Gait phase classification for in-home gait assessment

Ye, Minxiang; Yang, Cheng; Stanković, Vladimir; Cheng, Samuel

doi:10.1109/icme.2017.8019500

Cited by 13 publications

(13 citation statements)

References 21 publications

(32 reference statements)

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“…Inertial sensors are also extensively used for ADL analysis, balance assessment and gait analysis of older people . The use of a Kinect sensor in gait analysis has gained attention of researchers for gait pattern recognition and classification [24,25].…”

Section: Quantitative Assessmentmentioning

confidence: 99%

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

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Background: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. Results: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. Conclusion: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

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Section: Quantitative Assessmentmentioning

confidence: 99%

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

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“…Gait phase label at frame f in the m-th gait cycle curveS of cluster G z for gait parameter z 1 This work was presented in part at IEEE ICME-2017 [1].…”

Section: List Of Symbols Kmentioning

confidence: 99%

“…All 9 × 10 trained networks are evaluated for the 15% testing data for time delays from 1-9 frames with 10 network per time delay. The mean ACC of the NARX-NN model is evaluated in [1], showing that the best result is obtained for an input time delay of 8 frames; this will be used in the following experiments. For our proposed gait phase classification system, we randomly choose approximately 80% of the training data as training set and the rest as validation to tune hyper parameters.…”

Section: Reference Label Framementioning

confidence: 99%

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Distinct Feature Extraction for Video-Based Gait Phase Classification

Yang

Stanković

et al. 2020

IEEE Trans. Multimedia

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Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, image processing algorithms ensure gait analysis that is accurate enough for rehabilitation programs. This paper proposes high-accuracy classification of gait phases and muscle actions, using readings from low-cost motion capture systems. First, 12 gait parameters, drawn from the medical literature, are defined to characterize gait patterns. These proposed parameters are then used as input to our proposed multi-channel time-series classification and gait phase reconstruction methods. Proposed methods fully utilize temporal information of gait parameters, thus improving the final classification accuracy. The validation, conducted using 126 experiments, with 6 healthy volunteers and 9 stroke survivors with manually-labelled gait phases, achieves state-of-art classification accuracy of gait phase with lower computational complexity compared to previous solutions. 1 .

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“…Related range sensor and home-video based systems, which cost about £700, such as [9], [10] and [11], that build on the work of [12], with Pro-Trainer motion analysis software (Sports Motion, Inc., Cardiff, CA), offer gait analysis outside the gait laboratory, e.g., in local clinics and at homes. Similar to other range sensor and home-video based gait analysis systems [2,[13][14][15][16][17][18][19][20][21] and Inertial Measurement Unit (IMU) based gait analysis systems [22][23][24][25][26], the gait parameters obtained after data processing can be sent to physiatrists for clinical consultation, indicating the potential for tele-rehabilitation [27][28][29][30][31]. It is shown in [32] that a 2D video tracker software provides similar accuracy to VICON 3D system for knee angle measurement but not for measurement of the ankle angle over time.…”

Section: Introductionmentioning

confidence: 99%

Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

Yang

Ugbolue

McNicol

et al. 2019

IET Science, Measurement & Technology

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While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, we focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibration, autonomous detection of frames-ofinterest, Kalman-filter+Structural-Similarity-based marker tracking, and autonomous knee angle calculation. The proposed system is tested using 15 participants, including 10 stroke patients and 5 healthy volunteers. The evaluation of autonomous frames-ofinterest detection shows only 0.2% difference between the frame number of the detected frame compared to the frame number of the manually labelled ground truth frame, and thus can replace manual labelling. The system is validated against a gold standard optical motion analysis system, using knee angle accuracy as metric of assessment. The accuracy investigation between the RGBand the grayscale-video marker tracking schemes shows that the grayscale system suffers from negligible accuracy loss with a significant processing speed advantage. Experimental results demonstrate that the proposed system can automatically estimate the knee angle, with R-squared value larger than 0.95 and Bland-Altman plot results smaller than 3.0127 degrees mean error. r row centre coordinate of the marker t duration of one frame a candidate block b marker-template ∈ set membership,

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Gait phase classification for in-home gait assessment

Cited by 13 publications

References 21 publications

Assessment of frailty: a survey of quantitative and clinical methods

Assessment of frailty: a survey of quantitative and clinical methods

Distinct Feature Extraction for Video-Based Gait Phase Classification

Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

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