Combining Surface-Based Electromyography and Motion Sensing for Detection of Human Falling

• Main Authors: Ibarrientos, Lawrence, Bolingot, Harold Jay, Macaraig, Lea Cristina, Libatique, Nathaniel Joseph C, Tangonan, Gregory L
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: Myo Armband; Motion-based; Geriatric care; Fall detection; Chemistry; Engineering; Geriatric Nursing; Geriatrics
• Online Access: https://archium.ateneo.edu/chemistry-faculty-pubs/162; https://link.springer.com/chapter/10.1007/978-3-030-66169-4_40
• Institution: Ateneo De Manila University

The study of falling; especially for the elderly; is a very critical problem in geriatric care and medicine. This study investigated the potential role of a surface-based electromyography (sEMG) and inertial measurement unit (IMU) sensor package for analyzing and detecting humans falling. Previous studies have only relied on motion sensors only; such as accelerometers and gyroscopes; detecting falling; which completely ignores the muscular force reaction of extremities during the precise moment before a person actually falls. This experiments conducted in this study show promising results of combining the sEMG and accelerometer signals to describe the muscular activity generated as the reflex reaction of the subjects in their arms as they grab onto something to prevent the fall. Further simulations of falling were conducted at a foam pit; specifically deliberate falling and surprised falling. Similar findings were obtained in this scenario where the subjects did not have anything to grab onto upon falling; which cancan be attributed to the fact that the subjects feel the same sensation of reflex and panic whether deliberately falling or surprised falling.