Trajectory-based gait pattern shift detection for assistive robotics applications

Paulo, João; Peixoto, Paulo; Amorim, Paula

doi:10.1007/s11370-019-00280-z

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input data were primarily collected using a motion capture system and electromyography, including kinematics, kinetics, or neuromuscular signals from the trunk and lower limb movements during walking [36,37]. Recent machine learning studies have analyzed various sensor data from infrared cameras, accelerometers, inertial measurement units, and pressure as input data [38][39][40][41]. Although qualitative data were not included in this study, we also proposed a method to measure swing time asymmetry during walking in real time using video trained using a pose estimation module.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

Lee

Park

Jung

2021

JPM

View full text Add to dashboard Cite

The goal of this study was to develop a framework to classify dependence in ambulation by employing a deep model in a 3D convolutional neural network (3D-CNN) using video data recorded by a smartphone during inpatient rehabilitation therapy in stroke patients. Among 2311 video clips, 1218 walk action cases were collected from 206 stroke patients receiving inpatient rehabilitation therapy (63.24 ± 14.36 years old). As ground truth, the dependence in ambulation was assessed and labeled using the functional ambulatory categories (FACs) and Berg balance scale (BBS). The dependent ambulation was defined as a FAC score less than 4 or a BBS score less than 45. We extracted patient-centered video and patient-centered pose of the target from the tracked target’s posture keypoint location information. Then, the extracted patient-centered video was input in the 3D-CNN, and the extracted patient-centered pose was used to measure swing time asymmetry. Finally, we evaluated the classification of dependence in ambulation using video data via fivefold cross-validation. When training the 3D-CNN based on FACs and BBS, the model performed with 86.3% accuracy, 87.4% precision, 94.0% recall, and 90.5% F1 score. When the 3D-CNN based on FACs and BBS was combined with swing time asymmetry, the model exhibited improved performance (88.7% accuracy, 89.1% precision, 95.7% recall, and 92.2% F1 score). The proposed framework for dependence in ambulation can be useful, as it alerts clinicians or caregivers when stroke patients with dependent ambulatory move alone without assistance. In addition, monitoring dependence in ambulation can facilitate the design of individualized rehabilitation strategies for stroke patients with impaired mobility and balance function.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

Lee

Park

Jung

2021

JPM

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) is widely used in many fields such as medical diagnosis (Begg and Kamruzzaman, 2006;Farah et al, 2019), pattern recognition (Shim and Lee, 2015;Souza and Stemmer, 2018), image processing (Leightley et al, 2017;Wei et al, 2018), classification (Van Gestel et al, 2011;Senanayake et al, 2014), predictive analysis (Yoo et al, 2013;Pla et al, 2017;Xiong et al, 2019), monitoring (Van Gestel et al, 2011;Yoo et al, 2013;Senanayake et al, 2014;Xiong et al, 2019;Zeng et al, 2020), and is therefore suitable for gait research. Nonetheless, ML techniques have been used in many gait applications, such as diagnosing gait disorders (Alaqtash et al, 2011a;Devanne et al, 2016;Leightley et al, 2017), predicting early intervention related to fall-related risks due to disability or aging (Begg et al, 2005;Begg and Kamruzzaman, 2006;Paulo et al, 2019), determining motor recovery tasks (Costa et al, 2016b;Goh et al, 2018), or planning rehabilitation or therapeutic interventions (Liu et al, 2016;Thongsook et al, 2019).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Bao,

Gao,

Wang

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person’s gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992–2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

show abstract

Role of machine learning in gait analysis: a review

Khera

Kumar

2020

Journal of Medical Engineering & Technology

View full text Add to dashboard Cite

Trajectory-based gait pattern shift detection for assistive robotics applications

Cited by 4 publications

References 22 publications

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Role of machine learning in gait analysis: a review

Contact Info

Product

Resources

About