SBIR-STTR Award

Falls are the leading cause of death due to injury. This simple motion is so common that 30% of community dwelling older adults, and 50% of long term care facilities will experience a fall in the coming year. The risk of falling substantially increases for those having Alzheimer?s disease and related dementias, and those with Parkinson?s disease. The financial burden is significant with fall-related costs being $32 billion in 2016, with costs expected to rise to $44 billion by 2020. Care-giving institutions, who are often liable for the well-being of their patients, bear a substantial portion of the cost. A fall can cost $12,817 per case for long term care facilities and can cost $13,000 per case for hospitals. Commercially available fall detection systems operate wearable pendant-based devices that a patient presses after experiencing a fall. Newer generations of these systems also incorporate accelerometers that are reportedly able to detect falls. These systems are user- dependent, meaning that a patient must be wearing the pendant for it to work which older adults, particularly those with cognitive impairments, often do not. Furthermore, the patient has to be cognizant to press the button to call for aid if the pendant does not activate during a fall. This is unlikely to occur as even when people are not cognitively impaired they will only activate the system 20% of the time. There is a clear need for an automated, user-independent fall detection system. Better yet would be a system that can detect non-injurious falls or changes in gait, both of which are predictors of oncoming injurious falls. ASSET, in partnership with the University of South Carolina, have developed a patent-pending, floor vibration fall detection and gait analysis prototype system that can detect non-injurious falls and collect gait information whilst being user-independent. The innovation has the ability to firmly place control of liability back into the hands of care-giving institutions much like what a fire alarm does for property damage from fires, and potentially saving ~$2.2 billion in fall-related costs with just 5% market adoption. During Phase I, the system will be tested and algorithms refined in a deployment of eight systems to dwellings whose residents are at high risk of falling, and compare the gait analysis capabilities to that of the GAITRite Mat and ADPM system with an expected n of 58. Focus groups of stakeholders in the use of the innovation will also be conducted. The work in Phase I are expected to result in a refined alpha version of the fall detection and gait analysis technology, with goals of >90% accuracy in fall detection and comparable gait information results to that of existing standards. Strong results in Phase I will warrant further development in a larger deployment of the technology in Phase II to validate and finalize the technology in preparation for commercialization.

Project Terms:
Accelerometer; Adoption; Adult; Age; Algorithms; Alzheimer's Disease; Back; base; caregiving; Cause of Death; Cessation of life; Clinic; commercialization; Communities; Continuity of Patient Care; cost; Data; Dementia; Detection; Development; Devices; Elderly; Emergency response; experience; fall risk; falls; Fire - disasters; Floor; Focus Groups; Future; Gait; gait examination; Gait speed; Generations; Goals; Hand; Health; Health care facility; Healthcare; high risk; Home environment; Hospital Nursing; Hospitals; Human; Impaired cognition; improved; Industry Standard; Injury; innovation; innovative technologies; Institution; Insurance; Insurance Carriers; Legal patent; Letters; Long-Term Care; Measurement; Measures; Medical center; Methods; Minor; Motion; Nursing Homes; Operating System; Output; Parkinson Disease; Patient Monitoring; Patients; Patients' Rooms; Penetration; Personal Satisfaction; Phase; Physical therapy; Preparation; Preventive healthcare; Property; prototype; recruit; Reporting; Research; Sales; Savings; Signal Transduction; South Carolina; System; Technology; Testing; Time; TimeLine; Universities; Ursidae Family; Veterans; vibration; volunteer; Work;

Falls are the leading cause of death due to injury. Falls are so common that 30% of community dwelling olderadults, and 50% of residents in Care Facilities will experience a fall in the coming year. The risk of fallingsubstantially increases for those having Alzheimer's disease and related dementias. The financial burden issignificant with fall-related costs being $50 billion. Care Facilities, who are often liable for the well-being of theirpatients, bear a substantial portion of the cost. A fall can cost $10,484 per case for Care Facilities.Commercially available fall detection systems operate via wearable pendant-based devices that patients pressafter experiencing a fall. Newer generations of these systems also incorporate accelerometers that arereportedly able to detect falls. These systems are patient-dependent, meaning that a patient must be wearingthe pendant for it to work which older adults, particularly those with cognitive impairments, often do not.Furthermore, the patient has to be cognizant to press the button to call for aid if the pendant does not activateduring a fall. This is unlikely to occur as even when people are not cognitively impaired, they will only activatethe system 20% of the time.There is a clear need for an automated, patient-independent fall detection system to fill the gaps left by currentapproaches. Better yet would be a system that can detect non-injurious falls or changes in gait parameters,both of which are predictors of oncoming injurious falls. ASSET, in partnership with the University of SouthCarolina, has developed a patented, floor vibration monitoring system that can detect falls and collect gaitinformation whilst being patient independent. The innovative product has the ability to firmly place control ofliability back into the hands of Care Facilities much like what a fire alarm does for property damage from fires,and potentially saving ~$2.2 billion in fall-related costs with just 5% market adoption.During Phase II our overall goals are two-fold, first to further develop a system that does not rely on the patientto operate, overcoming the limitation of wearable systems and can additionally capture falls that are a predictorof oncoming injurious falls. We will monitor common areas with our vibration sensor system in places whereCare Facility staff report the majority of falls occur. To accomplish the methods, we will use the Care Facilities'common area video camera system to corroborate sensor fall activations are actual falls. Second, we will usethe same passive system technology to explore gait measurement as an additional indicator of an oncominghealth changes such as a fall. We will use gait parameter measuring technology in a Care Facility medicaloffice for regular vital monitoring. We will use gait measurements with Facility fall reports to explore theeffectiveness of our predictive fall risk model against industry-standard fall risk assessments. Future directionswill include ASSET launching Beta trials of the product among Care Facilities for final refinement of the productbefore full release to the public.

