Early Detection of Hemiplegia by Analyzing the Gait Characteristics and Walking Patterns Using Convolutional Neural Networks

  • Sagar Patil
  • Akshay Shah
  • Shubham Dalvi
  • Jignesh Sisodia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)


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.


Gait detection Hemiplegia Convolution neural networks Deep learning 



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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sagar Patil
    • 1
  • Akshay Shah
    • 1
  • Shubham Dalvi
    • 1
  • Jignesh Sisodia
    • 1
  1. 1.Department of Information TechnologySardar Patel Institute of TechnologyMumbaiIndia

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