Diagnostic value of a vision-based intelligent gait analyzer in screening for gait abnormalities

Tang, Yanmin; Wang, Yanhong; Feng, Xinyu; Zou, Qiaosha; Wang, Qing; Ding, Jing; Shi, Richard; Wang, Xin

doi:10.1016/j.gaitpost.2021.10.028

Cited by 17 publications

(11 citation statements)

References 35 publications

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“…The methodological quality of the 22 analyzed studies (Table S1 in Multimedia Appendix 1 [ 51 , 52 , 54 , 56 , 58 , 59 , 61 , 63 , 64 , 69 , 70 , 72 ]) ranged from 11 points in 2 (9.1%) studies [ 52 , 65 ] to 16 points in 7 (32%) studies [ 53 , 54 , 56 , 63 , 69 - 71 ].…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the applied assessment procedures, 3 main options stood out: studies with tests or assessments accompanied by sensors or accelerometers [ 51 - 57 , 59 , 61 - 63 , 66 , 70 - 72 ], studies with tests or assessments accompanied by cameras [ 68 , 69 ], or studies based on medical records [ 58 , 60 , 65 , 67 ] ( Figure 2 ). Guillan et al [ 64 ] performed a physical and functional evaluation of the participants.…”

Section: Resultsmentioning

confidence: 99%

“…In total, 22.7% (5/22) of the studies used the Timed Up and Go test [ 53 , 54 , 69 , 71 , 72 ]. In 18.2% (4/22) of them, the participants performed the test while wearing a sensor to collect data [ 53 , 54 , 71 , 72 ].…”

Section: Resultsmentioning

confidence: 99%

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The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2024

J Med Internet Res

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Background Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. Conclusions The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2024

J Med Internet Res

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“…When considering how long to make the TUG for use in a gait analysis based on machine vision, we read the relevant literature and found that gait parameters [including step length (SL), step width (SW), step duration (SD), single-stance duration (SSD) and double-stance duration (DSD)] extracted from 3-m TUG videos can be used as an effective and stable gait quantification method ( Aberg et al, 2021 ). A previous study segmented patients’ starting, turning, and walking tasks from the video of the 3-m TUG to classify patients based on disease status ( Tang et al, 2022 ). In addition, a similar study formulated vision-based FOG detection from 3-m TUG videos from 45 PD patients( Hu et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision

Li¹,

Chen²,

Zhang³

et al. 2022

Front. Aging Neurosci.

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BackgroundFreezing of gait (FOG) is a common clinical manifestation of Parkinson’s disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment.ObjectiveA method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital.MethodsIn this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model.ResultsWe adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%.ConclusionA method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.

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“…Professional gait analyzers, such as instrumented walkways and marker-based motion capture systems, are not used widespread in the clinical setting because of their high cost and low portability. Our team proposed a vision-based artificial intelligent gait analyzer for the rapid detection of gait spatiotemporal parameters and walking pattern classification based on videos of the Timed Up and Go Test (TUG) without the use of body markers [ 16 ]. By learning the gait diagnosis of three experts, the area under the curve of our intelligent gait analyzer for screening gait abnormalities was 0.96, with an accuracy of 90.14%.…”

Section: Introductionmentioning

confidence: 99%

Association between gait features assessed by artificial intelligent system and cognitive function decline in patients with silent cerebrovascular disease: study protocol of a multicenter prospective cohort study (ACCURATE-2)

Tang

Fei

et al. 2022

BMC Neurol

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Background Gait disturbances may appear prior to cognitive dysfunction in the early stage of silent cerebrovascular disease (SCD). Subtle changes in gait characteristics may provide an early warning of later cognitive decline. Our team has proposed a vision-based artificial intelligent gait analyzer for the rapid detection of spatiotemporal parameters and walking pattern based on videos of the Timed Up and Go (TUG) test. The primary objective of this study is to investigate the relationship between gait features assessed by our artificial intelligent gait analyzer and cognitive function changes in patients with SCD. Methods This will be a multicenter prospective cohort study involving a total of 14 hospitals from Shanghai and Guizhou. One thousand and six hundred patients with SCD aged 60–85 years will be consecutively recruited. Eligible patients will undergo the intelligent gait assessment and neuropsychological evaluation at baseline and at 1-year follow-up. The intelligent gait analyzer will divide participant into normal gait group and abnormal gait group according to their walking performance in the TUG videos at baseline. All participants will be naturally observed during 1-year follow-up period. Primary outcome are the changes in Mini-Mental State Examination (MMSE) score. Secondary outcomes include the changes in intelligent gait spatiotemporal parameters (step length, gait speed, step frequency, step width, standing up time, and turning back time), the changes in scores on other neuropsychological tests (Montreal Cognitive Assessment, the Stroop Color Word Test, and Digit Span Test), falls events, and cerebrovascular events. We hypothesize that both groups will show a decline in MMSE score, but the decrease of MMSE score in the abnormal gait group will be more significant. Conclusion This study will be the first to explore the relationship between gait features assessed by an artificial intelligent gait analyzer and cognitive decline in patients with SCD. It will demonstrate whether subtle gait abnormalities detected by the artificial intelligent gait analyzer can act as a cognitive-related marker for patients with SCD. Trial registration This trial was registered at ClinicalTrials.gov (NCT04456348; 2 July 2020).

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Diagnostic value of a vision-based intelligent gait analyzer in screening for gait abnormalities

Cited by 17 publications

References 35 publications

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision

Association between gait features assessed by artificial intelligent system and cognitive function decline in patients with silent cerebrovascular disease: study protocol of a multicenter prospective cohort study (ACCURATE-2)

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