scholarly journals An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2869
Author(s):  
Jiaen Wu ◽  
Kiran Kuruvithadam ◽  
Alessandro Schaer ◽  
Richie Stoneham ◽  
George Chatzipirpiridis ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensor ◽  
Neurological Diseases ◽  
Principal Component ◽  
Sampling Frequency ◽  
Powerful Analytical Tool ◽  
Optical Motion ◽  
Analysis System ◽  
Optical Motion Capture ◽  
Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

scholarly journals An Intelligent In-Shoe System for Real-Time Gait Monitoring and Analysis

2021 ◽  
Author(s):  
Jiaen Wu ◽  
Kiran Kuruvithadam ◽  
Alessandro Schaer ◽  
Richie Stoneham ◽  
George Chatzipirpiridis ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensor ◽  
Neurological Diseases ◽  
Principal Component ◽  
Sampling Frequency ◽  
Eigen Analysis ◽  
Gait Characteristic ◽  
Optical Motion ◽  
Analysis System ◽  
Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on the technologies for gait characteristic assessment, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigen-analysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

scholarly journals Comparative Analysis of Inertial Sensor to Optical Motion Capture System Performance in Push-Pull Exertion Postures

2016 ◽  
Vol 60 (1) ◽  
pp. 970-974 ◽  
Author(s):  
Sol Lim ◽  
Andrea Case ◽  
Clive D’Souza
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Interactive Effects ◽  
Flexion Angle ◽  
Task Demand ◽  
Optical Motion ◽  
Physical Task ◽  
Target Force ◽  
Optical Motion Capture

This study examined interactions between inertial sensor (IS) performance and physical task demand on posture kinematics in a two-handed force exertion task. Fifteen male individuals participated in a laboratory experiment that involved exerting a two-handed isometric horizontal force on an instrumented height-adjustable handle. Physical task demand was operationalized by manipulating vertical handle height, target force magnitude, and force direction. These factors were hypothesized to influence average estimates of torso flexion angle measured using inertial sensors and an optical motion capture (MC) system, as well as the root mean squared errors (RMSE) between instrumentation computed over a 3s interval of the force exertion task. Results indicate that lower handle heights and higher target force levels were associated with increased torso and pelvic flexion in both, push and pull exertions. Torso flexion angle estimates obtained from IS and MC did not differ significantly. However, RMSE increased with target force intensity suggesting potential interactive effects between measurement error and physical task demand.

scholarly journals Validation of IMU-Based Gait Event Detection During Curved Walking and Turning in Older Adults and Parkinson's Disease Patients

2020 ◽  
Author(s):  
Robbin Romijnders ◽  
Elke Warmerdam ◽  
Clint Hansen ◽  
Julius Welzel ◽  
Gerhard Schmidt ◽  
...  
Keyword(s):  
Older Adults ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Daily Life ◽  
Neurological Diseases ◽  
Detection Performance ◽  
Straight Line ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Task Conditions

Abstract Background: Identication of individual gait events is essential for clinical gait analysis, because it can beused for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson'sdisease. Previous research has shown that gait events can be detected from a shank-mounted inertialmeasurement unit (IMU), however detection performance was often evaluated only from straight-line walking.For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well asin single-task and dual-task conditions.Methods: Participants (older adults, people with Parkinson's disease, or people who had suered from astroke) performed three dierent walking trials: 1) straight-line walking, 2) slalom walking, 3) Stroop-and-walktrial. An optical motion capture system was used a reference system. Markers were attached to the heel andtoe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity ofthe shank IMUs was used to detect instances of initial foot contact (IC) and nal foot contact (FC), whichwere compared to reference values obtained from the marker trajectories.Results: The detection method showed high recall, precision and F1 scores in dierent populations for bothinitial contacts and nal contacts during straight-line walking (IC: recall = 100%, precision = 100%, F1 score= 100%; FC: recall = 100%, precision = 100%, F1 score = 100%), slalom walking (IC: recall = 100%,precision 99%, F1 score =100%; FC: recall = 100%, precision 99%, F1 score =100%), and turning (IC:recall 85%, precision 95%, F1 score 91%; FC: recall 84%, precision 95%, F1 score 89%).Conclusions: Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalomwalking and turning. However, more false events were observed during turning and more events were missedduring turning. For use in daily life we recommend identifying turning before extracting temporal gaitparameters from identied gait events.

