A Survey of Human Gait-Based Artificial Intelligence Applications

Harris, Elsa J; Khoo, I‐Hung; Demircan, Emel

doi:10.3389/frobt.2021.749274

Cited by 38 publications

(8 citation statements)

References 296 publications

(414 reference statements)

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“…Such long-term monitoring, especially if combined with further sensors, may produce large amounts of data that require automated analyses. Among the possibilities is the use of machine-learning algorithms trained with annotated data for pattern recognition of which activities a person is performing or has been able to perform ( Harris et al, 2022 ). Such algorithms could be trained to recognize not only level walking and running, but also activities such as climbing stairs, cycling, driving a car, riding a train or bus, indicate falls and events with excessive loads, and quantify the overall time a person has been active.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, prediction algorithms could be implemented for falls and diseases. Machine-learning algorithms are already in use for many applications in gait analyses, including spatiotemporal human gait recognition ( Harris et al, 2022 ; Konz et al, 2022 ). Such analyses in real time open up the possibility to deliver patient feedback, including warning of excessive forces and movements, as well as to remind patients that it is time to get up and exercise ( Zheng and Chen, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

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Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle

Wolff

Steinheimer²,

Warmerdam³

et al. 2023

Front. Bioeng. Biotechnol.

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The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmeanload and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle

Wolff

Steinheimer²,

Warmerdam³

et al. 2023

Front. Bioeng. Biotechnol.

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“…Microsoft has started to develop a new body-tracking SDK for the Azure Kinect using DL and convolutional neural networks (CNN) machine learning (ML) algorithms [ 52 ]. However, there is a paucity of research reports on gait analysis using Azure Kinect or other computer-vision-based ML and DL techniques [ 63 ]. Our study suggested that the Azure camera and Human Skeleton Recognition and Tracking software toolkits can effectively discriminant the gait patterns using machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…Compared with computer vision-based, most ML-based gait analysis adopt IMU-based sensor systems [ 63 , 64 , 65 , 66 , 67 ]. The ML techniques used in IMU-based gait analysis include decision tree (DT) [ 68 ], linear discriminant analysis (LDA) [ 69 ], k-nearest neighbors (k-NN) [ 70 ], support vector machine (SVM) [ 67 ], CNN [ 71 , 72 ], random forest (RF) [ 61 , 66 , 73 ], and LSTM [ 49 ].…”

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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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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“…Identification of people based on how they walk, and move is not at all an easy process, even though the instruments discussed above are quite straightforward. The intricacy of the job causes the standard gait recognition approaches, which entail data pre-processing and manually collected characteristics for subsequent recognition, to have a lot of limits and issues [7]. For instance, they need to be able to perceive the whole gait as well as huge intra-class variances, occlusions, and shadows, and they need to be able to find the body segments.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic

Sayeed

Yusof

Abdullah

et al. 2023

IJEECS

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Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explored.

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A Survey of Human Gait-Based Artificial Intelligence Applications

Cited by 38 publications

References 296 publications

Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle

Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle

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

A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic

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