Abstract
Wireless signals–based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions’ CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.
Highlights
Vision-based human activity analysis attempts to understand the movements of the human body using computer vision and machine learning techniques
Many studies have been done in recent years [1,2,3,4,5], robust action recognition is still a challenging problem due to the following issues: (a) Body parts or big size obstacles may cause partial occlusions; (b) An action, observed from different viewpoints, has different appearances; (c) Clothing, especially long skirts, may lead to apparent anthropometric differences; (d) The start-time and end-time points of an action are sometimes hard to detect accurately; (e) Dynamic backgrounds may make it difficult to locate and observe actions; (f) Smoke-filled, dim, or dark rooms may make it hard to observe actions; (g) People may feel uncomfortable with a camera overhead, especially in a bathroom
Studies [10,11] have proved that the existence and movement of humans will affect the channel state information (CSI) of wireless signals, and CSI has an advantage over light, infrared, or thermal energy when attempting to infer people’s movements
Summary
Vision-based human activity analysis attempts to understand the movements of the human body using computer vision and machine learning techniques. Many studies have been done in recent years [1,2,3,4,5], robust action recognition is still a challenging problem due to the following issues: (a) Body parts or big size obstacles may cause partial occlusions; (b) An action, observed from different viewpoints, has different appearances; (c) Clothing, especially long skirts, may lead to apparent anthropometric differences; (d) The start-time and end-time points of an action are sometimes hard to detect accurately;. We try to make a further step in solving the above vision-based issues for robust indoor, full-body action recognition by exploring the properties of CSI of Wi-Fi Wireless multi-input multi-output (MIMO) radios. (1) A framework for recognizing indoor human actions is proposed based on the recognition of the combination of primitive actions. (3) By Kernel SVM based multi-classification with a feature selection method, many activities from the combination of primitive actions can be recognized efficiently insensitive to the location, orientation, speed, and anthropometric differences
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