LSTM-RNNs combined with scene information for human activity recognition

Chen, Wenhui; Baca, Carlos Andres Betancourt; Tou, Chih-Hao

doi:10.1109/healthcom.2017.8210846

Cited by 25 publications

(16 citation statements)

References 7 publications

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“…Although a lot of work has been done for basic human activity detection (walking [2][3][4][5], [8], running [10][11][12]18], sitting still [14][15][16]20] etc. [22,23,[26][27][28]); to the best of our knowledge none of these activities mentioned earlier for questionable observer detection have been detected.…”

Section: Introductionmentioning

confidence: 60%

“…Cheng et al used a dataset where five motion sensors were placed at five different parts of the body [20]. Accelerometers and gyroscopes data were used and processed for human activity recognition in different researches [23,[26][27][28]. All these works have some common problems: the users have to wear gadgets on the body which is highly impractical in real world [4]; some of them are not real time detection [4]; in the cases where the data was collected from smart phones of the users, each users wear their smart phones quite differently (some hold them in one hand, others put them in their pockets, or in their bags etc.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Anisuzzaman¹,

Saif²

2018

IJIGSP

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This research presents a framework to detect a questionable observer depending on a specific activity named "frequent iris movement". We have focused on some activities and behaviors upon which we can classify one as questionable. So this research area is not only an important part of computer vision and artificial intelligence, but also a major part of human activity recognition (HAR). We have used Haar Cascade Classifier to detect irises of both left and right eyes. Then running some morphological operation we have detected the midpoint between left and right irises; and based on some characteristics of midpoint movement we have detected a specific activityfrequent iris movement. Depending on this activity we are declaring someone as questionable observer. To validate this research we have created our own dataset with 86 videos, where 15 individuals have volunteered. We have achieved an accuracy of 90% for the first 100 frames or 3.33 seconds of each of our videos and an accuracy of 93% for the first 150 frames or 5.00 seconds of each of our videos. No work has been done yet on basis of this specific activity to detect someone as questionable and furthermore our work outperforms most of the existing work on questionable observe detection and suspicious activity recognition.

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Section: Introductionmentioning

confidence: 60%

Section: Related Workmentioning

confidence: 99%

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Anisuzzaman¹,

Saif²

2018

IJIGSP

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“…Guan proposes an ensemble of deep long-term short-term memory (LSTM) networks for behavior recognition [27]. In [28], Chen et al use LSTM recurrent neural networks to analyze sensor readings from accelerometers and gyroscopes to identify human activity and provide position-aware methods to improve recognition accuracy.…”

Section: Related Workmentioning

confidence: 99%

Activity Feature Solving Based on TF‐IDF for Activity Recognition in Smart Homes

Guo

Xiong

et al. 2019

Complexity

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Smart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents' lives with minimal cost, daily activity recognition aims to know resident's daily activity in non-invasive manner. The performance of daily activity recognition heavily depends on solving strategy of activity feature. However, the current common employed solving strategy based on statistical information of individual activity does not support well the activity recognition. To improve the common employed solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. The proposed strategy exploits statistical information related to both individual activity and the whole of activities. Two distinct datasets have been commissioned, to mitigate against any possible effect of coupling between dataset and sensor configuration. Finally, a number of machine learning (ML) techniques and deep learning technique have been evaluated to assess their performance for residents activity recognition.

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“…Ha S [6] presented CNNs(CNN-pf and CNN-pff)for multi-sensor data which learned from multi-sensor data and were eventually aggregated in upper layers. Chen et.al [7] analyzed sensor readings from accelerometers and gyroscopes using long short-term memory to improve the recognition accuracy. Zhao Y [8] proposed a deep network architecture using residual bidirectional long short-term memory(LSTM)for human activity.…”

Section: Related Workmentioning

confidence: 99%

“…Zhao Y [8] proposed a deep network architecture using residual bidirectional long short-term memory(LSTM)for human activity. Such neural networks as LSTM-RNNs [9], ConvLSTM [10], RNN [11], CNN-LSTM [12], CNN [13] [14], AutoEncoder [15] are also applicable to human activity recognition.…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition on Cellphone Using Compositional Bidir-LSTM-CNN Networks

Wang¹,

Long²,

PiRahc³

et al. 2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

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Recently, the multimoal and high dimensional sensor data are prone to problems such as artificial error and time-consuming acquisition processes, especially in supervised human activity recognition. Therefore, this study proposes an activity recognition framework called compositional Bidir-LSTM-CNN Networks, which automatically extracts features from raw data using the optimized Convolutional Neural Network and further capture dynamic temporal features through the Bidirectional Lone Short Term Memory Network. Finally, this study paves the way for accurate recognition of human activities using the proposed framework with significantly improve 8% recognition accuracy along with additional features such as robustness and generalization.

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LSTM-RNNs combined with scene information for human activity recognition

Cited by 25 publications

References 7 publications

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Activity Feature Solving Based on TF‐IDF for Activity Recognition in Smart Homes

Human Action Recognition on Cellphone Using Compositional Bidir-LSTM-CNN Networks

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