Abstract
Over the time the human posture can become a cause of concern. Bad posture cannot only be caused by our living habits but can also be a symptom of neurological diseases. Various diseases can be detected and diagnosed at an early stage if the posture pattern of a person is observed and analyzed. In this paper, we devise a method utilizing deep learning and convolutional neural networks which analyzes the gait characteristics of humans to identify Hemiplegia. The method distinguishes the users as healthy or Hemiplegic based on their posture and walking pattern. The proposed method will help health professionals with the clinical examination of a patient for Hemiplegia identifications.
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Acknowledgement
We thank Dr. Ganesh Salvi, Dr. Salvi’s Clinic, Mumbai, for taking the required permissions from their patients to use their video data for research purposes. The authors also thank Dr. Ganesh Salvi for their work in authenticating the Hemiplegic patient samples.
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Patil, S., Shah, A., Dalvi, S., Sisodia, J. (2020). Early Detection of Hemiplegia by Analyzing the Gait Characteristics and Walking Patterns Using Convolutional Neural Networks. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_5
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