Human Gait Feature Detection Using Inertial Sensors Wavelets

Głowiński, Sebastian; Błażejewski, Andrzej; Krzyżyński, Tomasz

doi:10.1007/978-3-319-46532-6_65

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…In the aspect of gait research, CWT based methods have been proven to offer a time-frequency representation with the capability to capture and analyze varying frequencies of acceleration signals, be adaptive to irregular acceleration patterns even in the presence of frequency variations, as well as to attenuate inherent noise and baseline drift [20]. Considering these advantages, wavelet transforms are being increasingly used and have been reported to have good results particularly for gait event detection [13,20,21,22,23,24,25,26,27,28].…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Guo

et al. 2019

Sensors

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Gait event detection is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. Recently, the continuous wavelet transform (CWT) based methods have been increasingly proposed for gait event detection due to their robustness. However, few investigations on determining the appropriate mother wavelet with proper selection criteria have been performed, especially for hemiplegic patients. In this study, the performances of commonly used mother wavelets in detecting gait events were systematically investigated. The acceleration signals from the tibialis anterior muscle of both healthy and hemiplegic subjects were recorded during ground walking and the two core gait events of heel strike (HS) and toe off (TO) were detected from the signal recordings by a CWT algorithm with different mother wavelets. Our results showed that the overall performance of the CWT algorithm in detecting the two gait events was significantly different when using various mother wavelets. By using different wavelet selection criteria, we also found that the accuracy criteria based on time-error minimization and F1-score maximization could provide the appropriate mother wavelet for gait event detection. The findings from this study will provide an insight on the selection of an appropriate mother wavelet for gait event detection and facilitate the development of adequate rehabilitation aids.

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Section: Introductionmentioning

confidence: 99%

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

Zhou

Guo

et al. 2019

Sensors

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“…In addition, since the position of the IMU sensors for measuring joint movement does not match the anatomical position of the actual joint, the corrected data must be converted into meaningful three-dimensional position data, such as a joint angle. For measuring the position data, the IMU system uses a similar analysis related to Wavelets to visualize gait identification [14][15][16]. Nevertheless, despite this shortcoming, the advantages of IMU systems are that they are increasingly utilized in industry and for medical diagnostic purposes [17,18].…”

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confidence: 99%

Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System

Yeo

Park

2020

Sensors

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Inertial measurement unit systems are wearable sensors that can measure the movement of a human in real-time with relatively little space and high portability. The purpose of this study was to investigate the accuracy of the inertial measurement unit (IMU) system for gait analysis by comparing it with measurements obtained using an optical motion capture (OMC) system. To compare the accuracies of these two different motion capture systems, the Spatio-temporal and kinematic parameters were measured in young adults during normal walking. Thirty healthy participants participated in the study. Data were collected while walking 5 strides on a 7 m walkway at a self-selected speed. Results of gait analysis showed that the Spatio-temporal (stride time, stride length, cadence, step length) and kinematic (knee joint peak to peak of movement) parameters were not significantly different in the participant. Spatio-temporal and kinematic parameters of the two systems were compared using the Bland-Altman method. The results obtained showed that the measurements of Spatio-temporal and kinematic parameters of gait by the two systems were similar, which suggested that IMU and OMC systems could be used interchangeably for gait measurements. Therefore, gait analysis performed using the wearable IMU system might efficiently provide gait measurements and enable accurate analysis. (OMC) system, which utilizes multiple optical cameras to calculate 3D positions of body joints, is most commonly used to analyze gait phases and has the advantage of providing real-time measures of movement a rapid reaction rate and the ability to measure data close to the human movements even for complex motions [7,9].Over the past few years, a number of wearable inertial measurement unit (IMU) systems have been used to analyze human joint movement in laboratories or in living environments [10]. The IMU system is useful because the system function is verified in the gait analysis of people with disabilities in low cost and non-invasive methods [11][12][13]. In order to increase the universal use of the IMU system, it is very necessary to analyze the accuracy of the IMU system by comparing the results of the gait analysis of the IMU system and the OMC system. Inertial measurement unit systems use accelerometers, gyroscopes, and magnetometers to provide data on the motion characteristics of joints and segments during various tasks [10]. IMU system can measure human joint movements in real-time and has the advantage of being able to measure in relatively little space and is highly portable compared to the OMC system. However, the disadvantage of the IMU system is that it includes a position drift that may vary over time with lower position accuracy. Position accuracy is a problem because it is determined by constantly integrating acceleration over time to calculate speeds and positions, and this results in cumulative measurement errors. In addition, since the position of the IMU sensors for measuring joint movement does not match the anatomical position of the ...

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“…The second limitation concerns the static parameters. By using inertial measurement units, we could receive the information about patient body acceleration during therapy and we could analyze kinetic parameters [21,22]. Finally, the physical therapy by using SPIDER and proposed model may be beneficial for patients and physiotherapist.…”

Section: Discussionmentioning

confidence: 99%

SPIDER as A Rehabilitation Tool for Patients with Neurological Disabilities: The Preliminary Research

Głowiński

Błażejewski

2020

JPM

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(1) Background and purpose: SPIDER (Strengthening Program for Intensive Developmental Exercises and activities for Reaching health capability) is dedicated for patients suffering from Cerebral Palsy, Sclerosis Multiplex, Spinal Bifida, Spinal Muscular Atrophy and strokes. Authors proposed a computer model for the evaluation patient’s condition and the rehabilitation progress. (2) Methods: The 2-year-old and 76-year-old patients with neurological problems, who underwent individual therapy included balancing and coordination practising with SPIDER device. The model comparing the forces, which act during the therapy process, such as the expander and gravity forces, was worked out using Matlab software. (3) Results: The model allowed controlling the changes into the patients centre of gravity forces continuous adjustment and postural stability during any patient’s movement. After rehabilitation sessions, lasted for 28 days during which patients received the progress information and the therapist got the numeric data, regarding the period of the therapy. (4) Conclusions: The first patient was able to move, dramatically improved the ability to balance and coordination. The second one presented change in gait, improvement in mobility, motor function and decreased fall risk. The proposed computer model gives information about the forces acting to the patient body. The physiotherapist can evaluate the progress of patient verticalization and receive information, in the form of numbers and charts.

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Human Gait Feature Detection Using Inertial Sensors Wavelets

Cited by 15 publications

References 5 publications

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

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

Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System

SPIDER as A Rehabilitation Tool for Patients with Neurological Disabilities: The Preliminary Research

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