Space Invariant Artificial Neural Network based Prediction of human activity of Knee joints

Human action recognition is a difficult problem that is being actively studied by the computer vision field. Human activity recognition is identifying human activities inside a digital image or video. Technologies like those used for monitoring the elderly and detecting intruders or unusual objects in public have made the strengths of artificial intelligence, and deep learning in particular, readily obvious. This research proposes employing wearable sensors to identify human activity, or Identification of Human Activity (IHA), to identify joint and knee replacement problems in older people. This research thus focuses on image-based IHA, seeks to understand more about knee replacement, and uses deep learning algorithms to recognize common human actions captured by sensing cameras, placing these devices at the cutting edge of technological advancements in consumer products. Personal impedance analysis may be used to assess practicability in several contexts.