Moses

JavaScript library for mouse/touch gesture recognition.

It's a JS version of https://github.com/ifrost/moses-flash.

Demo

http://ifrost.github.io/moses/html/demo/

How it works?

A Sampler is responsible for capturing user events and dispatching sampling events with a list of sampled points.

A Recogniser listens to sampling events. When a gesture is sampled recogniser processes the data. It checks all registered Patterns and runs the pattern's Algorithm passing sampled data. Each Pattern contains data that is used by algorithm to compare with the sampled points. Algorithm returns a Match object with 0-1 value showing how well recognition of the pattern went.

Samplers (js/sampler): *DistanceSampler based on the distance between points *TimeSampler based on time delay

Recognisers (js/reconigser):

• DefaultRecogniser

Algorithms (js/algorithm):

• DefaultMosesAlgorithm based on simplified directions (similar to http://www.bytearray.org/?p=91)
• ShiftedPointsMosesAlgorithm variaton of DefaultMosesAlgorithm that shifts the points before recognition so closed patterns can be recognises from any starting point
• ReversedMosesAlgorithm variation of DefaultMosesAlgorithm that additionally compares the pattern with data points in reversed order
• PolygonalLineAlgorithm recognising polygonal lines
• StraightLineAlgorithm recognising if the gesture was a simple, straight line
• PatternCollectionAlgorithm algorithm used by PatternCollection

Some predefined patterns are available: moses.model.MosesPatterns.create().

Example

// create collection of predefined patterns
var mosesPatterns = moses.model.MosesPatterns.create();
 
// choose patterns from the collection
var patterns = [mosesPatterns.V, mosesPatterns.CIRCLE, mosesPatterns.DASH, mosesPatterns.SQUARE, mosesPatterns.SEVEN, mosesPatterns.Z];
 
// create a sampler
var div = document.getElementById("sampler");
var sampler = moses.sampler.DistanceSampler.create(div, 5);

// create a recogniser
var recogniser = moses.recogniser.DefaultRecogniser.create();

// register selecte patterns
patterns.forEach(function(pattern) {
   recogniser.register(pattern);
});

// display the result
recogniser.on('recognised', function(data) {
   var text;
   console.log('Best match:', data.bestMatch);
   if (data.bestMatch.recognised) {
       text = 'Recognised: ' + data.bestMatch.pattern.name + ' (' + Math.floor(data.bestMatch.value * 100) + '%)';
       console.log(text);
   }
   else {
       text = 'Not recognised. Was it ' + data.bestMatch.pattern.name + '? (' + Math.floor(data.bestMatch.value * 100) + '%)';
       console.log(text);
   }
});

// assign sampler to the recogniser
recogniser.sampler = sampler;

// activate the sampler
sampler.activate();