Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review

Cerfoglio, Serena; Ferraris, Claudia; Vismara, Luca; Amprimo, Gianluca; Priano, Lorenzo; Pettiti, Giuseppe; Galli, Manuela; Mauro, Alessandro; Cimolin, Verônica

doi:10.3390/s22134910

Cited by 19 publications

(13 citation statements)

References 104 publications

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“…The IR camera can have a resolution of 1 MP 1024 × 1024 pixels. It has been successfully used and validated in human gait analysis [ 52 , 53 , 54 ]. The RGB-D sensor system can estimate features of gait patterns in pathological individuals and differences between disorders.…”

Section: Discussionmentioning

confidence: 99%

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Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Chen

et al. 2022

Sensors

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Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.

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Section: Discussionmentioning

confidence: 99%

“…Most of the previous studies demonstrated that Azure and the Body Tracking SDK can recognize the gait patterns and differences in individuals with disorders [ 52 , 54 , 55 , 59 ]. Azure Kinect tracking accuracy of the foot marker trajectories is significantly higher than the previous model Kinect v2 overall treadmill velocities.…”

Section: Discussionmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Chen

et al. 2022

Sensors

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“…Identifying how wearable technologies have been used over the past decade to assess gait and mobility in persons with stroke [19] Presenting an overview on the state of the art regarding the use of the different Microsoft Kinect camera models to assess gait in post-stroke individuals Describing and summarizing the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings [20] Reviewing the technical and clinical specifications of biofeedback systems developed for post-stroke gait rehabilitation, including the complementary use with assistive devices and/or physiotherapist-oriented cues…”

Section: Reviewmentioning

confidence: 99%

Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

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Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

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“…Alternatively, taking measurements outside the laboratory, such as with a low-cost, depth-sensing camera (e.g., Microsoft Kinect, Microsoft, USA), provides a portable and cost-effective markerless 3D motion capture device that enables depth evaluation to be integrated with typical 2D photographs [ 24 ]. In addition, the Microsoft Kinect has been suggested as a potential alternative tool with relatively high reliability and validity for clinical gait analysis [ 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera

Promsri

2022

Sensors

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Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called “principal movements” (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM1, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM3, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion.

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Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review

Cited by 19 publications

References 104 publications

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Multimodal Sensing in Stroke Motor Rehabilitation

Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera

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