A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System

Fröhlich, Holger; Claes, Kasper; Wolf, Catherine G.; Damme, Xavier Van; Michel, Anne

doi:10.1109/tbme.2017.2701204

Cited by 27 publications

(15 citation statements)

References 18 publications

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“…Research and development of wearable devices have been gaining more attention and efforts recently. Attempts have been made to develop smart wearable devices that allow human motion capture [13,14,15,16,17] and estimation of biometric properties [18,19,20], and even provide corresponding biofeedback to users based on the analysis [21,22] with various sensors and feedback units. Improvements of balance and gait control have been achieved with such devices [23,24,25,26,27].…”

Section: Introductionmentioning

confidence: 99%

Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion

Zong-Hao

Ling

Shea

et al. 2019

Sensors

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Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. Methods: In ten healthy participants, the changes in muscle area and activity of gastrocnemius, plantarflexion and dorsiflexion of right leg during walking were evaluated by the developed system and the Vicon system. The existence of significant changes in a gait cycle, comparison of the ankle kinetic data captured by the developed system and the Vicon system, and test-retest reliability (evaluated by the intraclass correlation coefficient, ICC) in each channel’s data captured by the developed system were examined. Results: Moderate to good test-retest reliability of various channels of the developed system (0.512 ≤ ICC ≤ 0.988, p < 0.05), significantly high correlation between the developed system and Vicon system in ankle joint angles (0.638R ≤ 0.707, p < 0.05), and significant changes in muscle activity of gastrocnemius during a gait cycle (p < 0.05) were found. Conclusion: A newly developed wearable motion capture and measurement system with ultrasound imaging that can accurately capture the motion of one leg was evaluated in this study, which paves the way towards real-time comprehensive evaluation of muscles and joint motions during different activities in both indoor and outdoor environments.

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Section: Introductionmentioning

confidence: 99%

Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion

Zong-Hao

Ling

Shea

et al. 2019

Sensors

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“…[13,14,16,17] Minimum paw print intensity Minimum intensity of the paw. [17,19] Mean paw print intensity Mean intensity of the paw. [13,[16][17][18][19] Maximum intensity at maximum contact Maximum intensity of the paw at maximum contact.…”

Section: Static Paw Parametersmentioning

confidence: 99%

“…[17,19] Mean paw print intensity Mean intensity of the paw. [13,[16][17][18][19] Maximum intensity at maximum contact Maximum intensity of the paw at maximum contact. [17,19] Relative paw position Relative positions of fore and hind paws: hind paw position is related to the previous forepaw position.…”

Section: Static Paw Parametersmentioning

confidence: 99%

“…[13,[16][17][18][19] Maximum intensity at maximum contact Maximum intensity of the paw at maximum contact. [17,19] Relative paw position Relative positions of fore and hind paws: hind paw position is related to the previous forepaw position. If the hind paw is placed (partially) after the forepaw, the distance is positive; otherwise, it is negative.…”

Section: Static Paw Parametersmentioning

confidence: 99%

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Gait Analysis Using Animal Models of Peripheral Nerve and Spinal Cord Injuries

2021

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The present review discusses recent data regarding rodent models of spinal cord and peripheral nerve injuries in terms of gait analysis using the CatWalk system (CW), an automated and exceptionally reliable system for assessing gait abnormalities and motor coordination. CW is a good tool for both studying improvements in the walking of animals after suffering a peripheral nerve and spinal cord lesion and to select the best therapies and procedures after tissue destruction, given that it provides objective and quantifiable data. Most studies using CW for gait analysis that were published in recent years focus on injuries inflicted in the peripheral nerve, spinal cord, and brain. CW has been used in the assessment of rodent motor function through high-resolution videos, whereby specialized software was used to measure several aspects of the animal’s gait, and the main characteristics of the automated system are presented here. CW was developed to assess footfall and gait changes, and it can calculate many parameters based on footprints and time. However, given the multitude of parameters, it is necessary to evaluate which are the most important under the employed experimental circumstances. By selecting appropriate animal models and evaluating peripheral nerve and spinal cord lesion regeneration using standardized methods, suggestions for new therapies can be provided, which represents the translation of this methodology into clinical application.

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“…While there are several recent contributions applying machine learning to Parkinsonian tremors (16) and gait analysis (17) as well as the freezing of gait detection (13,18), to our knowledge, no work has used measurements of stepping actions, using kinetic data alone to predict upcoming freezing events. Therefore, this study aims to forecast freezing events from the kinetic stepping data.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Prediction of Freezing of Gait Events From Stepping Data

Unni

Menon

Livi

et al. 2020

Front. Med. Technol.

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Freezing of gait (FoG) is typically a symptom of advanced Parkinson's disease (PD) that negatively influences the quality of life and is often resistant to pharmacological interventions. Novel treatment options that make use of auditory or sensory cues might be optimized by prediction of freezing events. These predictions might help to trigger external sensory cues—shown to improve walking performance—when behavior is changed in a manner indicative of an impending freeze (i.e., when the user needs it the most), rather than delivering cue information continuously. A data-driven approach is proposed for predicting freezing events using Random Forrest (RF), Neural Network (NN), and Naive Bayes (NB) classifiers. Vertical forces, sampled at 100 Hz from a force platform were collected from 9 PD subjects as they stepped in place until they at least had one freezing episode or for 90 s. The F1 scores of RF/NN/NB algorithms were computed for different IL (input to the machine learning algorithm), and GL (how early the freezing event is predicted). A significant negative correlation between the F1 scores and GL, highlighting the difficulty of early detection is found. The IL that maximized the F1 score is approximately equal to 1.13 s. This indicates that the physiological (and therefore neurological) changes leading to freezing take effect at-least one step before the freezing incident. Our algorithm has the potential to support the development of devices to detect and then potentially prevent freezing events in people with Parkinson's which might occur if left uncorrected.

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A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System

Cited by 27 publications

References 18 publications

Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion

Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion

Gait Analysis Using Animal Models of Peripheral Nerve and Spinal Cord Injuries

Data-Driven Prediction of Freezing of Gait Events From Stepping Data

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