Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals

Ji, Ning; Zhou, Hui; Guo, Kunkun; Samuel, Oluwarotimi Williams; Huang, Zhen; Xu, Lisheng; Li, Guanglin

doi:10.3390/s19163462

Cited by 32 publications

(26 citation statements)

References 41 publications

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“…The F1 score can be considered as a weighted average of the recall and precision. Furthermore, algorithm performance was evaluated by assessing the time error between the reference event (from the marker-based algorithm) and the predicted event [ 34 , 35 ], defined as: where and denoted the time of the predicted IC and FC from the IMU-based algorithm, and and denoted the reference time of the IC and FC obtained from the OMC.…”

Section: Methodsmentioning

confidence: 99%

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients

Romijnders

Warmerdam

Hansen

et al. 2021

J NeuroEngineering Rehabil

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Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement 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 as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final 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 $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%), and turning (IC: recall $$\ge$$ ≥ 85%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 91%; FC: recall $$\ge$$ ≥ 84%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

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Section: Methodsmentioning

confidence: 99%

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients

Romijnders

Warmerdam

Hansen

et al. 2021

J NeuroEngineering Rehabil

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“…The simplest computation methods of the gait detection are the threshold values. There are different types of threshold algorithms as a set of value rules that figures out certain characteristics of the gait phases or events [ 9 , 10 , 11 , 12 , 13 , 15 , 90 ], time-frequency analysis method based on thresholding values [ 18 , 19 , 20 , 21 , 92 ], or a peak heuristic algorithms which is also a branch of the threshold method in case the derivative passes through zero [ 16 , 19 , 23 , 77 , 93 ]. From the sensor signals, the shank angular velocity signal of the sagittal plane shows clearly two negative peaks at the IC and FO events, this is the simple rule to determine IC and FO events with a high accuracy of detection [ 11 , 19 , 79 , 82 ].…”

Section: Gait Detection Methodsmentioning

confidence: 99%

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Dong

Cao

et al. 2020

Sensors

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Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

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“…Acceleration signals, collected by inertial sensors, have been widely used to divide the two gait events of HS and TO [35][36][37]. Studies have proved that the threshold of comprehensive change rate of the acceleration signal can be used to determine HS and TO events, but the final result will be affected by the degree of smoothing filtering of acceleration signals [38].…”

Section: Introductionmentioning

confidence: 99%

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

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A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

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Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals

Cited by 32 publications

References 41 publications

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

A Determination Method for Gait Event Based on Acceleration Sensors

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