Method of evolving classifier programs for signal processing and control

A method of evolving programs for processing signal from a domain comprising the steps of:

• selecting and actuating sensors appropriate for the domain;
• sensing the domain environment using the selected sensors to produce raw sensor data;
• acquiring and recording the raw sensor data;
• calculate single event data consisting of the raw signal data between the start and stop of a signal event;
• calculate a single event feature set that includes all features calculated for each sensor;
• add the single event feature set to the training database;
• add the classification for the single event feature set to the training database, which consists of records, each of which contains one event classification and the features associated with that event;
• calculate confidence by evaluating a classifier against the testing database and establishing its accuracy;
• resolve any unrecognized and misclassified signal event data to determine the proper classification;
• add the now-recognized signal event data to the training database for future refinement of the classifiers;
• randomly and evenly divide the training database into separate training and testing sets, such that
• 1. each set contains events of all desired signal classifications
• 2. each set contains adequate numbers of events for each desired signal classification
• 3. the two sets are statistically equivalent and interchangeable;
• select values for items required for using genetic programming including initial fitness functions, population parameters, random seeds, genetic operators and termination criteria;
• run the genetic program until it finishes according to the termination criteria, then, record data, results and statistics about each run separately;
• determine if genetic program converged to acceptable solutions;
• and
• convert signal classifiers from genetic program form into one appropriate for evaluation/execution by the embedded domain classifier program.

A method of evolving programs to process at least one input signal comprising the steps of:

• generating a database of signals from said input signal channel;
• converting the signals in said input signal database to a form encountered in field use;
• calculating features from each signal in the converted signal database to characterizing the classification of said signals;
• producing at least one candidate classifying program responsive to said calculated features of said signals;
• producing a fitness function calculation program that calculates a value proportional to the signal classification success of candidate classifying program;
• and
• evolving the classification program using the fitness function to optomize the signal classifying program.

A method of evolving programs for processing a plurality of signal channels sensing the electric signals from a user's residual limb to control the user's prosthesis comprising the steps of:

• collecting signal data from a plurality of sensors on the user's residual limb while the user commands each desired motion a plurality of times to form a database of known motion commands associated with the electrical signals from the user's residual limb resulting from the motion command;
• reducing the plurality of electric signals in a time interval close to and including the motion command to a feature set;
• performing a separability analysis to choose a subset of signal channels and signal features that yields the best discrimination of the motion commands that the user generates;
• evolving a motion discriminating program using the subset of signal channels and features predicted to offer the best discrimination and a fitness function.