#Signal processing engineer series#

The above figure shows a proposed architecture for using LSTM based Deep RNNs for HAR. This Paper: Deep Recurrent Neural Networks for Human Activity Recognition outlines some LSTM based Deep RNN’s to build HAR models for classifying activities mapped from variable length input sequences. Recurrent Neural Networks are a suitable choice for signal data as it inherently has a time component, thereby a sequential component. Sensor data is growing at a rapid pace (eg: Apple Watch, Fitbit, pedestrian tracking etc) and the amount of data generated is sufficient for deep learning methods to learn and generate more accurate results. Deep Learning methods that are able to automatically extract features, scale better for more complex tasks. Most research has shifted to a sensor based approach due to advancement in sensor technology and its low cost.ĭeep Learning techniques have been used to overcome the shortcomings of machine learning techniques that follow heuristics formed by the user. Sensor based HAR (wearables that are attached to a human body and human activity is translated into specific sensor signal patterns that can be segmented and identified).It has real world applications in industries ranging from healthcare, fitness, gaming, military and navigation. Human Activity Recognition (HAR) has been gaining traction in recent years with the advent of advancing human computer interactions. Long Short-Term Memory Models (LSTM’s) for Human Activity Recognition (HAR) Two famous examples of this are Fast Fourier Transform (FFT) and the Discrete Fourier Transform (DFT). A signal is transformed between time and frequency domains using mathematical operators called a “Transform”.

Time Domain Analysis shows a signal’s energy distributed over time while a frequency domain representation includes information on the phase shift that must be applied to each frequency component in order to recover the original time signal with a combination of all the individual frequency components. It is used in areas such as Communications, Geology, Remote Sensing, and Image Processing. According to MathWorks, Frequency Domain Analysis is one of the key aspects of Signal Processing.Visual inspection of time series, looking at change over time, inspecting peaks and troughs.

#Signal processing engineer series#

#3: Once the data has been visualized, it will be necessary to transform and extract features from the data such as peaks, change points and signal patterns.īefore the advent of machine learning or deep learning, classical models for time series analysis were used since signals have a time specific domain. For signal processing, visualizing is required in the time, frequency and time-frequency domains for proper exploration. #2: Visualizing the data will be key to identifying the type of pre-processing and feature extraction techniques that will be required. If you are planning to use a supervised learning algorithm, the data will require labeling. This will also involve extracting and transforming features and splitting into training and test sets.

#1: Firstly, the process would involve storing, reading and pre-processing the data. Mathematics is necessary to evaluate it, Informatics enables the implementation and the physical world will generate the signals. Signals include almost all forms of data that can be digitized such as images, videos, audio and sensor data. According to the above diagram, signal processing is the intersection of Mathematics, Informatics and Physical stimuli.

And now, signal processing is starting to make some waves in deep learning.Ī signal is a physical support of information. Speech and audio, autonomous driving, image processing, wearable technology, and communication systems all work thanks to signal processing. Signal Processing is a branch of electrical engineering that models and analyzes data representations of physical events. Computers, radios, videos, mobile phones are all enabled by signal processing. Using Deep Learning for Signal ProcessingĪccording to the Institute of Electrical and Electronic Engineers (IEEE), Signal Processing encapsulates our daily lives without any of us even knowing.