Systems and methods for remote clinical exams and automated labeling of signal data

A computer-implemented method, comprising:

• receiving sensor data from a wearable sensor system during a user activity in a free-living environment;
• determining, based on the sensor data, that the user activity corresponds with a clinical exam activity;
• and
• generating, using a machine learning algorithm and by the wearable sensor system, an annotation indicative of a predicted clinical exam score of the clinical exam activity, wherein prior to generating the annotation, the machine learning algorithm is trained using clinical exam data and clinical exam annotations indicating a performance of the clinical exam activity by a subject.

A computer-implemented method, comprising:

• receiving, at a first time and from a wearable sensor system, first sensor data indicative of a clinical activity;
• receiving first annotation data associated with the first sensor data;
• training a first machine learning algorithm using the first sensor data and the first annotation data;
• receiving, at a second time different from the first time and from the wearable sensor system, second sensor data indicative of a user performing an activity outside of a clinical environment;
• generating, by the wearable sensor system and using the first machine learning algorithm, second annotation data associated with the second sensor data;
• training a second machine learning algorithm using the second annotation data and the second sensor data;
• and
• generating, by the wearable sensor system and using a second machine learning algorithm trained using the second annotation data and the second sensor data, third annotation data associated with an activity other than the clinical activity.

A computer-implemented method, comprising:

• receiving, at an input device of a wearable sensor system, a first user input identifying a beginning of a first time period in which a virtual motor exam is conducted;
• receiving, at the input device of the wearable sensor system, a second user input identifying an end of the first time period;
• accessing, by the wearable sensor system and based on the virtual motor exam, first signal data output by a first sensor of the wearable sensor system during the first time period;
• receiving a first annotation from a clinical provider associated with the first signal data;
• receiving, from the wearable sensor system, second signal data output by the first sensor of the wearable sensor system during a second time period;
• and
• generating, using the wearable sensor system and based on the first signal data, the first annotation, and the second signal data, a second annotation associated with the second signal data indicative of a user performance.

A computer-implemented method, comprising:

• receiving, at a first time during a motor exam and from a wearable sensor system, first sensor data indicative of a motor exam activity;
• receiving a first annotation associated with the first sensor data;
• receiving, at a second time during a virtual motor exam and using the wearable sensor system, second sensor data;
• receiving a second annotation associated with the second sensor data;
• receiving, at a third time different from the first time and the second time, third sensor data indicative of user activity over an extended period of time;
• determining an activity window of the third sensor data that corresponds to the motor exam activity or the virtual motor exam by comparing the first sensor data and the second sensor data to a portion of the third sensor data;
• and
• generating, by the wearable sensor system using a machine learning algorithm trained using the first sensor data, first annotation, second sensor data, and the second annotation, a third annotation associated with the activity window and describing a user performance during the activity window.