Public Health Relevance Statement:
Project Narrative Falls are the leading cause of injury-related death in adults above the age of 65, and Care Facilities (e.g. assisted living, nursing homes) bear a substantial portion of the cost due to liability. Yet, this simple motion can be avoided if non-injurious falls and changes in gait parameters, both of which are predictors of future injurious falls, are recognized in time for preventative healthcare measures to be enacted. ASSET, in partnership with the University of South Carolina, is leveraging floor vibrations to detect non-injurious falls and changes in gait parameters as an affordable solution that alerts Care Facilities before an injurious fall occurs.

Project Terms:
Adoption ; Adult ; 21+ years old ; Adult Human ; adulthood ; Age ; ages ; Elderly ; advanced age ; elders ; geriatric ; late life ; later life ; older adult ; older person ; senior citizen ; Back ; Dorsum ; Ursidae Family ; Bears ; Ursidae ; bear ; Beds ; Cause of Death ; Communities ; Disasters ; Fire - disasters ; Fires ; fire ; Floor ; Future ; Gait ; Goals ; Hand ; Health ; Health care facility ; Health Facilities ; Healthcare Facility ; care facilities ; Health Status ; Level of Health ; Recording of previous events ; History ; Hospitals ; Insurance ; Insurance Carriers ; Insurers ; Intelligence ; Methods ; Motion ; Persons ; Discipline of Nursing ; Nursing ; Nursing Field ; Nursing Profession ; Nursing Homes ; nursing home ; Legal patent ; Patents ; Patient Monitoring ; Patients ; Patients' Rooms ; Personal Satisfaction ; well-being ; wellbeing ; Physicians' Offices ; pressure ; Quality of life ; QOL ; Risk ; Savings ; South Carolina ; Technology ; Time ; United States Department of Veterans Affairs ; United States Veterans Administration ; Veterans Administration ; Veterans Affairs ; Universities ; Work ; Generations ; Measures ; Privacy ; falls ; Healthcare ; health care ; Risk Assessment ; prisma ; TimeLine ; Injury ; injuries ; base ; sensor ; improved ; Left ; Area ; Penetration ; Phase ; Medical ; insight ; Individual ; Measurement ; Letters ; tool ; Lutherans ; Lutheran Church ; Cognitive Disturbance ; Cognitive Impairment ; Cognitive decline ; Cognitive function abnormal ; Disturbance in cognition ; cognitive dysfunction ; cognitive loss ; Impaired cognition ; Adopted ; System ; vibration ; Gait Analysis ; gait examination ; Medical center ; innovative technologies ; experience ; 65+ years old ; Aged 65 and Over ; age 65 and greater ; age 65 and older ; aged 65 and greater ; aged ≥65 ; old age ; human old age (65+) ; Devices ; Reporting ; Modeling ; Property ; assisted living ; assistive living ; assistive living facilities ; Assisted Living Facilities ; fall risk ; Effectiveness ; Legal ; Data ; Detection ; Monitor ; Operating System ; cost ; predictive modeling ; computer based prediction ; prediction model ; design ; designing ; innovation ; innovate ; innovative ; combat ; operation ; Industry Standard ; Accelerometer ; accelerometry ; activity monitor ; activity tracker ; fall injury ; fall related injury ; injurious falls ; Preventive healthcare ; Preventative health care ; Preventative healthcare ; Preventive health care ; Gait speed ; wearable sensor technology ; body sensor ; body worn sensor ; wearable biosensor ; wearable sensor ; wearable system ; wireless sensor technology ; wearable device ; wearable electronics ; wearable technology ; care providers ; primary care provider ; aging in place ; age in place ; Alzheimer's disease related dementia ; AD related dementia ; ADRD ; Alzheimer related dementia ; feature detection ; feature recognition ; Financial Hardship ; financial burden ; financial distress ; financial strain ; financial stress ; injury-related death ; detection platform ; detection system ; Home ;