System, Method and Apparatus for Forming Machine Learning Sessions

A method to automatically label toileting acts of a user, the method comprising:

• activating a sensor to record the toileting acts in a plurality of frames, each frame capturing one or more activities during a discrete time interval and each frame separated from a subsequent frame by a time interval;
• forming one or more sequences by grouping the frames separated from each other by a substantially constant time interval;
• and
• forming one or more sessions by grouping the sequences as a function of a differential interval;
• labeling each frame by identifying one or more conditions present in the frame;
• labeling each sequence by identifying one or more conditions present in the plurality of frames in the respective sequence;
• and
• labeling each session by identifying one or more conditions present in the plurality of sequences in the respective session.

A system to automatically label toileting acts of a user, the system comprising:

• a processor circuitry;
• a memory circuitry in communication with the processor circuity, the memory circuitry configured with instructions to cause the processor circuitry to automatically label toileting acts of the user by:
• activate a sensor to record the toileting acts in a plurality of frames, each frame capturing one or more activities during a discrete time interval and each frame separated from a subsequent frame by a time interval;
• form one or more sequences by grouping the frames separated from each other by a substantially constant time interval;
• and
• form one or more sessions by grouping the sequences as a function of a differential interval;
• label each frame by identifying one or more conditions present in the frame;
• label each sequence by identifying one or more conditions present in the plurality of frames in the respective sequence;
• and
• label each session by identifying one or more conditions present in the plurality of sequences in the respective session.

A method to form machine learning (ML) sessions from a user's activities, the method comprising:

• receiving a plurality of frames from a sensor, each frame capturing one or more activities during a discrete time interval and each frame separated from a subsequent frame by a time interval;
• forming one or more sequences by grouping the frames separated from each other by a substantially constant time interval;
• and
• forming one or more sessions by grouping the sequences as a function of a differential interval that exceeds a threshold.

A system to form machine learning (ML) sessions from a user's activities, the system comprising:

• a processor circuitry;
• a memory circuitry in communication with the processor circuity, the memory circuitry configured with instructions to cause the processor circuitry to:
• receive a plurality of frames from a sensor, each frame capturing one or more activities during a discrete time interval and each frame separated from a subsequent frame by a substantially constant time interval;
• form one or more sequences by grouping the frames separated from each other by a differential interval;
• and
• form one or more sessions by grouping the sequences as a function of a differential interval that exceeds a threshold.

A non-transitory computer-readable medium comprising a processor circuitry and a memory circuitry in communication with the processor circuitry and including instructions to form machine learning (ML) sessions from a user's activities, the memory circuitry further comprising instructions to cause the processor to:

• receive a plurality of frames from a sensor, each frame capturing one or more activities during a discrete time interval and each frame separated from a subsequent frame by a substantially constant time interval;
• form one or more sequences by grouping the frames separated from each other by a differential interval;
• and
• form one or more sessions by grouping the sequences as a function of a differential interval that exceeds a threshold.

A system to improve classification accuracy of toileting acts of a user through human-in-the-loop training, the system comprising:

• a processor circuitry;
• and
• a memory circuitry in communication with the processor circuitry, the memory circuitry configured with instructions to cause the processor circuitry to:
• receive a plurality of frames from one or more sensors capturing the user's toileting activity;
• process the plurality of frames to generate sequences, sessions, and corresponding classification outputs indicating one or more conditions associated with the toileting acts;
• receive feedback or annotation data from one or more human reviewers identifying verification or correction of at least a portion of the classification outputs;
• update, based on the feedback or annotation data, one or more learning parameters of a machine learning model stored in the memory circuitry to improve subsequent classifications of toileting acts;
• monitor a classification performance metric comprising model confidence, precision, or recall across multiple sessions;
• and
• automatically adjust a classification threshold or a time-differential parameter for session formation responsive to changes in the classification performance metric.