A human activity recognition method based on sparse representation and Softmax classification

A technology of human activity and recognition method, applied in the field of medical human activity detection and recognition, can solve problems such as large time complexity, large noise signal, large time delay, etc., and achieve the effects of improving classification performance, improving complexity and improving efficiency

Inactive Publication Date: 2018-12-25
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] 1. The acquisition of activity signals is affected by external factors, and there are a large number of noise signals, which have a very important impact on the accuracy of subsequent algorithm processing
[0005] 2. The

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  • A human activity recognition method based on sparse representation and Softmax classification
  • A human activity recognition method based on sparse representation and Softmax classification
  • A human activity recognition method based on sparse representation and Softmax classification

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[0053]The technical solutions provided by the present invention will be further described below with reference to the accompanying drawings.

[0054] Sparse representation based on high-dimensional data In computer vision and machine learning, the best classification systems often choose sparse representation as their key module. Methods such as linear projection and random forest based on sparse representation can pass the natural image itself as a sparse signal, and its optimization model is established from the perspective of signal reconstruction to obtain a good approximation to the original signal. Softmax regression learning obtains the estimation function of rank classification by learning the feature vector, and uses its maximum probability to classify the signature feature. To this end, the present invention provides a human activity recognition method based on sparse representation and Softmax classification.

[0055] see Figure 1-5 , the present invention provid...

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Abstract

The invention discloses a human activity identification method based on sparse representation and Softmax classification. The method comprises: Step S1: a Softmax model being trained by using a largeamount of wireless sensor activity signal data set. Step S2: classifying and identifying the real-time detected activity signals using the trained Softmax model. By adopting the technical proposal ofthe invention, the massive sensor activity signal data set in the database is trained, the belonging class of the signal is determined, and then the activity signal is processed by combining the sparse representation algorithm, thereby greatly reducing the calculation amount and the complexity of the identification of the human activity signal, and simultaneously effectively improving the accuracyof the human activity analysis.

Description

technical field [0001] The invention relates to the field of medical human body activity detection and recognition, in particular to a human body activity recognition method based on sparse representation and Softmax classification. Background technique [0002] In the past few decades, the advancement of modern computer technology has made human activity recognition a popular research field, and the analysis of human activity recognition has great research significance in the fields of medicine, security, and human-computer interaction. Most current activity recognition algorithms are based on activity recognition under wireless sensor networks. Wireless sensors collect activity signals, then transmit and process the collected activity signals, and then use signal processing algorithms for identification. However, due to the large amount of noise in the wireless sensor signal and the huge amount of storage and calculation of the signal, there are certain challenges to the r...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/23G06F18/21G06F18/24
Inventor 袁友伟姚瑶鄢腊梅俞东进李万清
Owner HANGZHOU DIANZI UNIV
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