A system ( 100) for generating a hybrid learning technique for sensor signal analytics, comprising: a memory ( 102) storing instructions and one or more modules ( 108) a database ( 110) one or more communication or input/output interfaces ( 106) and one or more processors ( 104) coupled to the memory ( 102) via the one or more communication interfaces ( 106), wherein the one or more processors ( 104) are configured by the instructions to execute the one or more modules ( 108) comprising: an input module ( 202) for receiving sensor signals as an input, wherein the sensor signals are captured using a plurality of different sensors a pre-processing and noise cleaning module ( 204) for processing the received the sensor signals for noise removal a path selector ( 206) for choosing a plurality of learning techniques for the processed sensor signal based on a plurality of domain constraints a feature generation module ( 210), a feature recommendation module ( 216) and a classification module ( 218) for generating a plurality of hybrid learning techniques from the plurality of learning techniques a optimization module ( 224) for choosing a hybrid learning technique from the generated plurality of hybrid learning techniques based on the performance matrix generated individually for each of the plurality of hybrid learning techniques based on optimization techniques and an output module ( 226) for displaying the chosen hybrid learning technique signal analytics of the received sensor signals.ġ3. The method of claim 1, wherein the predicted hybrid learning technique and its corresponding unique feature representation is displayed on the output module ( 226) for signal analytics of the received sensor signals.ġ2. The method of claim 1, wherein the performance matrix is generated based on optimization techniques that include performing constraint optimization, wherein parameters are maximized based on a pre-defined threshold.ġ1. The method of claim 5, wherein the classification techniques include deep learning and machine learning techniques.ġ0. The method of claim 5, wherein the feature recommendation techniques are implemented based on statistical measures, ranking and classification techniques.ĩ. The method of claim 6, where the generated features generation techniques are fused to generate a unique feature representation that represents the hybrid learning technique to be displayed on an output module ( 226).Ĩ.
The method of claim 5, wherein features generation techniques are based on unsupervised learning techniques that include deep learning and signal processing techniques (feature space exploration) information theoretic, and statistical features along with features of pre-trained deep recurrent neural network.ħ. The method of claim 1, wherein the plurality of hybrid learning techniques are generated based on feature generation, feature recommendation and classification techniques.Ħ.
The method of claim 1, wherein the plurality of learning techniques are chosen using a rule based engine and domain constraints, wherein the domain constraints include business requirements and computational constraints.ĥ. The method of claim 1, wherein the plurality of sensors are from diverse application domains including exemplary domains physiological time series sensor signals that include Electrocardiography (ECG), electroencephalography (EEG), motion sensors that include accelerometer, gyro meter, magnetometer, temperature sensors.Ĥ. The method of claim 1, wherein hybrid learning techniques refers to learning techniques that are a hybrid combination a plurality of techniques that include of deep learning, machine learning and signal processing.ģ. A processor-implemented method for generating a hybrid learning technique for sensor signal analytics, the method comprising: receiving sensor signals as an input, wherein the sensor signals are captured using a plurality of different sensors processing the received the sensor signals for noise removal choosing a plurality of learning techniques for the processed sensor signal based on a plurality of domain constraints generating a plurality of hybrid learning techniques from the plurality of learning techniques generating a performance matrix individually for each of the plurality of hybrid learning techniques based on optimization techniques predicting the hybrid learning technique from the generated plurality of hybrid learning techniques based on the performance matrix and generating an unique feature representation for the predicted hybrid learning techniques.Ģ.
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