scholarly journals Inertial sensor based gait analysis: a clinical application in patients with osteoarthritis

2012 ◽  
Vol 20 ◽  
pp. S107 ◽  
Author(s):  
L.R. brunton ◽  
S.A. Bolink ◽  
B. Grimm ◽  
S. Van Laarhoven ◽  
M. Lipperts ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Clinical Application ◽  
Inertial Sensor

scholarly journals On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15990-15997
Author(s):  
Felix Laufer ◽  
Michael Lorenz ◽  
Bertram Taetz ◽  
Gabriele Bleser
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Inertial Sensor ◽  
Sensor Data

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 Inertial Sensor-Based Two Feet Motion Tracking for Gait Analysis

Sensors ◽  
2013 ◽  
Vol 13 (5) ◽  
pp. 5614-5629 ◽  
Author(s):  
Tran Hung ◽  
Young Suh
Keyword(s):  
Gait Analysis ◽  
Motion Tracking ◽  
Inertial Sensor

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.

J2320103 Gait analysis using an inertial sensor

2014 ◽  
Vol 2014 (0) ◽  
pp. _J2320103--_J2320103-
Author(s):  
Takashi KATSU ◽  
Ryosuke KAKIMORI ◽  
Ryota NISHIKIDO ◽  
Yoshio INOUE ◽  
Kyoko SHIBATA ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Inertial Sensor
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