Automatic recognition, learning, monitoring, and management of human physical activities
In one embodiment, a method includes one or more processors that collect motion and physiological sensor data of a user from one or more sensors worn by the user, the sensor data comprising one or more sensor data vectors, generate an activity signature based on the sensor data, determine whether a signature match exists between the activity signature and a known activity signature associated with an activity type from a set of known activity signatures, and if the signature match exists, recognize a known activity type, otherwise if the signature match does not exist, generate an unknown activity type based on the one or more sensor data vectors.
1. A processor implemented method of performing motion and physiology sensing and analysis, the method being implemented by one or more processors and one or more sensors to perform the following operations:
sense motion and physiological sensor data of a user from the one of more sensors worn by the user;
collect the motion and physiological sensor data of the user from the one or more sensors worn by the user, the sensor data including one or more sensor data vectors;
generate an activity signature based on the sensor data;
determine whether a signature match exists between the activity signature and a known activity signature associated with an activity type from a set of known activity signatures, such that:
if the signature match exists, recognize a known activity type; and
if the signature match does not exist, generate an unknown activity type based on the one or more sensor data vectors;
compare the known or unknown activity type with a contemporaneously scheduled activity in an activity calendar of the user; and
mark the contemporaneously scheduled activity as completed.
2. The method of claim 1 , wherein determine whether a signature match exists comprises performing a vector quantization process on the activity signature.
3. The method of claim 1 , further comprising classify the unknown activity type for future matching.
4. The method of claim 1 , wherein generate an unknown activity type comprises machine learning techniques.
5. The method of claim 1 , further comprising provide user feedback to the user based on the known activity type or the unknown activity type.
6. The method of claim 1 , further comprising provide user feedback to the user based on the known activity type or the unknown activity type in a context-aware manner.
7. The method of claim 6 , wherein the context-aware manner comprises a social network, sports network, fitness network, or health network.
8. The method of claim 1 , further comprising provide user feedback to a second user based on the completed contemporaneously scheduled activity.
9. A system configured to perform motion and physiology sensing and analysis, the system comprising:
one or more sensors worn by a user configured to sense motion and physiological sensor data of the user; and
one or more processors operable to:
collect the motion and physiological sensor data of the user from the one or more sensors worn by the user, the sensor data including one or more sensor data vectors;
generate an activity signature based on the sensor data;
determine whether a signature match exists between the activity signature and a known activity signature associated with an activity type from a set of known activity signatures, such that:
if the signature match exists, recognize a known activity type; and
if the signature match does not exist, generate an unknown activity type based on the one or more sensor data vectors;
compare the known or unknown activity type with a contemporaneously scheduled activity in an activity calendar of the user; and
mark the contemporaneously scheduled activity as completed.
10. The system of claim 9 , further comprising classify the unknown activity type for future matching.
11. The system of claim 9 , further comprising provide user feedback to the user based on the known activity type or the unknown activity type.
12. The system of claim 9 , further comprising provide user feedback to the user based on the known activity type or the unknown activity type in a context-aware manner.
13. The system of claim 9 , further comprising provide user feedback to a second user based on the completed contemporaneously scheduled activity.
14. One or more computer-readable non-transitory storage media embodying software that is operable for use with one or more sensors worn by a user configured to sense motion and physiological sensor data of the user to perform motion and physiology sensing and analysis by executing the following operations:
collect motion and physiological sensor data of the user from the one or more sensors worn by the user, the sensor data including one or more sensor data vectors; generate an activity signature based on the sensor data;
determine whether a signature match exists between the activity signature and a known activity signature associated with an activity type from a set of known activity signatures, such that:
if the signature match exists, recognize a known activity type; and
if the signature match does not exist, generate an unknown activity type based on the one or more sensor data vectors;
compare the known or unknown activity type with a contemporaneously scheduled activity in an activity calendar of the user; and
mark the contemporaneously scheduled activity as completed.
15. The media of claim 14 , further comprising classify the unknown activity type for future matching.
16. The media of claim 14 , further comprising provide user feedback to the user based on the known activity type or the unknown activity type.
17. The media of claim 14 , further comprising provide user feedback to the user based on the known activity type or the unknown activity type in a context-aware manner.