Method and system for detecting a fall based on comparing data to criteria derived from multiple fall data sets

1. A device for characterizing potential fall event data, comprising:

a processor configured for:

loading a first acceleration magnitude threshold value from a memory;

loading a second acceleration magnitude threshold value from a memory;

sampling acceleration data received from an acceleration measuring means of the device;

storing the sampled acceleration data into a memory capable of storing a predetermined number of sampled acceleration data samples;

determining a calculated magnitude acceleration value from the acceleration data for each sample;

comparing the calculated magnitude acceleration value to the first acceleration magnitude threshold value for each sample;

comparing the calculated magnitude acceleration value to the second acceleration magnitude threshold value if the calculated magnitude acceleration value does not exceed the first acceleration magnitude threshold value, wherein the first acceleration magnitude threshold value corresponds to a first characteristic segmentation of fall event data sets and the second acceleration magnitude threshold value corresponds to a second characteristic segmentation of fall event data sets; and

outputting a signal indicating that a fall may have occurred if the calculated magnitude exceeds either the first or the second acceleration magnitude threshold values.

2. The device of claim 1 further comprising performing further analysis of the stored sampled acceleration data if the calculated magnitude acceleration value exceeds the second acceleration magnitude threshold value.

3. The device of claim 2 wherein the further analysis includes:

sampling, orientation information from an orientation measuring means of the device;

determining a change in orientation of the device between a time corresponding to a sample where the calculated magnitude acceleration value exceeded either of the first or the second acceleration magnitude threshold values and a time corresponding to a sample that precedes the sample where the calculated magnitude acceleration value exceeded either of the first or the second acceleration magnitude threshold values by the predetermined number of samples;

outputting a signal indicating that a fall may have occurred if the determined change in the orientation of the device exceeds a predetermined orientation threshold; and

wherein the predetermined orientation threshold corresponds to one of the first or second acceleration magnitude threshold value, and wherein the predetermined orientation threshold has a different value if it corresponds to the first acceleration magnitude threshold value than if it corresponds to the second acceleration magnitude threshold value.

4. The device of claim 1 wherein the processor is further configured for:

sampling orientation information from an orientation measuring means of the device;

determining a change in orientation of the device between a time corresponding to a sample where the calculated magnitude acceleration value exceeded either of the first or the second acceleration magnitude threshold values and a time corresponding to a sample that precedes the sample where the calculated magnitude acceleration value exceeded either of the first or the second acceleration magnitude threshold values by the predetermined number of samples;

outputting a signal indicating that a fall may have occurred if the determined change in the orientation of the device exceeds a predetermined orientation threshold; and

wherein the predetermined orientation threshold corresponds to one of the first or second acceleration magnitude threshold values, and wherein the predetermined orientation threshold has the same value regardless of whether it corresponds to the first acceleration threshold value or to the second acceleration magnitude threshold value if the first acceleration magnitude threshold value and the second acceleration magnitude threshold value are substantially the same.

5. The device of claim 1 wherein each sampled acceleration data includes acceleration magnitudes for three axes, and wherein the processor determines for each sample the calculated magnitude acceleration value by calculating a square root of a sum of squares of the acceleration magnitudes for the three axes.

6. The device of claim 1 wherein each calculated magnitude acceleration value is processed with a filter into a filtered calculated magnitude acceleration value before comparing to either of the first and second acceleration magnitude threshold values.

7. The device of claim 2 wherein the further analysis includes comparison of at least one of an integral, a mean, a median, an average, a standard deviation, and Fourier analysis result to corresponding integral, mean, median, average, standard deviation, or Fourier analysis result values for characteristic segmentation.

8. A device for characterizing potential fall event data, comprising:

a processor configured for:

loading a first acceleration magnitude threshold value and a gravitational proxy value from a memory;

sampling acceleration information from an acceleration measuring means of the device at a predetermined sample rate into sampled acceleration data;

determining a calculated magnitude acceleration value from the sampled acceleration data for each sample;

storing the calculated magnitude acceleration values into a memory capable of storing a predetermined number of calculated magnitude acceleration values corresponding to a predetermined number of samples of acceleration data;

comparing each calculated magnitude acceleration value to the first acceleration magnitude threshold value;

evaluating the predetermined number of calculated magnitude acceleration values to determine a number of samples composing a fall evaluation period, wherein a calculated magnitude acceleration value that drops below the gravitational proxy value indicates a beginning of the fall evaluation period, and wherein the predetermined number of calculated magnitude acceleration values corresponds to samples during the fall evaluation period occurring before a sample having a calculated magnitude acceleration value that exceeds the first acceleration magnitude threshold value;

comparing the number of samples in the fall evaluation period to a fall evaluation period limit value, wherein the fall evaluation period limit value is determined from empirical fall data; and

outputting a signal indicating that a fall may have occurred if the number of samples in the fall evaluation period does not exceed the fall evaluation period limit value.

9. The device of claim 8 , wherein the predetermined fall evaluation period limit value is a predetermined amount of time.

10. The device of claim 8 wherein the predetermined fall evaluation period limit value is a predetermined number of samples.

11. The device of claim 8 wherein the predetermined fall evaluation period limit value corresponds to an empirically determined period of free-fall for fall event data sets in a characteristic segmentation that corresponds to the first acceleration magnitude threshold value.

12. The device of claim 8 wherein each calculated magnitude acceleration value is processed with a filter into a filtered calculated magnitude acceleration value before comparing to the first acceleration magnitude threshold value.

13. A device for characterizing potential fall event data, comprising:

a processor configured for:

loading a first elevation change threshold value from a memory;

loading a second elevation change threshold value from a memory;

determining that data from one, or more, measurement sensor means, corresponding to one, or more, respective parameters, indicates that a trigger event has occurred;

evaluating data generated, by at least one of the measurement sensor means that measures pressure, before the trigger event with data generated after the trigger event to result in an elevation chance value;

comparing the elevation change value with the first elevation change threshold value;

comparing data from one, or more, of the measurement sensor means to first criteria for the one, or more, corresponding parameters if the elevation change value exceeds the first elevation change threshold value, wherein the first criteria corresponds to the first elevation change threshold value;

characterizing the potential fall event data as a fall event if the data from the one, or more, measurement sensor means satisfies the first criteria;

if the data from the one, or more, measurement sensor means does not satisfy the first criteria, comparing data from one, or more, of the measurement sensor means to second criteria for the one, or more, corresponding parameters if the elevation change value exceeds the second elevation change threshold value, wherein the second criteria corresponds to the second elevation change threshold value; and

characterizing the potential fall event data as a fall event if the data from the one, or more, measurement sensor means satisfies the second criteria.

14. The device of claim 13 wherein the measurement sensor means is at least one of: an accelerometer device, a barometric pressure measuring device, a gyroscope, a sound measuring device, a light measuring device, magnetic field sensing device, and a strain measuring device that includes a strain gage.

15. The device of claim 13 wherein the data indicating the trigger event is data from an accelerometer device.

16. The device of claim 13 wherein the data indicating the trigger event is an acceleration magnitude spike.

17. The device of claim 13 further comprising performing further analysis of the potential fall event data before characterizing the potential fail event data as a fall if the elevation change value exceeds a second elevation change threshold value;

wherein the further analysis includes comparison of at least one of an integral, a mean, a median, an average, a standard deviation, and Fourier analysis result to corresponding integral, mean, median, average, standard deviation, or Fourier analysis result values for characteristic segmentation.