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Apple to advance "Fall Detection" by introducing a new Fall Risk Assessment testing process users could take on Mobile Devices

1 cover Fall Assessment test on mobile devices

Apple's first patent for 'Fall Detection' was published in 2018 under application 20190103007. Since that time we've posted several related patent reports on this subject (01, 02, 03, 04 and 05).  Last Thursday, the US Patent & Trademark Office published a patent application from Apple that relates to the science behind a possible next-phase for Fall Detection that covers an advanced testing process users may be able to take with an iPhone, Apple Watch or future Fitness Band.

Assessing Fall Risk of a Mobile Device User

In Apple's patent background they note that every year, nearly 1 in 3 adults over the age of 65 reports falling. Fall death rates are on the rise, increasing 3% per year from 2007 to 2016 among adults 65 and older. The approximate cost of a fall treated in the emergency department, hospital, or outpatient setting is $10,000, and with the aging baby boomer population, it is likely that falls will become an even greater health concern and economic burden. There are evidence-based strategies that have been proven to reduce falls, if individuals can be made aware they have high fall risk and take appropriate action.

Fall risk is most commonly evaluated in a doctor’s office with a patient filling out a questionnaire and potentially doing a short functional assessment such as a Timed Up and Go (TUG) test. The questionnaire asks about a variety of risk factors including prior history of falls, intrinsic mobility limitations, medication usage and depression. All of the reported factors are added up, and if the sum is above a certain threshold, the patient has a high fall risk and follow-up education and interventions are discussed with the patient. The TUG test is a simple test of overall functional mobility, where the patient is asked to stand up from a chair, walk 10 meters, turn around, walk back, and sit back down. This procedure is timed and above a certain time is indicative that the patient may be at increased risk for falls. The completion of these assessments is dependent on integration of fall prevention in primary care practices, which is low and only routinely done for adults 65 years and older.

Apple's patent relates to assessing fall risk of a mobile device user.

Apple notes that in one embodiment, several components are required to estimate and classify the quality of a user’s walking steadiness and subsequently infer their prospective risk of falling. To estimate walking steadiness requires component biomechanical models of steadiness, measures of behavior, and longitudinal evaluation over different timescales. All of the individual biomechanical component models used to estimate steadiness are combined or fused to create a single walking steadiness indicator that informs functional mobility of the user across a wide range of mobility levels and limitations. When assessed at scale across a general population with a wide range of ages, functional status, and prior fall history, the steadiness indicator can be classified into particular categories. The category in which a user is classified indicates they may be at relatively increased likelihood of having mobility risk factors or future risk of falling.

Apple further notes that a mobile device platform (e.g., iPhone, Apple Watch or fitness band) system is disclosed that combines sensors, such as accelerometer, gyroscope, magnetometer, barometer, and Global Navigation Satellite System (GNSS) (e.g., Global Position System (GPS)), to assess the quality and dynamic balance of a user’s walking gait (referred to hereinafter as “walking steadiness”), classifies how the user’s walking steadiness compares to the walking steadiness of the general population, and predicts a likelihood of the user falling (“fall risk”) in the future (e.g., the next 12 months).

Apple's patent FIG. 1A is a block diagram of a system for assessing walking steadiness and fall risk; FIG. 1B illustrates compensatory walking patterns.

2 Fall Detection advancement patent figs 1A  1B

Apple's patent FIG. 2A below illustrates a basic approach for training feature weights for component models; FIG. 2B illustrates training feature weights for component models using stacked models to train to their own residuals; FIG. 3 illustrates a training architecture using an ensemble of machine learning models.

3 Apple patent figs 2a  2b  3 for advanced fall detection

While Apple will make testing for walking steadiness relatively easy, the technology behind this is mind-boggling. Below are some of the sub-categories that Apple discusses in their patent filing:

  • Mobility Metrics
  • Longitudinal Features
  • Sufficiency Checker/Filter
  • Behavioral Discordance Checker
  • Walking Steadiness Component Models
  • Capacity (Strength/Endurance) Model
  • Gait Compensatory (Variability) Model
  • Entropy Metric
  • Degree of Dispersion Metric
  • Anomalous Gait Model
  • Gait Smoothness Model
  • Behavioral Model
  • Fused Inference Model
  • Sources of Truth Data
  • Steadiness and Falls Together
  • Steadiness by Fall Type
  • Training of Feature Weights for Component Models - Basic Approach
  • Training of Feature Weights - Stacked Approach
  • Training of Feature Weights - Age Balancing Approach
  • Training of Feature Weights - Activity Changes
  • Training Ensemble - ML Architecture
  • Fusing Models
  • Loss Function Options, and more.

For more details, review Apple's patent application number US 20230112071.

Apple Inventors

  • Adeeti Ullal: Senior Manager, Health Algorithms - Motion Technologies
  • Asif Khalak: Data Science + Algorithms
  • Mariah Whitmore: Engineering Manager / Motion Scientist
  • Richard Fineman: Senior Health and Fitness Scientist
  • Jaehyun Bae: Motion & Health Algorithm
  • Sheena Sharma: Data Scientist
  • Mark Sena: Motion Scientist
  • Allison Gilmore: Health and Motion Algorithms
  • Edith Arnold: Senior Manager, Engineering
  • Gabriel Blanco: Software Engineering Manager (Motion Engineering)
  • Maryam Etezadi-Amoli: While we couldn't find a LinkedIn profile, there was an article listed on the net that Maryam Etezadi-Amoli is associated with titled: "Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson’s disease," that you could review here.

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