scholarly journals Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

2021 ◽  
Vol 2 ◽  
Author(s):  
Philipp Arens ◽  
Christopher Siviy ◽  
Jaehyun Bae ◽  
Dabin K. Choe ◽  
Nikos Karavas ◽  
...  
Keyword(s):  
Real Time ◽  
Daily Living ◽  
Stride Length ◽  
Inertial Sensors ◽  
Gait Training ◽  
The Body ◽  
Optical Motion ◽  
Foot Clearance ◽  
Optical Motion Capture ◽  
Induced Changes

Abstract Hemiparetic walking after stroke is typically slow, asymmetric, and inefficient, significantly impacting activities of daily living. Extensive research shows that functional, intensive, and task-specific gait training is instrumental for effective gait rehabilitation, characteristics that our group aims to encourage with soft robotic exosuits. However, standard clinical assessments may lack the precision and frequency to detect subtle changes in intervention efficacy during both conventional and exosuit-assisted gait training, potentially impeding targeted therapy regimes. In this paper, we use exosuit-integrated inertial sensors to reconstruct three clinically meaningful gait metrics related to circumduction, foot clearance, and stride length. Our method corrects sensor drift using instantaneous information from both sides of the body. This approach makes our method robust to irregular walking conditions poststroke as well as usable in real-time applications, such as real-time movement monitoring, exosuit assistance control, and biofeedback. We validate our algorithm in eight people poststroke in comparison to lab-based optical motion capture. Mean errors were below 0.2 cm (9.9%) for circumduction, −0.6 cm (−3.5%) for foot clearance, and 3.8 cm (3.6%) for stride length. A single-participant case study shows our technique’s promise in daily-living environments by detecting exosuit-induced changes in gait while walking in a busy outdoor plaza.

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 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 The real-time elderly fall posture identifying scheme with wearable sensors

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988561
Author(s):  
Tao Xu ◽  
Wei Sun ◽  
Shaowei Lu ◽  
Ke-ming Ma ◽  
Xiaoqiang Wang
Keyword(s):  
Activities Of Daily Living ◽  
Real Time ◽  
Daily Living ◽  
Wearable Sensors ◽  
Smart Phone ◽  
The Elderly ◽  
The Body ◽  
Short Message ◽  
Triaxial Accelerometer ◽  
Time Fall

The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.

scholarly journals Feasibility of Using Foot–Ground Clearance Biofeedback Training in Treadmill Walking for Post-Stroke Gait Rehabilitation

2020 ◽  
Vol 10 (12) ◽  
pp. 978
Author(s):  
Hanatsu Nagano ◽  
Catherine M. Said ◽  
Lisa James ◽  
Rezaul K. Begg
Keyword(s):  
Real Time ◽  
Training Session ◽  
Step Length ◽  
Gait Training ◽  
Treadmill Walking ◽  
Biofeedback Training ◽  
Step Width ◽  
Double Support ◽  
Post Stroke ◽  
Foot Clearance

Hemiplegic stroke often impairs gait and increases falls risk during rehabilitation. Tripping is the leading cause of falls, but the risk can be reduced by increasing vertical swing foot clearance, particularly at the mid-swing phase event, minimum foot clearance (MFC). Based on previous reports, real-time biofeedback training may increase MFC. Six post-stroke individuals undertook eight biofeedback training sessions over a month, in which an infrared marker attached to the front part of the shoe was tracked in real-time, showing vertical swing foot motion on a monitor installed in front of the subject during treadmill walking. A target increased MFC range was determined, and participants were instructed to control their MFC within the safe range. Gait assessment was conducted three times: Baseline, Post-training and one month from the final biofeedback training session. In addition to MFC, step length, step width, double support time and foot contact angle were measured. After biofeedback training, increased MFC with a trend of reduced step-to-step variability was observed. Correlation analysis revealed that MFC height of the unaffected limb had interlinks with step length and ankle angle. In contrast, for the affected limb, step width variability and MFC height were positively correlated. The current pilot-study suggested that biofeedback gait training may reduce tripping falls for post-stroke individuals.

scholarly journals Use of the Azure Kinect to measure foot clearance during obstacle crossing

2021 ◽  
Author(s):  
Kohei Yoshimoto ◽  
Masahiro Shinya
Keyword(s):  
Motion Capture ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Random Errors ◽  
Obstacle Crossing ◽  
Risk Of Falls ◽  
Optical Motion ◽  
Foot Clearance ◽  
Optical Motion Capture ◽  
Motion Capture Systems

