Adaptive classification of fall detection for personal emergency response systems
Techniques described herein relate to the classification of fall events for PER (personal emergency response) devices. In one implementation, data relating to acceleration events that occurred at the PER devices may be received. The data relating to the acceleration events may be associated with indications of whether the acceleration events correspond to fall events of users of the PER devices. A classification model may be trained based on the data relating to the acceleration events and the indications of whether the data relating to the acceleration events corresponds to the fall events. The classification model may be transmitted to at least some of the PER devices to update a previous version of the classification model at the at least some of the PER devices.
1. A method, implemented by one or more devices, comprising:
receiving, by the one or more devices and from a plurality of personal emergency response (PER) devices, data relating to acceleration events that occurred at the PER devices;
associating, by the one or more devices, the data relating to the acceleration events with indications of whether the data relating to the acceleration events corresponds to fall events of users of the PER devices;
training, by the one or more devices, a classification model based on the data relating to the acceleration events and the indications of whether the data relating to the acceleration events corresponds to the fall events;
customizing the classification model, based on user-specific data, for a particular user of the PER devices, the customization including removing nodes of the classification model that are determined, based on the user-specific data for the particular user, to not affect an output of the classification model for the particular user;
transmitting, by one or more devices, the customized classification model to at least some of the PER devices to update a previous version of the classification model at the at least some of the PER devices;
identifying high-risk users, out of a plurality of users associated with the PER devices, based on a number of previous fall events associated with one or more users, of the plurality of users;
performing a clustering operation based on the identified high-risk users; and
determining additional high-risk users based on a result of the clustering operation.
2. The method of claim 1 , wherein the training further includes:
associating the user-specific data with the data relating to each acceleration event; and
training the classification model based additionally on the user-specific data, to obtain the customized classification models.
3. The method of claim 1 , further comprising:
splitting the data relating to the acceleration events into a set of training data and a set of cross-validation data,
wherein the training of the classification model is performed based on the set of training data.
4. The method of claim 3 , further comprising:
testing an accuracy of the classification model, based on the set of cross-validation data, to determine whether the classification model satisfies a threshold level of accuracy,
wherein the transmitting includes transmitting the classification model only when the testing indicates that the classification model satisfies the threshold level of accuracy.
5. The method of claim 4 , wherein testing the accuracy of the classification model includes defining a first threshold level based on an acceptable portion of true negatives and a second threshold level based on an acceptable portion of false positives, the testing further including:
determining whether the classification model satisfies the first threshold and the second threshold.
6. The method of claim 1 , wherein the classification model includes a binary classification tree (BCT).
7. The method of claim 6 , further comprising:
pruning the BCT to reduce a complexity of the BCT.
8. The method of claim 1 , wherein the data relating to the acceleration events includes:
first data derived based on the output of an accelerometer; and
second data derived based on the output of at least one of a barometric pressure sensor, a gyroscope, a magnetometer, a proximity sensor, heart rate sensor, glucose sensor, audio sensor, altimeter sensor, blood oxygen sensor, photo diode sensor, infrared sensor, or temperature sensor.
9. The method of claim 8 , wherein
the first data includes at least one of a maximum acceleration value, a measurement of energy over a particular time period, an estimated slope from an observed maximum to minimum acceleration value, an approximate area beneath acceleration values, a standard deviation of acceleration measurements, a combination of two or more acceleration sensor readings, or an entropy of acceleration measurements; and
the second data includes at least one of a pressure value, pressure statistic derived from combining two or more pressure value readings, gyroscopic statistic derived from combining two or more gyroscopic readings, values relating to orientation of the PER device, or a value relating to body temperature, a glucose level, a heart rate, or blood pressure of the user.
10. The method of claim 1 , wherein the associating the data relating to the relating to the acceleration events, further includes:
receiving a first indication of whether a user activated a cancel button on a PER device; or
receiving a second indication of whether emergency response personnel were dispatched to the user of the PER device; and
using the first or second indication to determine whether the user experienced a fall event.
11. The method of claim 1 , wherein removing nodes of the classification model includes removing nodes of a Binary Classification Tree (BCT) to tune the classification model for a particular user.
12. One or more devices comprising:
at least one processor; and
a memory including instructions, that when executed by the at least one processor, cause the at least one processor to:
receive, from a plurality of personal emergency response (PER) devices, data relating to acceleration events that occurred at the PER devices;
associate the data relating to the acceleration events with indications of whether the data relating to the acceleration events corresponds to fall events of users of the PER devices;
train a classification model based on the data relating to the acceleration events and the indications of whether the data relating to the acceleration events corresponds to the fall events;
customize the classification model, based on user-specific data, for a particular user of the PER device, the customization including removing nodes of the classification model that are determined, based on the user-specific data for the particular user, to not affect an output of the classification model for the particular user;
transmit the customized classification model to at least some of the PER devices to update a previous version of the classification model at the at least some of the PER devices;
identify high-risk users, out of a plurality of users associated with the PER devices, based on a number of previous fall events associated with one or more users, of the plurality of users;
perform a clustering operation based on the identified high-risk users; and
determine additional high-risk users based on a result of the clustering operation.
13. The one or more devices of claim 12 , wherein the instructions to train the classification model additionally include instructions, that when executed by the at least one processor, cause the at least one processor to:
associate the user-specific data with the data relating to each acceleration event; and
train the classification model based additionally on the user-specific data, to obtain the customized classification models.
14. The one or more devices of claim 12 , wherein the memory additionally includes instructions, that when executed by the at least one processor, cause the at least one processor to:
split the data relating to the acceleration events into a set of training data and a set of cross-validation data,
wherein the training of the classification model is performed based on the set of training data.
15. The one or more devices of claim 14 , wherein the memory additionally includes instructions, that when executed by the at least one processor, cause the at least one processor to:
test an accuracy of the classification model, based on the set of cross-validation data, to determine whether the classification model satisfies a threshold level of accuracy,
wherein the transmitting includes transmitting the classification model only when the testing indicates that the classification model satisfies the threshold level of accuracy.
16. The one or more devices of claim 15 , wherein testing the accuracy of the classification model includes defining a first threshold level based on an acceptable portion of true negatives and a second threshold level based on an acceptable portion of false positives, wherein when testing the accuracy of the classification model, the memory additionally includes instructions, that when executed by the at least one processor, cause the at least one processor to:
determine whether the classification model satisfies the first threshold and the second threshold.
17. The one or more devices of claim 12 wherein the classification model includes a binary classification tree (BCT).
18. The one or more devices of claim 12 , wherein the data relating to the acceleration events includes:
data derived based on the output of an accelerometer; and
data derived based on the output of at least one of a barometric pressure sensor, a gyroscope, a magnetometer, a proximity sensor, heart rate sensor, glucose sensor, audio sensor, altimeter sensor, blood oxygen sensor, photo diode sensor, infrared sensor, or temperature sensor.
19. The one or more devices of claim 12 , wherein removing nodes of the classification model includes removing nodes of a Binary Classification Tree (BCT) to tune the classification model for the particular user.