DETECTING FALLS USING A MOBILE DEVICE

The abstract describes a method where a mobile device detects an impact experienced by a user, determines motion characteristics before and after the impact, and generates a notification if the user has fallen.

Mobile device obtains signal indicating acceleration measured by a sensor over a time period
Mobile device determines impact experienced by user based on the signal
Mobile device analyzes first motion characteristics of the user before the impact and second motion characteristics after the impact
Mobile device identifies if the user has fallen based on the impact and motion characteristics
Mobile device generates a notification indicating that the user has fallen

Potential Applications

This technology could be applied in:

Fall detection systems for elderly individuals
Sports monitoring devices for athletes
Safety features in wearable devices

Problems Solved

Quick detection of falls for immediate assistance
Monitoring user activity and impact levels
Providing timely notifications for potential injuries

Benefits

Enhances user safety and well-being
Improves response time in case of emergencies
Offers peace of mind for users and caregivers

Potential Commercial Applications

Health and fitness tracking devices
Smart home systems with safety features
Wearable technology for outdoor activities

Possible Prior Art

One possible prior art could be the use of accelerometers in wearable devices to track user movement and detect falls.

What is the accuracy rate of fall detection using this technology?

The accuracy rate of fall detection using this technology may vary depending on the specific implementation and sensor capabilities. Further testing and validation studies would be needed to determine the exact accuracy rate.

How does this technology differentiate between intentional movements and falls?

This technology may differentiate between intentional movements and falls by analyzing the impact patterns, motion characteristics, and context of the user's activity. Machine learning algorithms could also be employed to improve the accuracy of fall detection and reduce false alarms.

Original Abstract Submitted

in an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. the mobile device determines an impact experienced by the user based on the signal. the mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. the mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Apple inc. (20240127683). DETECTING FALLS USING A MOBILE DEVICE simplified abstract

Contents

DETECTING FALLS USING A MOBILE DEVICE

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