, smart devices, and healthcare. Automatic recognition of human actions in daily living environments, mainly using wearable sensors, is still an open research problem of the field of pervasive computing. This research focuses on extracting a set of features related to human motion, in particular the motion of the upper and lower limbs, in order to recognize actions in daily living environments, using time-series of joint orientation. Ten actions were performed by five test subjects in their homes: cooking, doing housework, eating, grooming, mouth care, ascending stairs, descending stairs, sitting, standing, and walking. The joint angles of the right upper limb and the left lower limb were estimated using information from five wearable inertial sensors placed on the back, right upper arm, right forearm, left thigh and left leg. The set features were used to build classifiers using three inference algorithms: Naive Bayes, K-Nearest Neighbours, and AdaBoost. The F- m e a s u r e average of classifying the ten actions of the three classifiers built by using the proposed set of features was 0.806 ( σ = 0.163).

scholarly journals Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6330
Author(s):  
Jack H. Geissinger ◽  
Alan T. Asbeck
Keyword(s):  
Machine Learning ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Human Motion ◽  
Supervised Machine Learning ◽  
Upper Body ◽  
Worst Case ◽  
Open World ◽  
Fitness Training ◽  
Full Body

In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics estimates using a small number of sensors. To accomplish this, we introduced a new dataset (the Virginia Tech Natural Motion Dataset) of full-body human motion capture using XSens MVN Link that contains more than 40 h of unscripted daily life motion in the open world. Using this dataset, we conducted self-supervised machine learning to do kinematics inference: we predicted the complete kinematics of the upper body or full body using a reduced set of sensors (3 or 4 for the upper body, 5 or 6 for the full body). We used several sequence-to-sequence (Seq2Seq) and Transformer models for motion inference. We compared the results using four different machine learning models and four different configurations of sensor placements. Our models produced mean angular errors of 10–15 degrees for both the upper body and full body, as well as worst-case errors of less than 30 degrees. The dataset and our machine learning code are freely available.

Imitation Control for Biped Robot Using Wearable Motion Sensor

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Tao Liu ◽  
Yoshio Inoue ◽  
Kyoko Shibata
Keyword(s):  
Motion Analysis ◽  
Control Method ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Biped Robot ◽  
Human Motion ◽  
Optical Tracking ◽  
Human Motion Analysis ◽  
Leg Length ◽  
Using Data

In conventional imitation control, optical tracking devices have been widely adopted to capture human motion and control robots in a laboratory environment. Wearable sensors are attracting extensive interest in the development of a lower-cost human-robot control system without constraints from stationary motion analysis devices. We propose an ambulatory human motion analysis system based on small inertial sensors to measure body segment orientations in real time. A new imitation control method was developed and applied to a biped robot using data of human joint angles obtained from a wearable sensor system. An experimental study was carried out to verify the method of synchronous imitation control for a biped robot. By comparing the results obtained from direct imitation control with an improved method based on a training algorithm, which includes a personal motion pattern, we found that the accuracy of imitation control was markedly improved and the tri-axial average errors of x-y- and z-moving displacements related to leg length were 12%, 8% and 4%, respectively. Experimental results support the feasibility of the proposed control method.

scholarly journals Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2564 ◽  
Author(s):  
Andrea Ancillao ◽  
Salvatore Tedesco ◽  
John Barton ◽  
Brendan O’Flynn
Keyword(s):  
Inertial Sensors ◽  
Wearable Sensors ◽  
Reaction Force ◽  
Performance Testing ◽  
Indirect Measurement ◽  
Sport Performance ◽  
Ground Reaction Forces ◽  
Kinematic Data ◽  
Reaction Forces ◽  
Wearable Inertial Sensors

In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

scholarly journals Validity of Inertial Sensors for Assessing Balance Kinematics and Mobility during Treadmill-Based Perturbation and Dance Training

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3065
Author(s):  
Ernest Kwesi Ofori ◽  
Shuaijie Wang ◽  
Tanvi Bhatt
Keyword(s):  
Concurrent Validity ◽  
Inertial Sensors ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Human Motion ◽  
Intraclass Correlation Coefficients ◽  
Dance Training ◽  
Silver Standard ◽  
Mean Square Errors ◽  
Reactive Balance

Inertial sensors (IS) enable the kinematic analysis of human motion with fewer logistical limitations than the silver standard optoelectronic motion capture (MOCAP) system. However, there are no data on the validity of IS for perturbation training and during the performance of dance. The aim of this present study was to determine the concurrent validity of IS in the analysis of kinematic data during slip and trip-like perturbations and during the performance of dance. Seven IS and the MOCAP system were simultaneously used to capture the reactive response and dance movements of fifteen healthy young participants (Age: 18–35 years). Bland Altman (BA) plots, root mean square errors (RMSE), Pearson’s correlation coefficients (R), and intraclass correlation coefficients (ICC) were used to compare kinematic variables of interest between the two systems for absolute equivalency and accuracy. Limits of agreements (LOA) of the BA plots ranged from −0.23 to 0.56 and −0.21 to 0.43 for slip and trip stability variables, respectively. The RMSE for slip and trip stabilities were from 0.11 to 0.20 and 0.11 to 0.16, respectively. For the joint mobility in dance, LOA varied from −6.98–18.54, while RMSE ranged from 1.90 to 13.06. Comparison of IS and optoelectronic MOCAP system for reactive balance and body segmental kinematics revealed that R varied from 0.59 to 0.81 and from 0.47 to 0.85 while ICC was from 0.50 to 0.72 and 0.45 to 0.84 respectively for slip–trip perturbations and dance. Results of moderate to high concurrent validity of IS and MOCAP systems. These results were consistent with results from similar studies. This suggests that IS are valid tools to quantitatively analyze reactive balance and mobility kinematics during slip–trip perturbation and the performance of dance at any location outside, including the laboratory, clinical and home settings.

