Automatic Arm Motion Recognition Based on Radar Micro-Doppler Signature Envelopes

Abstract

In considering human-machine interface (HMI) for smart environment, a simple but effective method is proposed for automatic arm motion recognition with a Doppler radar sensor. Arms, in lieu of hands, have stronger radar cross-section and can be recognized from relatively longer distances. An energy-based thresholding algorithm is applied to the spectrograms to extract the micro-Doppler (MD) signature envelopes. The positive and negative frequency envelopes are concatenated to form a feature vector. The nearest neighbor (NN) classifier with Manhattan distance (L1) is then used to recognize the arm motions. It is shown that this simple method yields classification accuracy above 97 percent for six classes of arm motions. Despite its simplicity, the proposed method is superior to those of handcrafted featurebased classifications and low-dimension representation techniques based on principal component analysis (PCA), and is comparable to convolutional neural network (CNN).

arXiv comment9 pages, 11 figures

References in corpus (1)

• RF Sensing for Continuous Monitoring of Human Activities for Home Consumer Applications

Cited by in corpus (3)

• Through-the-Wall Radar Human Activity Micro-Doppler Signature Representation Method Based on Joint Boulic-Sinusoidal Pendulum Model
• Range-Max Enhanced Ultra-Wideband Micro-Doppler Signatures of Behind Wall Indoor Human Activities
• Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures