Analysis of Walking Pattern using LRCN for Early Diagnosis of Dementia in Elderly Patients

Abstract

Abstract: Analysis of walking pattern using LRCN for early diagnosis of Dementia in elderly patients has emerged as one of the most important study areas in the fields of health and human-machine interaction in recent years. Many artificial intelligencebased models for activity recognition have been developed; however, these algorithms fail to extract spatial and temporal data, resulting in poor real-world long-term HAR performance. Furthermore, in the literature, a restricted number of datasets for physical activity recognition with a smaller number of activities are publicly available. Given these constraints, we create a hybrid model for activity recognition that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Long-Short Term Memory (LSTM) networks are designed to work with sequence data, while Convolutional Neural Networks (CNN) are helpful for improving understanding of picture information. CNN can be used to extract spatial data at points in a series (video). The LSTM layer is granted authority for temporal sequence modelling with extracted spatial characteristics in situ, whereas the convolution layer is responsible for evaluating spatial features from the frames. 50 activity categories, 25 video groups per activity category, 133 medium videos per activity category, and more make up the UCF50- Action Recognition Dataset. 320 frames per second, 199 frames per video, 240 frames each for each video. Each video is an average of 26 intermediate frames per second. We determine which model performs best by analysing its Loss and Accuracy curves, then we test that model using certain testing films. The drawback of this paper is that most persons performing various duties cannot use our vision.