Fall is the leading cause of injury among elderly. One in three adults over 65 falls each year. Of those who fall,20% to 30% suffer moderate to severe injuries and increase their risk of early death. In 2015, the total medicalcosts related to fall injuries for people 65 and older was over $50 billion. Currently, there are devices that usemotion sensors (accelerometers) to detect imminent falls and inflate micro-airbags located in garments worn bythe users to protect from injury. The literature has shown that wearable solutions based on motion sensinghave low detection accuracy and suffer from false-positive events (the airbag may erroneously deploy duringdaily activities after interpreting abrupt movements as falls). GraceFall, Inc. (GFI) will develop a patentprotected fall detection device based on a sensor-fusion algorithm that combines brain (EEG) and body motionsignals to allow reliable fall prediction and injury protection. Our initial findings, along with supporting literature,show that a reliable EEG signal preceding an unexpected loss of balance could be the key to developing acomplete, reliable, ergonomic solution for fall detection and injury prevention, and would have a major impacton maintaining mobility and quality of life in our aging population. Accelerometers reflect body movement and itis difficult to distinguish between loss of stability and other non-fall related activities. The key differencebetween intended actions and unintended loss of balance is the appearance of a "startle" response that can becaptured on most EEG channels. Using EEG sensors will allow us to identify the difference between a fall andother acceleration scenarios. Our goal is to create a device that primarily uses these reliable brain responses,coupled with motion sensors, to accurately detect loss of balance and stability, thereby preventing injuries dueto falling. The goal of the proposed Phase I project is to provide a proof of concept for a future product. Usingexisting fall protection products that rely on motion sensing to detect an imminent fall, we will identify scenariosin which these products have either false-positive (the airbag erroneously deploying in daily activities afterinterpreting an abrupt movement as a fall) or false-negative (the airbag not deploying in a real fall scenario)events. We will simulate these same scenarios on human subjects (Aim 1) and we will characterize thephysiological parameters of the startle response in an elderly population (Aim 2) to refine the sensor fusionalgorithm. The purpose of this proposal is proof of concept that adding a sensor fusion algorithm that combinesthe EEG information with the acceleration data, improves the performance and reliability of the protectionsystem. Project Narrative Injury from falling is a serious concern for the elderly and other populations at risk such as Parkinson or Epilepsy patients. We are developing a device that will read electrical brain (EEG) signals and, together with motion sensors, will be able to predict a fall more accurately than existing methods used today to trigger micro-airbags that protect the wearer from injury. This project will serve as a proof of concept to show that adding an EEG signal to the fall detection algorithm (which has never been done before) can improve the accuracy and reliability of existing products in the market. Acceleration ; Adult ; 21+ years old ; Adult Human ; adulthood ; Elderly ; advanced age ; elders ; geriatric ; late life ; later life ; older adult ; older person ; senior citizen ; Algorithms ; Brain ; Brain Nervous System ; Encephalon ; Cessation of life ; Death ; Electroencephalography ; EEG ; Epilepsy ; Epileptic Seizures ; Epileptics ; Seizure Disorder ; epilepsia ; epileptiform ; epileptogenic ; Equilibrium ; balance ; balance function ; Future ; Goals ; Hip region structure ; Coxa ; Hip ; Pediatric Hospitals ; Children's Hospital ; Human ; Modern Man ; Lead ; Pb element ; heavy metal Pb ; heavy metal lead ; Literature ; Methods ; mortality ; Motion ; Movement ; body movement ; Parkinson Disease ; Paralysis Agitans ; Parkinson ; Parkinson's disease ; Parkinsons disease ; Primary Parkinsonism ; Legal patent ; Patents ; Patients ; Philadelphia ; Physiology ; Posture ; Protective Devices ; safety equipment ; Public Health ; Quality of life ; QOL ; Reflex action ; Reflex ; Research ; Risk ; Signal Transduction ; Cell Communication and Signaling ; Cell Signaling ; Intracellular Communication and Signaling ; Signal Transduction Systems ; Signaling ; biological signal transduction ; Startle Reaction ; startle response ; Technology ; Translating ; falls ; Medical Care Costs ; medical costs ; injury prevention ; Air Bags ; Airbags ; Injury ; injuries ; base ; human subject ; sensor ; improved ; Body measure procedure ; Body Measures ; Anterior ; Phase ; Physiological ; Physiologic ; Peripheral Nervous System ; Link ; young adult ; adult youth ; young adulthood ; Populations at Risk ; Event ; Reaction ; Sensory ; Pattern ; System ; Performance ; Participant ; Prevention ; Devices ; Appearance ; Sampling ; response ; preventing ; prevent ; Data ; Detection ; Motor ; Image ; imaging ; Output ; neuromuscular ; Population ; Coupled ; aging population ; aged population ; population aging ; Accelerometer ; accelerometry ; activity monitor ; activity tracker ; fall injury ; fall related injury ; injurious falls ; experimental study ; experiment ; experimental research ; wearable device ; wearable electronics ; wearable technology ; motion sensor ; severe injury ; critical injury ; devastating injury ;
