In recent years, the risk of falling, especially among the aging population, has been propelled by the rising prevalence of various chronic diseases such as Alzheimer’s and Dementia along with the increasing incidence of chronic disorders such as diabetes, chronic obstructive pulmonary disease (COPD), cardiovascular diseases, etc. Implementation of fall detection systems is vital because falls are risky especially for individuals who live alone in their homes. According to the World Health Organization1, about 28% - 35% of individuals over the age of 65 are projected to go through one fall at a minimum each year, and this danger rises to 42% for individuals over the age of 70. Such a situation impels the necessity for fall detection systems. Fall detection systems can mitigate some of the adverse consequences of a fall. One major outcome of a fall is a bone fracture, for instance, a fracture of the hip, femur, forearm, leg, or ankle bone. Immediate medical intervention could effectively prevent serious deterioration of bodily functions. Through this project, we develop ElderBOT, a wearable fall detection device with an alert system that utilizes 3 modes of notifications in case of an emergency. Our product is a reliable, effective, and cost-efficient device designed to cater to user requirements.
Project video: https://drive.google.com/file/d/1kUZlkXkYoyiRHa6RiEkOUjJ5zr7rKidT/view
Awards won: IEEE IES Best Student Branch Chapter Competiton - second best project.
IEEE IHTC video competition award - Region 10 - 2nd place.