Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper.

Falls are a major health problem with one in three people over the age of 65 falling each year, oftentimes causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data. Fall detection algorithms are typically trained on simulated fall data that contain a well-balanced number of examples of falls and activities of daily living. However, real-world falls occur infrequently, making them difficult to capture and causing severe data imbalance.

This dataset is in support of my Research paper 'Design of 6-DoF Combat Quadcopter'.

Preprint:   

The system is basic, on existing designs.It is very simple for any graduate,degree holder.

Related Claim : Novel ß Non-Linear Theory

This is an open-access page. All content can be freely downloaded after sign-up. This webpage contains datasets and models, proposed by me which are Self-Claimed advancements. Complete Model and Project folder will be uploaded once Certain Medical People reject it for whatever reason, or accept it. Models shared only for humanitarian purposes, Can be used or modified by Clinical Doctors or Pharmacologists or Researchers under License CC-BY.