Human Activity Recognition in AAL Environments Using Random Projections

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

doi:10.1155/2016/4073584

Cited by 77 publications

(44 citation statements)

References 53 publications

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“…A confusion matrix was adopted, which is a method commonly used to identify error types (false positives and negatives) [40]. Several different performance metrics—accuracy (the standard metric to express classification performance), precision, recall and F-measure—could be calculated based on the matrix [9].…”

Section: Discussionmentioning

confidence: 99%

Activity Recognition and Semantic Description for Indoor Mobile Localization

Guo

Xiong

Zheng

et al. 2017

Sensors

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As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization. The location acquisition method combines pedestrian dead reckoning (PDR), human activity recognition (HAR) and landmarks to acquire accurate indoor localization information. Considering the problem of initial position determination, a hidden Markov model (HMM) is utilized to infer the user’s initial position. To provide an improved service for further applications, the landmarks are further assigned semantic descriptions by detecting the user’s activities. The experiments conducted in this study confirm that a high degree of accuracy for a user’s indoor location can be obtained. Furthermore, the semantic information of a user’s trajectories can be extracted, which is extremely useful for further research into indoor location applications.

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Section: Discussionmentioning

confidence: 99%

Activity Recognition and Semantic Description for Indoor Mobile Localization

Guo

Xiong

Zheng

et al. 2017

Sensors

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“…The most common are mean, variance, skewness, kurtosis, zero/threshold crossing rate, or frequency-domain features like energy, frequency bands, etc. [28][29][30]. Since this stage often drastically reduces the data rate, as a relatively small number of features is calculated from a large number of samples of a window, it was subject to a lot of research to perform this stage as near to the sensor as possible, i.e., on board of a wireless sensor node [14][15][16]18,19], or even on-sensor [17,21].…”

Section: Data Acquisitionmentioning

confidence: 99%

Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

et al. 2018

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The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

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“…Considering inertial data, many different features for human action recognition have been proposed, with the aim to reduce the complexity of the features extraction process and to enhance the separation among the classes [25]. Wearable inertial sensors are quite cheap and generate a limited amount of data that can be processed easily with respect to video data, even if they do not provide information about the context.…”

Section: Related Work On Not Vision-based Devicesmentioning

confidence: 99%

Human Action Recognition with RGB-D Sensors

Cippitelli¹,

Gambi²,

Spinsante³

2017

Motion Tracking and Gesture Recognition

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Human action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset.

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Human Activity Recognition in AAL Environments Using Random Projections

Cited by 77 publications

References 53 publications

Activity Recognition and Semantic Description for Indoor Mobile Localization

Activity Recognition and Semantic Description for Indoor Mobile Localization

Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

Human Action Recognition with RGB-D Sensors

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