scholarly journals A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7517
Author(s):  
Vânia Guimarães ◽  
Inês Sousa ◽  
Miguel Velhote Correia
Keyword(s):  
Gait Analysis ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Coordinate Frame ◽  
Complete Evaluation ◽  
Gait Parameters ◽  
Deep Recurrent Neural Network ◽  
Optical Motion ◽  
Frame Transformation

Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.

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.

scholarly journals Comprehensive Validation of a Wearable Foot Sensor System for Estimating Spatiotemporal Gait Parameters by Simultaneous Three-Dimensional Optical Motion Analysis

2021 ◽  
Author(s):  
Kentaro Homan ◽  
Keizo Yamamoto ◽  
Ken Kadoya ◽  
Naoki Ishida ◽  
Norimasa Iwasaki
Keyword(s):  
Gait Analysis ◽  
High Speed ◽  
Inertial Sensors ◽  
Three Dimensional ◽  
Scientific Basis ◽  
The Body ◽  
Gait Parameters ◽  
Optical Motion ◽  
Analysis System ◽  
Two Parameters

Abstract Background Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate all spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS). Methods Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks. Results All gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for all gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions. Conclusion The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.

PREDICTION OF THE SPATIO-TEMPORAL GAIT PARAMETERS USING INERTIAL SENSOR

2018 ◽  
Vol 18 (07) ◽  
pp. 1840002 ◽  
Author(s):  
JIAN LIU ◽  
THURMON LOCKHART ◽  
SUKWON KIM
Keyword(s):  
Prediction Models ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Cost Effective ◽  
Human Gait ◽  
Measurement Unit ◽  
Promising Alternative ◽  
Gait Parameters ◽  
Optical Motion ◽  
Spatio Temporal

Monitoring human gait is essential to quantify gait issues associated with fall-prone individuals as well as other gait-related movement disorders. Being portable and cost-effective, ambulatory gait analysis using inertial sensors is considered a promising alternative to traditional laboratory-based approach. The current study aimed to provide a method for predicting the spatio-temporal gait parameters using the wrist-worn inertial sensors. Eight young adults were involved in a laboratory study. Optical motion analysis system and force-plates were used for the assessment of baseline gait parameters. Spatio-temporal features of an Inertial Measurement Unit (IMU) on the wrist were analyzed. Multi-variate correlation analyses were performed to develop gait parameter prediction models. The results indicated that gait stride time was strongly correlated with peak-to-peak duration of wrist gyroscope signal in the anterio-posterior direction. Meanwhile, gait stride length was successfully predicted using a combination model of peak resultant wrist acceleration and peak sagittal wrist angle. In conclusion, current study provided the evidence that the wrist-worn inertial sensors are capable of estimating spatio-temporal gait parameters. This finding paves the foundation for developing a wrist-worn gait monitor with high user compliance.

Examination of the reliability of an inertial sensor-based gait analysis system

2017 ◽  
Vol 62 (6) ◽  
pp. 615-622 ◽  
Author(s):  
Katja Orlowski ◽  
Falko Eckardt ◽  
Fabian Herold ◽  
Norman Aye ◽  
Jürgen Edelmann-Nusser ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Upper Body ◽  
Initial Contact ◽  
Gait Parameters ◽  
Analysis System ◽  
Almost All

AbstractGait analysis is an important and useful part of the daily therapeutic routine. InvestiGAIT, an inertial sensor-based system, was developed for using in different research projects with a changing number and position of sensors and because commercial systems do not capture the motion of the upper body. The current study is designed to evaluate the reliability of InvestiGAIT consisting of four off-the-shelf inertial sensors and in-house capturing and analysis software. Besides the determination of standard gait parameters, the motion of the upper body (pelvis and spine) can be investigated. Kinematic data of 25 healthy individuals (age: 25.6±3.3 years) were collected using a test-retest design with 1 week between measurement sessions. We calculated different parameters for absolute [e.g. limits of agreement (LoA)] and relative reliability [intraclass correlation coefficients (ICC)]. Our results show excellent ICC values for most of the gait parameters. Midswing height (MH), height difference (HD) of initial contact (IC) and terminal contact (TC) and stride length (SL) are the gait parameters, which did not exhibit acceptable values representing absolute reliability. Moreover, the parameters derived from the motion of the upper body (pelvis and spine) show excellent ICC values or high correlations. Our results indicate that InvestiGAIT is suitable for reliable measurement of almost all the considered gait parameters.