scholarly journals Wearable Sensors Integrated with Virtual Reality: A Self-Guided Healthcare System Measuring Shoulder Joint Mobility for Frozen Shoulder

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Jianjun Cui ◽  
Shih-Ching Yeh ◽  
Si-Huei Lee
Keyword(s):  
Virtual Reality ◽  
Shoulder Joint ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Frozen Shoulder ◽  
Joint Mobility ◽  
Joint Movement ◽  
Rehabilitation Process ◽  
Shoulder Condition ◽  
At Home

Frozen shoulder is a common clinical shoulder condition. Measuring the degree of shoulder joint movement is crucial to the rehabilitation process. Such measurements can be used to evaluate the severity of patients’ condition, establish rehabilitation goals and appropriate activity difficulty levels, and understand the effects of rehabilitation. Currently, measurements of the shoulder joint movement degree are typically conducted by therapists using a protractor. However, along with the growth of telerehabilitation, measuring the shoulder joint mobility on patients’ own at home will be needed. In this study, wireless inertial sensors were combined with the virtual reality interactive technology to provide an innovative shoulder joint mobility self-measurement system that can enable patients to measure their performance of four shoulder joint movements on their own at home. Pilot clinical trials were conducted with 25 patients to confirm the feasibility of the system. In addition, the results of correlation and differential analyses compared with the results of traditional measurement methods exhibited a high correlation, verifying the accuracy of the proposed system. Moreover, according to interviews with patients, they are confident in their ability to measure shoulder joint mobility themselves.

scholarly journals Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4132 ◽  
Author(s):  
Ku Ku Abd. Rahim ◽  
I. Elamvazuthi ◽  
Lila Izhar ◽  
Genci Capi
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Ensemble Methods ◽  
Daily Activities ◽  
Support Vector ◽  
Random Subspace ◽  
Ensemble Classifiers ◽  
Accuracy Rate

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

scholarly journals A flexible motion tracking system based on inertial sensors

2018 ◽  
Vol 198 ◽  
pp. 04010
Author(s):  
Zhonghao Han ◽  
Lei Hu ◽  
Na Guo ◽  
Biao Yang ◽  
Hongsheng Liu ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Data Transfer ◽  
Inertial Sensors ◽  
Tracking System ◽  
Calibration Procedure ◽  
Human Motion ◽  
The Body ◽  
Support Vector ◽  
Motion Data ◽  
Motion Tracking System

As a newly emerging human-computer interaction, motion tracking technology offers a way to extract human motion data. This paper presents a series of techniques to improve the flexibility of the motion tracking system based on the inertial measurement units (IMUs). First, we built a most miniatured wireless tracking node by integrating an IMU, a Wi-Fi module and a power supply. Then, the data transfer rate was optimized using an asynchronous query method. Finally, to simplify the setup and make the interchangeability of all nodes possible, we designed a calibration procedure and trained a support vector machine (SVM) model to determine the binding relation between the body segments and the tracking nodes after setup. The evaluations of the whole system justify the effectiveness of proposed methods and demonstrate its advantages compared to other commercial motion tracking system.

scholarly journals Intelligent Monitoring of Affective Factors Underlying Sport Performance by Means of Wearable and Mobile Technology

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1202 ◽  
Author(s):  
Carlos Bailon ◽  
Miguel Damas ◽  
Hector Pomares ◽  
Daniel Sanabria ◽  
Pandelis Perakakis ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Affective State ◽  
Sport Performance ◽  
Affective States ◽  
Complete Evaluation ◽  
Affective Factors ◽  
Physiological Variables ◽  
Sport Events ◽  
The Impact ◽  
The Relationship

The fluctuation of affective states is a contributing factor to sport performance variability. The context surrounding athletes during their daily life and the evolution of their physiological variables beyond sport events are relevant factors, as they modulate the affective state of the subject over time. However, traditional procedures to assess the affective state are limited to self-reported questionnaires within controlled settings, thus removing the impact of the context. This work proposes a multimodal, context-aware platform that combines the data acquired through smartphones and wearable sensors to assess the affective state of the athlete. The platform is aimed at ubiquitously monitoring the fluctuations of affective states during longitudinal studies within naturalistic environments, overcoming the limitations of previous studies and allowing for the complete evaluation of the factors that could modulate the affective state. This system will also facilitate and expedite the analysis of the relationship between affective states and sport performance.

scholarly journals Human motion component and envelope characterization via wireless wearable sensors

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Kaitlyn R. Ammann ◽  
Touhid Ahamed ◽  
Alice L. Sweedo ◽  
Roozbeh Ghaffari ◽  
Yonatan E. Weiner ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Human Motion
Sign in / Sign up

Export Citation Format

Share Document