Obstacle crossing is a typical adaptive locomotion known to be related to the risk of falls. Previous conventional studies have used elaborate and costly optical motion capture systems, which not only represent a considerable expense but also require participants to visit a laboratory. To overcome these shortcomings, we aimed to develop a practical and inexpensive solution for measuring obstacle-crossing behavior by using the Microsoft Azure Kinect, one of the most promising markerless motion capture systems. We validated the Azure Kinect as a tool to measure foot clearance and compared its performance to that of an optical motion capture system (Qualisys). We also determined the effect of the Kinect sensor placement on measurement performance. Sixteen healthy young men crossed obstacles of different heights (50, 150, and 250 mm). Kinect sensors were placed in front of and beside the obstacle as well as diagonally between those positions. As indices of measurement quality, we counted the number of measurement failures and calculated the systematic and random errors between the foot clearance measured by the Kinect and Qualisys. We also calculated the Pearson correlation coefficients between the Kinect and Qualisys measurements. The number of measurement failures and the systematic and random error were minimized when the Kinect was placed diagonally in front of the obstacle on the same side as the trail limb. The high correlation coefficient (r > 0.890) observed between the Kinect and Qualisys measurements suggests that the Azure Kinect has excellent potential for measuring foot clearance during obstacle-crossing tasks.

Visualization interface for posture monitoring of wheel loaders based on inertial sensors

2019 ◽  
Vol 233 (16) ◽  
pp. 5781-5794
Author(s):  
Chunsheng Xiao ◽  
Qingyuan Zhu ◽  
Huosheng Hu ◽  
Wei Chen ◽  
Junjun Ye
Keyword(s):  
Real Time ◽  
Good Accuracy ◽  
Inertial Sensors ◽  
Three Dimensional ◽  
The Body ◽  
Vehicle Safety ◽  
Display System ◽  
Interactive Interface ◽  
Posture Monitoring ◽  
Wheel Loaders

Wheel loaders need an interactive interface to collect and display the posture information when they operate in a harsh environment. This paper proposes a visualization interface for posture monitoring of wheel loaders. It is driven by the data from the inertial measurement system based on attitude heading reference system, including a real-time three-dimensional animation display system created using OpenGL. The proposed system could be effectively used for monitoring the body posture of wheel loaders under the influence of the external environment. Experimental results are presented to show the good accuracy and real-time performance of the proposed monitoring system. Moreover, the proposed system could enhance vehicle safety, remote operability, and human–vehicle interaction.

scholarly journals Geometric Design of a Passive Mechanical Knee for Lower Extremity Wearable Devices Based on Anthropomorphic Foot Task Geometry Scaling

2015 ◽  
Author(s):  
Shramana Ghosh ◽  
Nina Robson ◽  
J. M. McCarthy
Keyword(s):  
Wearable Devices ◽  
Heel Strike ◽  
Kinematic Synthesis ◽  
Leg Injuries ◽  
Recovery Treatment ◽  
Optical Motion ◽  
Efficient Alternative ◽  
Foot Clearance ◽  
Optical Motion Capture ◽  
Four Bar Linkage

The standard recovery treatment for ankle and lower leg injuries consists of using underarm crutches. Hands-free crutches have recently emerged as a more comfortable, natural and energy efficient alternative. However in the currently available devices such as the iWalk-Free (iWALKFree, Inc., USA) the lack of a knee joint results in abnormal motion pattern at the hip and pelvic joints to ensure foot clearance during the swing phase of the gait. To address this shortcoming, the paper describes the kinematic synthesis of a planar passive four-bar linkage that can be used as a mechanical knee in lower limb exoskeletons and other wearable devices. The knee design is based on anthropomorphic foot walking trajectory obtained from optical motion capture system. The task geometry at the foot, related to the contact and curvature constraints between the foot and the ground at two critical positions ‘heel strike’ and ‘toe off’ is scaled to the knee level. Velocity and acceleration specifications compatible with the contact and curvature constraints assist in defining the synthesis equations for the knee design. A working prototype of a passive wearable crutch substitute that incorporates the mechanical knee shows the applicability of the proposed technique.

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 Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3940
Author(s):  
Vânia Guimarães ◽  
Inês Sousa ◽  
Miguel Velhote Correia
Keyword(s):  
Gait Analysis ◽  
Relative Error ◽  
Clinical Decision Making ◽  
Inertial Sensors ◽  
Clinical Decision ◽  
The Body ◽  
Sensor Data ◽  
Sensor Orientation ◽  
Optical Motion ◽  
Spatio Temporal

Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated—potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of −3.1±9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of −3.4±6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot.

Sign in / Sign up

Export Citation Format

Share Document