scholarly journals Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7143 ◽  
Author(s):  
Dylan Kobsar ◽  
Zaryan Masood ◽  
Heba Khan ◽  
Noha Khalil ◽  
Marium Yossri Kiwan ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Scoping Review ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Linear Acceleration ◽  
Patient Specific ◽  
Common Location ◽  
Spatiotemporal Parameters ◽  
Wearable Inertial Sensors ◽  
Study Designs

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

Wearable inertial sensors for gait analysis in adults with osteoarthritis – a scoping review

2021 ◽  
Vol 29 ◽  
pp. S182-S183
Author(s):  
D. Kobsar ◽  
Z. Masood ◽  
H. Khan ◽  
N. Khalil ◽  
M. Kiwan ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Scoping Review ◽  
Inertial Sensors ◽  
Wearable Inertial Sensors

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

scholarly journals An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6559
Author(s):  
Nils Roth ◽  
Arne Küderle ◽  
Dominik Prossel ◽  
Heiko Gassner ◽  
Bjoern M. Eskofier ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Real World ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Analysis Pipeline ◽  
Postural Control System ◽  
Mobility Impairments ◽  
Spatial Features ◽  
Level Walking ◽  
Stair Ambulation

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10ms for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson’s disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient’s fall risk and disease state or progression.

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 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.

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 Motion Vector Estimation of a Stereovision Camera with Inertial Sensors

2012 ◽  
Vol 19 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Paweł Pełczyński ◽  
Bartosz Ostrowski ◽  
Dariusz Rzeszotarski
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Motion Vector ◽  
Linear Acceleration ◽  
Sensor Data ◽  
Image Motion ◽  
Motion Field ◽  
Camera Motion ◽  
Search Range ◽  
Vector Estimation

Motion Vector Estimation of a Stereovision Camera with Inertial SensorsThe aim of the presented work was the development of a tracking algorithm for a stereoscopic camera setup equipped with an additional inertial sensor. The input of the algorithm consists of the image sequence, angular velocity and linear acceleration vectors measured by the inertial sensor. The main assumption of the project was fusion of data streams from both sources to obtain more accurate ego-motion estimation. An electronic module for recording the inertial sensor data was built. Inertial measurements allowed a coarse estimation of the image motion field that has reduced its search range by standard image-based methods. Continuous tracking of the camera motion has been achieved (including moments of image information loss). Results of the presented study are being implemented in a currently developed obstacle avoidance system for visually impaired pedestrians.

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.

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