Classification of Stroke and Non-Stroke Patients from Human Body Movements using Smartphone Videos and Deep Neural Networks

Feliandra, Zafira Binta; Khadijah, Siti; Rachmadi, Muhammad Febrian; Chahyati, Dina

doi:10.1109/icacsis56558.2022.9923501

Cited by 4 publications

(1 citation statement)

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“…Among these models, Movenet [24] stands out as a promising option for application in various scenarios, due to its strong ability to detect key joint features using image information. It has been proved that MoveNet can be applied to create a software for monitoring physical activities in the elderly [25], and can be extended to the classification of stroke patients based on videos captured by smartphones [26]. In scenarios where precise and complex measuring instruments are challenging to use for posturee detection, MoveNet achieves more accurate results with simple image data alone, showcasing its immense application potential.…”

Section: Related Workmentioning

confidence: 99%

Addressing Ergonomic Challenges in Agriculture through AI-Enabled Posture Classification

Kapse,

Wu,

Thamsuwan

2024

Applied Sciences

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In this study, we explored the application of Artificial Intelligence (AI) for posture detection in the context of ergonomics in the agricultural field. Leveraging computer vision and machine learning, we aim to overcome limitations in accuracy, robustness, and real-time application found in traditional approaches such as observation and direct measurement. We first collected field videos to capture real-world scenarios of workers in an outdoor plant nursery. Next, we labeled workers’ trunk postures into three distinct categories: neutral, slight forward bending and full forward bending. Then, through CNNs, transfer learning, and MoveNet, we investigated the effectiveness of different approaches in accurately classifying trunk postures. Specifically, MoveNet was utilized to extract key anatomical features, which were then fed into various classification algorithms including DT, SVM, RF and ANN. The best performance was obtained using MoveNet together with ANN (accuracy = 87.80%, precision = 87.46%, recall = 87.52%, and F1-score = 87.41%). The findings of this research contributed to the integration of computer vision techniques with ergonomic assessments especially in the outdoor field settings. The results highlighted the potential of correct posture classification systems to enhance health and safety prevention practices in the agricultural industry.

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Section: Related Workmentioning

confidence: 99%