Development of ultrasonic tachography system for gait analysis

Paul, Sananda; Banerjee, Anwesha; Ghoshal, Rana; Tibarewala, D. N.

doi:10.1504/ijbet.2016.074115

Cited by 3 publications

(1 citation statement)

References 120 publications

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“…A great number of different approaches have been proposed in the literature for gait analysis. Regarding contactless sensors, they range from ultrasonic [6] sensors to light-based approaches using infrared light (IR) [7] or Laser Range Finders (LRF) modules [8]. A common approach and of particular relevance in the context of this work is the use of depth cameras [2], [9], [10], devices that use structured light (by means of aligned IR emitter matrix and matching sensor) to measure distance.…”

Section: Markerless Gait Analysismentioning

confidence: 99%

Markerless Gait Analysis Vision System for Real-time Gait Monitoring

André

Lopes

Palermo

et al. 2020

2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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On this paper a vision-based contact-and markerless method for gait evaluation is proposed, and validated in different experimental setups against commercial motion capture systems (Vicon) and inertial gait analysis tools (GaitShoes). While the development goal is its integration on the ASBGo Smart Walker platform, only an inexpensive depth camera is required. It is shown to have reasonable results when computing gait metrics in real time, in different experimental setups, from different walker types, vision hardware and walking scenarios. Performance is evaluated through RMSD values for several gait metrics. Results illustrate that the proposed approach can be a valuable non-invasive, contactless and low cost alternative to gait analysis systems used in clinical rehabilitation environments.

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Section: Markerless Gait Analysismentioning

confidence: 99%

Markerless Gait Analysis Vision System for Real-time Gait Monitoring

André

Lopes

Palermo

et al. 2020

2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Gait Analysis

Wagner

Mazurek

Morawski

2022

Health Information Science

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STJA-GCN: A Multi-Branch Spatial–Temporal Joint Attention Graph Convolutional Network for Abnormal Gait Recognition

et al. 2023

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Early recognition of abnormal gait enables physicians to determine a prompt rehabilitation plan for patients for the most effective treatment and care. The Kinect depth sensor can easily collect skeleton data describing the position of joints in the human body. However, the default human skeleton model of Kinect includes an excessive number of many joints, which limits the accuracy of the gait recognition methods and increases the computational resources required. In this study, we propose an optimized human skeleton model for the Kinect system and streamline the joints using a center-of-mass calculation. We integrate several techniques to propose an end-to-end, spatial–temporal, joint attention graph convolutional network (STJA-GCN) architecture. We conducted experiments with a fivefold cross-validation on two common datasets of information on abnormal gaits to evaluate the performance of the proposed method. The results show that the STJA-GCN achieved 93.17 and 92.08% accuracy on the two datasets, and compared to the original spatial–temporal graph convolutional network (ST-GCN), the recognition accuracy increases by 9.22 and 20.65%, respectively. Overall, the results demonstrate that the STJA-GCN can accurately recognize abnormal gaits and, thus, can support low-cost rehabilitation assessments at community hospitals or in patients’ homes.

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Development of ultrasonic tachography system for gait analysis

Cited by 3 publications

References 120 publications

Markerless Gait Analysis Vision System for Real-time Gait Monitoring

Markerless Gait Analysis Vision System for Real-time Gait Monitoring

Gait Analysis

STJA-GCN: A Multi-Branch Spatial–Temporal Joint Attention Graph Convolutional Network for Abnormal Gait Recognition

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