Fall validation with privacy-aware monitoring

1. A fall detector, comprising

an image capture device,

a microphone, and

a processor connected to a memory that includes machine-readable code defining an algorithm for controlling the image capture device and microphone to monitor a person for purposes of detecting falls, wherein the image capture device is adapted to capture only select information, and the algorithm is configured to identify flagging events based on falls detected by the image capture device, and to capture sound data from the microphone to supplement information not captured by the image capture device and to corroborate potential falls detected by the image capture device.

2. The fall detector of claim 1 , wherein the data not captured by the image capture device includes dead zones not covered by the image capture device, and privacy regions designated in the algorithm as private areas, which are obscured.

3. The fall detector of claim 1 , wherein the image capture device includes one or more of a video camera, a camera operating in a frequency range outside the visual spectrum that inherently limits image quality, a digital camera system where the pixel count is kept low enough to limit image quality to protect privacy, and a radar or sonar device.

4. The fall detector of claim 1 , wherein the processor is configured to compare verbal and non-verbal input from the microphone to pre-recorded sounds of people falling including exclamations by third parties or by the person being monitored, which are associated with a falling event, in order to supplement information not captured by the image capture device and to corroborate a potential fall detected by the image capture device.

5. The fall detector of claim 4 , wherein the algorithm includes an artificial intelligence (AI) system trained on fall image data to identify falls and potential falls.

6. The fall detector of claim 5 , wherein the AI system parses the image data captured by the image capture device and identifies trigger events that require corroboration by a second data source, and flagging events that warrant notifying a third-party.

7. The fall detector of claim 6 , wherein the second data source comprises sound data from the microphone for the same time frame.

8. The fall detector of claim 7 , further comprising a speaker connected to the processor, wherein the algorithm is configured to generate a message to the person being monitored, in the event of a fall or potential fall being detected by the image capture device or microphone.

9. The fall detector of claim 8 , wherein the message comprises one or more of: a consoling message in the event of a fall being detected that help is on the way, and a confirmation message in the event of a potential fall to verify that the person did fall, and using the microphone to capture any response for purposes of corroborating data from the image capture device or microphone.

10. A fall detector comprising

a video camera to capture video data,

a processor, and

a memory connected to the processor, wherein the memory includes-machine-readable code adapted to parse the video data to identify fall events, and the machine-readable code is adapted to generate an avatar of each human being captured or recorded by the video camera.

11. The fall detector of claim 10 , further comprising a motion detector, wherein the machine-readable code is adapted to keep a record of the video data or obfuscated video data only when motion is detected.

12. A method of enhancing the safety of a user in a dwelling, comprising

monitoring the user for falls in the dwelling by capturing image and sound data, and

corroborating the image data and the sound data in the event of a suspected fall, respectively with sound data and image data captured over a corresponding time frame.

13. The method of claim 12 , further comprising corroborating a suspected fall by querying the user about the user's condition.