Gait Analysis Using Principal Component Analysis and Long Short Term Memory Models

Maheswari R., Pattabiraman Venkatasubbu,A. Saleem Raja

Structural and Functional Aspects of Biocomputing Systems for Data ProcessingAdvances in Computer and Electrical Engineering(2023)

引用 0|浏览1
暂无评分
摘要
Human analysis and diagnosis have become attractive technology in many fields. Gait defines the style of movement and gait analysis is a study of human activity to inspect the style of movement and related factors used in the field of biometrics, observation, diagnosis of gait disease, treatment, rehabilitation, etc. This work aims in providing the benefit of analysis of gait with different sensors, ML models, and also LSTM recurrent neural network, using the latest trends. Placing the sensors at the proper location and measuring the values using 3D axes for these sensors provides very appropriate results. With proper fine-tuning of ML models and the LSTM recurrent neural network, it has been observed that every model has an accuracy of greater than 90%, concluding that LSTM performance is observed to be slightly higher than machine learning models. The models helped in diagnosing the disease in the foot (if there is injury in the foot) with high efficiency and accuracy. The key features are proven to be available and extracted to fit the LSTM RNN model and have a positive outcome.
更多
查看译文
关键词
gait analysis,principal component analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要