A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

Albuquerque, Pedro; Verlekar, Tanmay Tulsidas; Correia, Paulo Lobato; Soares, Luís Ducla

doi:10.3390/s21186202

Cited by 19 publications

(14 citation statements)

References 40 publications

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“…For patients who underwent joint replacement surgery, the prostheses may affect posture or walking, and their effect should be examined [55]. Eventually, the system might be useful in assessing what would happen to the patient's movement in the case of an incorrect joint replacement, thanks to a properly trained and implemented artificial intelligence algorithm [56,57].…”

Section: Discussionmentioning

confidence: 99%

Hybrid System Mixed Reality and Marker-Less Motion Tracking for Sports Rehabilitation of Martial Arts Athletes

et al. 2023

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Rehabilitation is a vast field of research. Virtual and Augmented Reality represent rapidly emerging technologies that have the potential to support physicians in several medical activities, e.g., diagnosis, surgical training, and rehabilitation, and can also help sports experts analyze athlete movements and performance. In this study, we present the implementation of a hybrid system for the real-time visualization of 3D virtual models of bone segments and other anatomical components on a subject performing critical karate shots and stances. The project is composed of an economic markerless motion tracking device, Microsoft Kinect Azure, that recognizes the subject movements and the position of anatomical joints; an augmented reality headset, Microsoft HoloLens 2, on which the user can visualize the 3D reconstruction of bones and anatomical information; and a terminal computer with a code implemented in Unity Platform. The 3D reconstructed bones are overlapped with the athlete, tracked by the Kinect in real-time, and correctly displayed on the headset. The findings suggest that this system could be a promising technology to monitor martial arts athletes after injuries to support the restoration of their movements and position to rejoin official competitions.

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Section: Discussionmentioning

confidence: 99%

Hybrid System Mixed Reality and Marker-Less Motion Tracking for Sports Rehabilitation of Martial Arts Athletes

et al. 2023

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“…It represents approximately 60% of the gait cycle. 31 The second phase begins after the toe-off and continues until the next heel strike. It represents approximately 40% of the gait cycle.…”

Section: Proposed Methods and Model Architecturementioning

confidence: 99%

“…Recurrent neural networks (RNNs) learn dependencies between inputs in a time series and are suitable for extracting temporal features from a video sequence. 6 , 31 LSTM network is an improved RNN proposed by Hochreiter and Schmidhuber. 41 Since human gait is bipedal using forward propulsion of the center of gravity (CoG) of the body, a certain pattern is repeated during the movement.…”

Section: Proposed Methods and Model Architecturementioning

confidence: 99%

“…Gao et al 2 proposed an algorithm based on LSTM and CNN (LCWSnet) to detect certain abnormal gait including hemiplegic, tiptoe, and cross-threshold gait with accuracy reaching 93.1% on the best. Albuquerque et al 31 combined both VGG-16 CNN and a bidirectional LSTM for the extraction of spatiotemporal features from gait sequences represented by a selection of binary silhouette keyframes with an overall accuracy of 91.4% using the GAIT-IT dataset.…”

Section: Related Workmentioning

confidence: 99%

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Human Pose Estimation for Clinical Analysis of Gait Pathologies

Ali,

Medhat Hassan,

Zaki

2024

Bioinform Biol Insights

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Gait analysis serves as a critical diagnostic tool for identifying neurologic and musculoskeletal damage. Traditional manual analysis of motion data, however, is labor-intensive and heavily reliant on the expertise and judgment of the therapist. This study introduces a binary classification method for the quantitative assessment of gait impairments, specifically focusing on Duchenne muscular dystrophy (DMD), a prevalent and fatal neuromuscular genetic disorder. The research compares spatiotemporal and sagittal kinematic gait features derived from 2D and 3D human pose estimation trajectories against concurrently recorded 3D motion capture (MoCap) data from healthy children. The proposed model leverages a novel benchmark dataset, collected from YouTube and publicly available datasets of their typically developed peers, to extract time-distance variables (e.g. speed, step length, stride time, and cadence) and sagittal joint angles of the lower extremity (e.g. hip, knee, and knee flexion angles). Machine learning and deep learning techniques are employed to discern patterns that can identify children exhibiting DMD gait disturbances. While the current model is capable of distinguishing between healthy subjects and those with DMD, it does not specifically differentiate between DMD patients and patients with other gait impairments. Experimental results validate the efficacy of our cost-effective method, which relies on recorded RGB video, in detecting gait abnormalities, achieving a prediction accuracy of 96.2% for Support Vector Machine (SVM) and 97% for the deep network.

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“…In addition, gait analysis allows for the investigation of sensory–motor interaction to understand the locomotor system functioning [ 5 ]. With the emergence of machine learning techniques, automated gait classification [ 6 , 7 ] became an evolving research area in developing smart healthcare systems. Gait analysis can be conducted either with motion capture systems or wearable sensors.…”

Section: Introductionmentioning

confidence: 99%

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors

Bawa,

Banitsas,

Abbod

2024

Sensors

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Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used—namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)—to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes.

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A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

Cited by 19 publications

References 40 publications

Hybrid System Mixed Reality and Marker-Less Motion Tracking for Sports Rehabilitation of Martial Arts Athletes

Hybrid System Mixed Reality and Marker-Less Motion Tracking for Sports Rehabilitation of Martial Arts Athletes

Human Pose Estimation for Clinical Analysis of Gait Pathologies

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors

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