Method and system for motion analysis and fall prevention
First Claim
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1. A system for detecting an emergent fall comprising:
- one or more sensors wearable by one or more users, at least one of the one or more sensors being configured to collect and transmit sensor data, including motion data;
a hub for receiving and labeling the sensor data, wherein labeling the sensor data includes date/time and whether a subsequent fall actually occurred;
a processor on the hub configured to;
classify the sensor data as a data classification including at least whether a fall is emerging or not according to a fall prediction model;
store the data classification, wherein the labeled sensor data is used to create one or more parameters, strings, features, data models, and classes to be used in subsequent data classification via supervised or unsupervised machine learning;
process one or more parameters, where the one or more parameters include time duration and time placement for the one or more strings;
process one or more strings, where the one or more strings comprise one or more streams of sensor data including N-dimensional event phase space information which can be matched to behavioral motion, such that the one or more strings are classified into one or more classes and the one or more classes are used to determine individual and group thresholds used by an alert system; and
match output from the data classification with the one or more parameters, strings, features, and data models from the fall prediction model; and
a transmitter for transmitting information to the alert system and to a repository of data for use by the fall prediction model;
the alert system being configured to send a notification that a risk of a fall is above a threshold based, in part, on a confidence level for a match to the one or more parameters, strings, features, and data models from the fall prediction model to the data classification.
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Abstract
A system and method of motion analysis, fall detection, and fall prediction using machine learning and classifiers. A wearable motion sensor for collecting and transmitting motion data for use in a fall prediction model using features and parameters to classify the motion data and notify when a fall is emergent. Using machine learning, the fall prediction model can be created, implemented, evaluated, and it can evolve over time with additional data. The system and method can use individual data or pool data from various individuals for use in fall prediction.
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Citations
20 Claims
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1. A system for detecting an emergent fall comprising:
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one or more sensors wearable by one or more users, at least one of the one or more sensors being configured to collect and transmit sensor data, including motion data; a hub for receiving and labeling the sensor data, wherein labeling the sensor data includes date/time and whether a subsequent fall actually occurred; a processor on the hub configured to; classify the sensor data as a data classification including at least whether a fall is emerging or not according to a fall prediction model; store the data classification, wherein the labeled sensor data is used to create one or more parameters, strings, features, data models, and classes to be used in subsequent data classification via supervised or unsupervised machine learning; process one or more parameters, where the one or more parameters include time duration and time placement for the one or more strings; process one or more strings, where the one or more strings comprise one or more streams of sensor data including N-dimensional event phase space information which can be matched to behavioral motion, such that the one or more strings are classified into one or more classes and the one or more classes are used to determine individual and group thresholds used by an alert system; and match output from the data classification with the one or more parameters, strings, features, and data models from the fall prediction model; and a transmitter for transmitting information to the alert system and to a repository of data for use by the fall prediction model; the alert system being configured to send a notification that a risk of a fall is above a threshold based, in part, on a confidence level for a match to the one or more parameters, strings, features, and data models from the fall prediction model to the data classification. - View Dependent Claims (2, 3, 4, 5, 6, 20)
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7. A method of detecting an emergent fall comprising:
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providing at least one wearable sensor on at least one user; measuring at least motion data with the at least one sensor; creating a fall prediction model, wherein an input for the fall prediction model comprises at least the motion data and time data; implementing the fall prediction model; comparing at least one stream of sensor data including N-dimensional event phase space information of the motion data with at least one stream of sensor data including N-dimensional event phase space information of the fall prediction model, wherein the at least one stream of sensor data can be matched to behavioral motion, such that the at least one stream of sensor data is classified into one or more classes and the one or more classes are used to determine individual and group thresholds used by an alert system; creating a data classification including at least whether a fall is emerging or not using the comparison of at least the motion data and optionally the time data to the fall prediction model; calculating a probability that, and a time frame within which, a fall is emergent; calculating a confidence level for the probability that a fall is emergent; indicating whether the fall is emergent based, in part, on the data classification; evaluating the fall prediction model in real-time; evolving the fall prediction model with additional data from the at least one sensor on at least one user; determining if the fall is emergent based, in part, on whether a risk of a fall is above a threshold; and communicating a notification with the alert system, if the fall is emergent, that the fall is emergent so that the fall can be prevented. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A wearable for detecting emerging falls and an actual fall comprising:
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a power source; one or more sensors wearable by at least one user, at least one of the one or more sensors being configured to collect and transmit at least motion data; at least one processor for classifying at least the motion data and time data received from the one or more sensors according to a fall prediction model, such that data classification includes at least whether a fall is emerging or not; one or more hubs configured to; receive raw sensor data and the classified data as well as information about the data classification, process one or more parameters including time duration and time placement for one or more strings, where the one or more strings comprise one or more streams of sensor data including N-dimensional event phase space information which can be matched to behavioral motion, such that the one or more strings are classified into one or more classes and the one or more classes are used to determine individual and group thresholds used by an alert system; and transmit the information about the classification to the alert system; a communications system capable of sending data and commands, as well as information contributing to sending alerts between a remote server, a local server and the one or more hubs; and the alert system for sending a notification when an emergent fall has been identified based in part on whether a risk of a fall is above a threshold based on the data classification by the fall prediction model. - View Dependent Claims (15, 16, 17, 18, 19)
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Specification