Recognizing human motions through mixture modeling of inertial data

Field, Matthew; Stirling, David; Pan, Zengxi; Ros, Montserrat; Naghdy, Fazel

doi:10.1016/j.patcog.2015.03.004

Cited by 45 publications

(17 citation statements)

References 34 publications

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“…In general, traditional HAR systems considered as device-based approaches such as vision-based, body-worn sensors, and mobile-phone interior sensors [1][2][3]. However, all have certain drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Indoor Human Activity Recognition Method Using CSI of Wireless Signals

Al-qaness¹,

Al-Eryani²,

AL-Jallad³

2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Human activity recognition has been studied for decades by leveraging vision-based and sensor-based technologies. However, the drawback of such techniques such as short-range area, and invasion of human privacy in vision-based technology, and inappropriate usage for sensor devices or inconvenient feeling of the user to carry sensor devices. All of these reasons encouraged researchers to wireless-based sensing technology or called device-free because the user needn't carry devices nor monitoring with a camera. In this regard, we present a device-free activity recognition system by exploiting the Channel State Information (CSI) of Wi-Fi signals which recognizes three dynamic activities. We build our prototype with a high efficient feature extraction algorithm and an agile and accurate classification algorithm. To examine the feasibility and performance of the proposed system, hundreds of experiments have been implemented in LOS and NLOS scenarios in a dynamic environment with different volunteer users. The experiment results show that the proposed method has gained a high-accuracy rate in both LOS and NLOS.

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Section: Introductionmentioning

confidence: 99%

Indoor Human Activity Recognition Method Using CSI of Wireless Signals

Al-qaness¹,

Al-Eryani²,

AL-Jallad³

2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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show abstract

“…In general, traditional HAR systems have been considered as device-based approaches such as vision-based [1], body-worn sensors [2], and smartphone interior sensors [3]. However, both vision-based and sensor-based activity recognition systems have certain drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Device-Free Indoor Activity Recognition System

Al-qaness

et al. 2016

Applied Sciences

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Abstract:In this paper, we explore the properties of the Channel State Information (CSI) of WiFi signals and present a device-free indoor activity recognition system. Our proposed system uses only one ubiquitous router access point and a laptop as a detection point, while the user is free and neither needs to wear sensors nor carry devices. The proposed system recognizes six daily activities, such as walk, crawl, fall, stand, sit, and lie. We have built the prototype with an effective feature extraction method and a fast classification algorithm. The proposed system has been evaluated in a real and complex environment in both line-of-sight (LOS) and none-line-of-sight (NLOS) scenarios, and the results validate the performance of the proposed system.

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“…Regarding the classification of longer motional activities, various speeds and types of forward movements were also tested, such as slow, normal and rush walking [25], jogging [22,33,[42][43], and running [23-25, 33, 40]. Some works tried to differentiate different directions of an activity type, like level walking, walking downstairs and upstairs [23, 33-34, 38-39, 42] or walking backwards [43]. Yang et al [42] recorded even continuous rotational movements, such as walking left-circle or right-circle, and turning left or right.…”

Section: Activity Classesmentioning

confidence: 99%

“…Special complex activities were also parts of the constructed databases, e.g. falling [24,41], jumping [23,33,42], writing [22], brushing teeth [40,44], eating and drinking [44], sweeping the floor, lifting a box onto a table, bouncing a ball [43], driving [34], cycling [23,44], etc.…”

Section: Activity Classesmentioning

confidence: 99%

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New methods in the application of inertial and magnetic sensors in online pattern recognition problems

Šarčević

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Recognizing human motions through mixture modeling of inertial data

Cited by 45 publications

References 34 publications

Indoor Human Activity Recognition Method Using CSI of Wireless Signals

Indoor Human Activity Recognition Method Using CSI of Wireless Signals

Device-Free Indoor Activity Recognition System

New methods in the application of inertial and magnetic sensors in online pattern recognition problems

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