Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms

Niswander, Wesley; Kontson, Kimberly

doi:10.3390/s21123989

Cited by 20 publications

(24 citation statements)

References 42 publications

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“…These data provided evidence that the deep learning-based model was able to derive stride-specific gait parameters. The differences between the deep learning model-based stride parameters and the marker-based stride parameters were in a similar range as a recent study that compared IMU-derived stride parameters against stride parameters obtained with a pressure sensing walkway [ 29 , 53 ] and were also in the same range as results from a study that compared IMU-derived stride parameters with stride parameters obtained with an OMC systems [ 64 ]. The mean error was lower than the mean errors reported for stance and swing time (0.011 s and 0.011 s, respectively) across elderly subjects, subjects with PD, subjects with choreatic movement disorder, and hemiparetic subjects [ 36 ].…”

Section: Discussionsupporting

confidence: 69%

“…In order to get from abstract IMU sensor readings to clinically relevant gait parameters (i.e., from accelerations and angular velocities to stride times) [ 10 ], different algorithmic approaches have been developed in the last twenty years of clinical gait research. A recent study evaluated a cross-section of these algorithms for different sensor locations on the lower leg and foot [ 29 ]. The algorithms were categorized according to which signals were analyzed, for example, the angular velocity about the medio-lateral axis or the accelerations along vertical and antero-posterior axes.…”

Section: Introductionmentioning

confidence: 99%

“…This means the sensor readings need to be linked with the anatomical axes, that is, one needs to know which sensor axis aligns with, for example, the medio-lateral axis. In most approaches, it is simply assumed that due to sensor attachment, the sensor axis aligns roughly with the anatomical axis of interest [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], or an additional calibration procedure (e.g., [ 37 ]) is required [ 29 , 38 ]. In ambulatory assessments, however, study participants often attach the sensor themselves, for example, after showering, and therefore the sensor location and alignment cannot be controlled for.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Romijnders

Warmerdam

Hansen

et al. 2022

Sensors

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Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.

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Section: Discussionsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Romijnders

Warmerdam

Hansen

et al. 2022

Sensors

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“…A popular choice is to use the shank angular velocity for TO and HS estimation corresponding to the minima in the sagittal plane angular velocity signal [ 17 ]. Many researchers have exploited this signal over the years for diverse subject populations [ 5 ], [ 6 ], [ 10 ], [ 15 ], [ 18 ]–[ 22 ]. Though reasonable accuracy may be achieved for the HS event (errors of less than 10 ms), its validity for the TO event has been subject to some debate.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms

Aftab

Ahmed

Ali

et al. 2022

Applied Bionics and Biomechanics

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Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population.

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“…A popular choice is to use the shank angular velocity for TC and IC estimation corresponding to the minima in the sagittal-plane angular velocity signal [18]. Many researchers have exploited this signal over the years for diverse subject populations [6], [7], [11], [16], [19]- [23]. This algorithm appears to predict the IC with reasonable accuracy, but its validity for the TC event has been subject to some debate [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Automatic Estimation of Gait Parameters Using Lower-Body Kinematics

et al. 2022

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Accurate estimation of gait parameters from the limb kinematics data remains a challenge for researchers. Several deterministic algorithms have been proposed in the literature for this purpose but their applicability is limited by their accuracy, especially for gait-impaired subjects. Gait parameter estimation relies on the accurate detection of key gait events of initial contact (IC) and terminal contact (TC) for gait segmentation. This study aimed to evaluate literature algorithms for IC and TC prediction for the amputee population and to introduce a novel algorithm to improve gait parameter estimation. A marker data set of 12 amputee subjects during treadmill walking was used. Seven algorithms, including one novel algorithm, detecting the landmarks (maxima, minima, and zero-crossings) in the foot, shank, and thigh velocity data were implemented and their IC and TC estimations were compared against the force platform data. IC was best detected by foot and shank velocity methods with median bias in the range of -3 to -27ms. However, the literature algorithms produced inferior accuracy for the TC event with consistent early prediction. The newly-proposed thigh velocity-based algorithm produced the best result for the TC prediction -14 to 19ms median bias. By combining the IC prediction from foot velocity data and the TC prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters with a median bias of within 5% of gait cycle time. Statistical analysis revealed the robustness of the algorithm across most walking speeds.

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Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms

Cited by 20 publications

References 42 publications

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms

A Novel Algorithm for Automatic Estimation of Gait Parameters Using Lower-Body Kinematics

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