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

scholarly journals Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study

10.2196/13961 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e13961
Author(s):  
Kim Sarah Sczuka ◽  
Lars Schwickert ◽  
Clemens Becker ◽  
Jochen Klenk
Keyword(s):  
Real World ◽  
Independent Living ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Center Of Mass ◽  
Fall Detection ◽  
Video Data ◽  
Sensor Data ◽  
Movement Behavior ◽  
The Real

Background Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. Objective The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. Methods Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. Results A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person’s center of mass during fall events based on the available sensor information. Conclusions Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available.

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 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 Fusing Object Information and Inertial Data for Activity Recognition

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4119 ◽  
Author(s):  
Alexander Diete ◽  
Heiner Stuckenschmidt
Keyword(s):  
Activity Recognition ◽  
Pervasive Computing ◽  
Human Activities ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Arm Movement ◽  
Sensor Data ◽  
Object A ◽  
Camera View ◽  
Different Types

In the field of pervasive computing, wearable devices have been widely used for recognizing human activities. One important area in this research is the recognition of activities of daily living where especially inertial sensors and interaction sensors (like RFID tags with scanners) are popular choices as data sources. Using interaction sensors, however, has one drawback: they may not differentiate between proper interaction and simple touching of an object. A positive signal from an interaction sensor is not necessarily caused by a performed activity e.g., when an object is only touched but no interaction occurred afterwards. There are, however, many scenarios like medicine intake that rely heavily on correctly recognized activities. In our work, we aim to address this limitation and present a multimodal egocentric-based activity recognition approach. Our solution relies on object detection that recognizes activity-critical objects in a frame. As it is infeasible to always expect a high quality camera view, we enrich the vision features with inertial sensor data that monitors the users’ arm movement. This way we try to overcome the drawbacks of each respective sensor. We present our results of combining inertial and video features to recognize human activities on different types of scenarios where we achieve an F 1 -measure of up to 79.6%.

Methods for Improving Foot Displacement Measurements Calculated from Inertial Sensors

2011 ◽  
pp. 93-116 ◽  
Author(s):  
Edgar Charry ◽  
Daniel T.H. Lai
Keyword(s):  
Measurement System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Human Movement ◽  
Sensor Data ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Band Pass ◽  
Sensor Errors ◽  
Displacement Measurements

The use of inertial sensors to measure human movement has recently gained momentum with the advent of low cost micro-electro-mechanical systems (MEMS) technology. These sensors comprise accelerometer and gyroscopes which measure accelerations and angular velocities respectively. Secondary quantities such as displacement can be obtained by integration of these quantities, a method which presents challenging issues due to the problem of accumulative sensor errors. This chapter investigates the spectral evaluation of individual sensor errors and looks at the effectiveness of minimizing these errors using static digital filters. The primary focus is on the derivation of foot displacement data from inertial sensor measurements. The importance of foot, in particular toe displacement measurements is evident in the context of tripping and falling which are serious health concerns for the elderly. The Minimum Toe Clearance (MTC) as an important gait variable for falls-risk prediction and assessment, and therefore the measurement variable of interest. A brief sketch of the current devices employing accelerometers and gyroscopes is presented, highlighting the problems and difficulties reported in literature to achieve good precision. These have been mainly due to the presence of sensor errors and the error accumulative process employed in obtaining displacement measurements. The investigation first proceeds to identify the location of these sensor errors in the frequency domain using the Fast Fourier Transform (FFT) on raw inertial sensor data. The frequency content of velocity and displacement measurements obtained from integrating the inertial data using a well known strap-down method is then explored. These investigations revealed that large sensor errors occurred mainly in the low frequency spectrum while white noise exists in all frequency spectra. The efficacy of employing a band-pass filter to remove a large portion of these errors and their effect on the derived displacements is elaborated on. The cross-correlation of the FFT power spectra from a highly accurate optical measurement system and processed sensor data is used as a metric to evaluate the performance of the band-pass filter at several stages of the processing stage. The motivation is that a more fundamental method would require less computational demand and could lead to more efficient implementations in low-power and systems with limited resources, so that portable sensor based motion measurement system would provide a good degree of measurement accuracy.

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