Toward accurate real-time marker labeling for live optical motion capture

2017 ◽  
Vol 33 (6-8) ◽  
pp. 993-1003 ◽  
Author(s):  
Shihong Xia ◽  
Le Su ◽  
Xinyu Fei ◽  
Han Wang
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Time Marker ◽  
Optical Motion ◽  
Optical Motion Capture

scholarly journals Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4675
Author(s):  
Ibai Gorordo Fernandez ◽  
Siti Anom Ahmad ◽  
Chikamune Wada
Keyword(s):  
Real Time ◽  
Fall Risk ◽  
Inertial Sensor ◽  
Feedback System ◽  
Sensor Data ◽  
Mobility Impairment ◽  
Orientation Estimation ◽  
Contact Phase ◽  
Optical Motion ◽  
Phase Prediction

Falls are among the main causes of injuries in elderly individuals. Balance and mobility impairment are major indicators of fall risk in this group. The objective of this research was to develop a fall risk feedback system that operates in real time using an inertial sensor-based instrumented cane. Based on inertial sensor data, the proposed system estimates the kinematics (contact phase and orientation) of the cane. First, the contact phase of the cane was estimated by a convolutional neural network. Next, various algorithms for the cane orientation estimation were compared and validated using an optical motion capture system. The proposed cane contact phase prediction model achieved higher accuracy than the previous models. In the cane orientation estimation, the Madgwick filter yielded the best results overall. Finally, the proposed system was able to estimate both the contact phase and orientation in real time in a single-board computer.

scholarly journals Validation of an IMU Suit for Military-Based Tasks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4280 ◽  
Author(s):  
Matthew P. Mavor ◽  
Gwyneth B. Ross ◽  
Allison L. Clouthier ◽  
Thomas Karakolis ◽  
Ryan B. Graham
Keyword(s):  
Motion Capture ◽  
Principal Component ◽  
Load Carriage ◽  
Body Motion ◽  
Promising Alternative ◽  
Flexion Extension ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Optical Markers ◽  
Full Body

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.

scholarly journals Realtime assessment of foot orientation by Accelerometers and Gyroscopes

2015 ◽  
Vol 1 (1) ◽  
pp. 446-469 ◽  
Author(s):  
Thomas Seel ◽  
David Graurock ◽  
Thomas Schauer
Keyword(s):  
Healthy Subjects ◽  
Sagittal Plane ◽  
Inertial Sensor ◽  
Orientation Angle ◽  
Frontal Plane ◽  
Ferromagnetic Material ◽  
Optical Motion ◽  
Transfemoral Amputees ◽  
Optical Motion Capture ◽  
Magnetic Disturbances

AbstractFoot orientation can be assessed in realtime by means of a foot-mounted inertial sensor. We consider a method that uses only accelerometer and gyroscope readings to calculate the foot pitch and roll angle, i.e. the foot orientation angle in the sagittal and frontal plane, respectively. Since magnetometers are avoided completely, the method can be used indoors as well as in the proximity of ferromagnetic material and magnetic disturbances. Furthermore, we allow for almost arbitrary mounting orientation in the sense that we only assume one of the local IMU coordinate axes to lie in the sagittal plane of the foot. The method is validated with respect to a conventional optical motion capture system in trials with transfemoral amputees walking with shoes and healthy subjects walking barefoot, both at different velocities. Root mean square deviations of less than 4° are found in all scenarios, while values near 2° are found in slow shoe walking. This demonstrates that the proposed method is suitable for realtime application such as the control of FES-based gait neuroprostheses and active orthoses.

Sign in / Sign up

Export Citation Format

Share Document