GPARS: a general-purpose activity recognition system

Sarkar, Jehad; Vinh, La The; Lee, Young-Koo; Lee, Sungyoung

doi:10.1007/s10489-010-0217-4

Cited by 18 publications

(21 citation statements)

References 17 publications

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“…Due to the wide use of portable and wearable sensors with low cost, low power consumption, high capacity and miniaturization, HAR based on sensor data has become a research hotspot. The HAR system can be used in human-computer interaction application [11] , behavior monitoring [12][13] , health monitoring [14] , smart home [15] , medical care [16][17] and so on.Data collected from portable and wearable sensors are usually time series data. Human activity recognition for time series is a complex process, which usually involves the following steps.…”

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confidence: 99%

Human Activity Recognition Based on Motion Sensor Using U-Net

Zhang

et al. 2019

IEEE Access

102

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Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method. This method faces the multi-class window problem which mistakenly labels different classes of sampling points within a window as a class. In this paper, a HAR algorithm based on U-Net is proposed to perform activity labeling and prediction at each sampling point. The activity data of the triaxial accelerometer is mapped into an image with the single pixel column and multi-channel which is input into the U-Net network for training and recognition. Our proposal can complete the pixellevel gesture recognition function. The method does not need manual feature extraction and can effectively identify short-term behaviors in long-term activity sequences. We collected the Sanitation dataset and tested the proposed scheme with four open data sets. The experimental results show that compared with Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree(DT), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN) and Fully Convolutional Networks (FCN) methods, our proposal has the highest accuracy and F1-socre in each dataset, and has stable performance and high robustness. At the same time, after the U-Net has finished training, our proposal can achieve fast enough recognition speed. IntroductionHuman activity recognition (HAR) is the key technology of human-computer interaction and human activity analysis. The basic task of HAR is to select the appropriate sensor and deploy it to monitor and capture the user's activity [1] . HAR can be divided into two categories. The first one is video-based HAR, using video cameras to monitor the activity of the human body. Another is sensor-based HAR, which is based on time series data collected by sensors such as mobile phone built-in accelerometers [2][3][4] , wrist-worn accelerometers [5][6][7] , waist-mounted accelerometers [8][9] , gyroscopes and magnetometers [10] . Due to the wide use of portable and wearable sensors with low cost, low power consumption, high capacity and miniaturization, HAR based on sensor data has become a research hotspot. The HAR system can be used in human-computer interaction application [11] , behavior monitoring [12][13] , health monitoring [14] , smart home [15] , medical care [16][17] and so on.Data collected from portable and wearable sensors are usually time series data. Human activity recognition for time series is a complex process, which usually involves the following steps. First, preprocess the time series data such as smoothing, normalization [6] , and separating gravity component [18] from acceleration data. Then segment the

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confidence: 99%

Human Activity Recognition Based on Motion Sensor Using U-Net

Zhang

et al. 2019

IEEE Access

102

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“…The same set of activities is considered as in [3]. The is set to 5 to set the self-transition probabilities to 80%.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Hidden Markov Mined Activity Model for Human Activity Recognition

Sarkar

2014

International Journal of Distributed Sensor Networks

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Object-usage-based human activity recognition systems require activity data for learning. Acquiring such data from the real world is expensive and time consuming. To overcome such difficulties, the exploitation of web activity data is gaining popularity. However, due to a lack of much real-world information in such data, existing activity models are not suitable for web data. In this paper, we propose a hidden Markov model-(HMM-) based activity model specially designed to use web activity data for activity recognition. It utilizes a sequence of object-usage information for activity recognition. We also propose a web activity data mining algorithm for this model. It is extremely fast and efficient in comparison with the existing algorithms. We perform three experiments to validate the proposed model. We show that the model can be effectively utilized by an activity recognition system.

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“…For instance, [9]- [11] proposed solutions for healthier cooking. Works in [12], [13] offered intelligent homes. In this section, we briefly review works aiming at tackling two major issues usually raised when deploying feasible applications.…”

Section: Related Workmentioning

confidence: 99%

SensCapsNet: Deep Neural Network for Non-Obtrusive Sensing Based Human Activity Recognition

Pham¹,

Nguyen-Thai²,

Huy

et al. 2020

IEEE Access

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This research was funded by Vietnam Ministry of Science and Technology under grant number DTDLCN-16/18 ''Automated Respiration Symptoms monitoring and Abnormal Human Activity Detection Using the Internet of Things''. ABSTRACT Recently, the recent advancement of deep learning with the capacity to perform automatic highlevel feature extraction has achieved promising performance for sensor-based human activity recognition (HAR). Among different deep learning methods, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) have been widely adopted. However, scalar outputs and pooling in CNN only allow to get the invariance but not the equivariance. The capsule networks (CapsNet) with the vector output and routing by agreement is able to capture the equivariance. In this paper, we propose a method for recognizing human activity from wearable sensors based on a capsule network named SensCapsNet. The architecture of SensCapsNet is designed to be suitable for spatial-temporal data coming from wearable sensors. Experimental results show that the proposed network outperforms CNN and LSTM methods. The performance of the proposed CapsNet architecture is assessed by altering dynamic routing between capsule layers. The proposed SensCapsNet yields improved accuracy values of 77.7% and 70.5% for 1 routing on two testing datasets in comparison with the baseline methods based on CNN and LSTM that yields the F1score of 67.7% and 69.2% for the first dataset and 65.3% and 67.6% for the second dataset respectively. Moreover, even several human activity datasets are available, privacy invasion and obtrusive concerns have not been carefully taken in to consideration in dataset building. Toward to build a non-obstructive sensing based human activity recognition method, in this paper, a dataset named 19NonSens is designed and collected from twelve subjects wearing e-Shoes and a smart watch to perform 19 activities under multiple contexts. This dataset will be made publicity available. Finally, thanks to the promising results obtained by the proposed method, we develop a life logging application which achieves a real-time computation and the accuracy rate greater than 80% for 5 common upper body activities. INDEX TERMS Human activity recognition, capsule net, wearable sensors.

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GPARS: a general-purpose activity recognition system

Cited by 18 publications

References 17 publications

Human Activity Recognition Based on Motion Sensor Using U-Net

Human Activity Recognition Based on Motion Sensor Using U-Net

Hidden Markov Mined Activity Model for Human Activity Recognition

SensCapsNet: Deep Neural Network for Non-Obtrusive Sensing Based Human Activity Recognition

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