A method predicts the fall risk of a user based on a machine learning model. The model is trained using data about the user, which may be from wearable sensors and depth sensors, manually input by the user, and received from other types of sources. Data about a population of users and data from structured tests completed by the user can also be used to train the model. The model uses features and motifs discovered based on the data that correlate to fall risk events to update fall risk scores and predictions. The user is provided a recommendation describing how the user can reduce a predicted fall risk for the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving, from one or more sensors worn by a user or placed near a user, sensor data tracked for determining fall risk for the user; receiving, from the user or from records associated with the user, one or more user inputs providing data about factors for the user that indicate the fall risk of the user; collecting, from one or more external sources, data about fall risk of a population of users; providing the received sensor data, the received one or more user inputs, and the collected data from the one or more external sources to a machine learning model configured for determining fall risk of users, the machine learning model having been trained based on data about a plurality of users that was received from sensors worn by or placed near a user of the plurality of users, from user inputs by a user of the plurality of users, and from external sources. determining a fall risk score for the user from the machine learning model based on the data about the user, the fall risk score used in determining a prediction as to the likelihood of the user experiencing a fall; and notifying, in response to the determined fall risk score being greater than a predetermined threshold, the user with information about the fall risk score and the determined prediction as to the likelihood of the user experiencing a fall. 2. The method of claim 1, further comprising conducting a structured test with the user regularly over time, the structured test including asking the user to perform certain actions to test a current state of the user and current fall risk for the user, wherein results of the structured test are used in training the machine learning model. 3. The method of claim 2, wherein the structured test comprises a test for measuring a mean radius of trace of the user while standing, the test comprising: providing instructions to the user to stand up for the structured test; measuring, with sensor data collected from the one or more sensors, a starting position of the user and a mean radius of trace of the user while the user is standing over a period of time, the mean radius of trace representing the average distance that the user deviated from the starting position over the period of time; and updating the prediction of the fall risk of the user based on the measured sway radius. 4. The method of claim 2, further comprising periodically retraining the machine learning model based on data collected during the structured test completed by the user. 5. The method of claim 1, further comprising: generating a recommendation describing how the user can reduce the determined fall risk for the user based on data obtained from the one or more sensors, the one or more user inputs, and the one or more external sources; and providing the recommendation to the user or a person associated with the user. 6. The method of claim 5, wherein the recommendation comprises a description of conditions in an environment of the user that the user should change to reduce the determined fall risk or a description of a change to the user's posture or gait to reduce the determined fall risk. 7. The method of claim 1, further comprising performing activity classification on the user based on the received sensor data, the activity classification used to determine a daily activity type and corresponding duration of the user and to determine based on body position of the user whether an assistive device is used by the user. 8. The method of claim 1, wherein the one or more sensors comprise at least one depth sensor placed in a room in which fall risk of the user is tracked, the at least one depth sensor taking measurements of a posture of the user while standing, the posture mapped to one or more axes to determine a center of gravity of the user, the center of gravity used in training the machine learning model. 9. The method of claim 1, wherein the user inputs comprise information associated with one or more of the following: the user's age; the user's history of previous falls; the user's use of assistive devices; the user's vision; the user's history of neurological diseases; and lighting conditions. 10. The method of claim 1, wherein the predetermined threshold is based on the information associated with the mean and standard deviation of the number of falls experienced by users per a predetermined period of time within the population of users. 11. The method of claim 1, wherein providing the received sensor data, the received one or more user inputs, and the collected data from the one or more external sources to the machine learning model further comprises: generating a multi-dimensional matrix including the data about the user organized by dimension; and providing features identified using the multi-dimensional matrix to the machine learning model, the features used to train the machine learning model. 12. A computer program product stored on a non-transitory computer-readable medium that includes instructions that, when loaded into memory, cause a processor to perform a method, the method comprising: receiving, from one or more sensors worn by a user or placed near a user, sensor data tracked for determining fall risk for the user; receiving, from the user or from records associated with the user, one or more user inputs providing data about factors for the user that indicate the fall risk of the user; collecting, from one or more external sources, data about fall risk of a population of users; providing the received sensor data, the received one or more user inputs, and the collected data from the one or more external sources to a machine learning model configured for determining fall risk of users, the machine learning model having been trained based on data about a plurality of users that was received from sensors worn by or placed near a user of the plurality of users, from user inputs by a user of the plurality of users, and from external sources. determining a fall risk score for the user from the machine learning model based on the data about the user, the fall risk score used in determining a prediction as to the likelihood of the user experiencing a fall; and notifying, in response to the determined fall risk score being greater than a predetermined threshold, the user with information about the fall risk score and the determined prediction as to the likelihood of the user experiencing a fall. 13. The computer program product of claim 12, further comprising conducting a structured test with the user regularly over time, the structured test including asking the user to perform certain actions to test a current state of the user and current fall risk for the user, wherein results of the structured test are used in training the machine learning model. 14. The computer program product of claim 12, further comprising: generating a recommendation describing how the user can reduce the determined fall risk for the user based on data obtained from the one or more sensors, the one or more user inputs, and the one or more external sources; and providing the recommendation to the user or a person associated with the user. 15. The computer program product of claim 12, further comprising performing activity classification on the user based on the received sensor data, the activity classification used to determine a daily activity type and corresponding duration of the user and to determine based on body position of the user whether an assistive device is used by the user. 16. The computer program product of claim 12, wherein providing the received sensor data, the received one or more user inputs, and the collected data from the one or more external sources to the machine learning model further comprises: generating a multi-dimensional matrix including the data about the user organized by dimension; and providing features identified using the multi-dimensional matrix to the machine learning model, the features used to train the machine learning model. 17. A system comprising: a processor configured to execute instructions; a non-transitory computer-readable memory storing instructions executable by the processor and causing the processor to carry out the steps of: receiving, from one or more sensors worn by a user or placed near a user, sensor data tracked for determining fall risk for the user; receiving, from the user or from records associated with the user, one or more user inputs providing data about factors for the user that indicate the fall risk of the user; collecting, from one or more external sources, data about fall risk of a population of users; providing the received sensor data, the received one or more user inputs, and the collected data from the one or more external sources to a machine learning model configured for determining fall risk of users, the machine learning model having been trained based on data about a plurality of users that was received from sensors worn by or placed near a user of the plurality of users, from user inputs by a user of the plurality of users, and from external sources. determining a fall risk score for the user from the machine learning model based on the data about the user, the fall risk score used in determining a prediction as to the likelihood of the user experiencing a fall; and notifying, in response to the determined fall risk score being greater than a predetermined threshold, the user with information about the fall risk score and the determined prediction as to the likelihood of the user experiencing a fall. 18. The system of claim 17, further comprising conducting a structured test with the user regularly over time, the structured test including asking the user to perform certain actions to test a current state of the user and current fall risk for the user, wherein results of the structured test are used in training the machine learning model. 19. The system of claim 17, further comprising: generating a recommendation describing how the user can reduce the determined fall risk for the user based on data obtained from the one or more sensors, the one or more user inputs, and the one or more external sources; and providing the recommendation to the user or a person associated with the user. 20. The system of claim 17, further comprising performing activity classification on the user based on the received sensor data, the activity classification used to determine a daily activity type and corresponding duration of the user and to determine based on body position of the user whether an assistive device is used by the user. 21. The system of claim 17, wherein providing the received sensor data, the received one or more user inputs, and the collected data from the one or more external sources to the machine learning model further comprises: generating a multi-dimensional matrix including the data about the user organized by dimension; and providing features identified using the multi-dimensional matrix to the machine learning model, the features used to train the machine learning model.