Probabilistic LMA-based classification of human behaviour understanding using Power Spectrum technique

Khoshhal, Kamrad; Aliakbarpour, Hadi; Quintas, João; Drews, Paulo; Dias, Jorge

doi:10.1109/icif.2010.5712107

Cited by 16 publications

(14 citation statements)

References 20 publications

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“…We believe that the frequency-based features are suitable features to achieve the LMA.Effort parameters and recognize human motion, as can be seen in our previous work [6] in detail (LMA.Effort parameters shall be introduced in the next section). Frequency-based features can be obtained by using Fast Fourier Transform (FFT) and Power Spectrum (PS) techniques on the input signals ( [13] and [5]).…”

Section: Frequency-based Feature Extractionmentioning

confidence: 95%

LMA-Based Human Behaviour Analysis Using HMM

Khoshhal

Aliakbarpour

Mekhnacha

et al. 2011

Technological Innovation for Sustainability

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In this paper a new body motion-based Human Behaviour Analysing (HBA) approach is proposed for the sake of events classification. Here, the interesting events are as normal and abnormal behaviours in a Automated Teller Machine (ATM) scenario. The concept of Laban Movement Analysis (LMA), which is a known human movement analysing system, is used in order to define and extract sufficient features. A two-phase probabilistic approach have been applied to model the system's state. Firstly, a Bayesian network is used to estimate LMA-based human movement parameters. Then the sequence of the obtained LMA parameters are used as the inputs of the second phase. As the second phase, the Hidden Markov Model (HMM), which is a well-known approach to deal with the time-sequential data, is used regarding the context of the ATM scenario. The achieved results prove the eligibility and efficiency of the proposed method for the surveillance applications.

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Section: Frequency-based Feature Extractionmentioning

confidence: 95%

LMA-Based Human Behaviour Analysis Using HMM

Khoshhal

Aliakbarpour

Mekhnacha

et al. 2011

Technological Innovation for Sustainability

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“…Of the estimated 1% of the elderly who fall and sustain a hip fracture, 20-30% dies within one year of the fracture. Almost two thirds of elderly population with hip fracture never regains their pre-fracture activity status and one-third needed admission to nursing home [2]. The cost forecasting of medical care of elderly people regarding falls injuries goes to $43.8 billion by 2020 [3]- [4].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Human movement detection using attitude and heading reference system

Fourlas

2014

Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

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Among different types of human movement, falls are the most important since they related with high social and economic costs. Falls can cause various unintentional injuries such as fractures or in the worst-case scenario even lead to death, elderly citizen. Wearable devices present a growing interest in health care applications since they can detect signals of human activity and continuously monitoring critical parameters, offering a reliable and inexpensive solution. In this paper, an attitude and heading reference system -inertial measurement unit (IMU) is used in order to detect human movement and especially different type of falls.

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“…Each component describes human movements by different aspects. Among the different components of LMA, here we have selected Effort component to observe human movements in terms of how motion of human body parts are happening with respect to inner intention (our recent work in [6]). Effort has four sub-components and each of them has two states; Effort.time (sudden/sustained), Effort.space (direct/indirect), Effort.weight (light/strong) and Effort.flow (bounded/free).…”

Section: Lma-based Human Movement Classificationmentioning

confidence: 99%

“…First four coefficients were collected from PS signal of each body part acceleration signals. Each coefficient has four state possibilities (see [6]) which are defined by several thresholds.…”

Section: Lma-based Human Movement Classificationmentioning

confidence: 99%

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HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

Aliakbarpour

Khoshhal

Quintas

et al. 2011

Technological Innovation for Sustainability

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Abstract. This paper proposes a HMM-based approach for detecting abnormal situations in some simulated ATM (Automated Teller Machine) scenarios, by using a network of heterogeneous sensors. The applied sensor network comprises of cameras and microphone arrays. The idea is to use such a sensor network in order to detect the normality or abnormality of the scenes in terms of whether a robbery is happening or not. The normal or abnormal event detection is performed in two stages. . The attained experimental results validate the applicability and effectiveness of the using heterogeneous sensor network to detect abnormal events in the security applications.

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Probabilistic LMA-based classification of human behaviour understanding using Power Spectrum technique

Cited by 16 publications

References 20 publications

LMA-Based Human Behaviour Analysis Using HMM

LMA-Based Human Behaviour Analysis Using HMM

Human movement detection using attitude and heading reference system

HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

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