Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer

2015 ◽  
Vol 75 (12) ◽  
pp. 7257-7270 ◽  
Author(s):  
Andrey D. Ignatov ◽  
Vadim V. Strijov
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Deep Dilation on Multimodality Time Series for Human Activity Recognition

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 53381-53396 ◽  
Author(s):  
Rui Xi ◽  
Ming Li ◽  
Mengshu Hou ◽  
Mingsheng Fu ◽  
Hong Qu ◽  
...  
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

Imaging and fusing time series for wearable sensor-based human activity recognition

2020 ◽  
Vol 53 ◽  
pp. 80-87 ◽  
Author(s):  
Zhen Qin ◽  
Yibo Zhang ◽  
Shuyu Meng ◽  
Zhiguang Qin ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Wearable Sensor

scholarly journals Performance Boosting of Scale and Rotation Invariant Human Activity Recognition (HAR) with LSTM Networks Using Low Dimensional 3D Posture Data in Egocentric Coordinates

2020 ◽  
Vol 10 (23) ◽  
pp. 8474
Author(s):  
Ibrahim Furkan Ince
Keyword(s):  
Time Series ◽  
Coordinate System ◽  
Dimension Reduction ◽  
Activity Recognition ◽  
Human Activity ◽  
Feature Space ◽  
Human Activity Recognition ◽  
Time Series Classification ◽  
Rotation Invariant ◽  
Low Dimensional

Human activity recognition (HAR) has been an active area in computer vision with a broad range of applications, such as education, security surveillance, and healthcare. HAR is a general time series classification problem. LSTMs are widely used for time series classification tasks. However, they work well with high-dimensional feature vectors, which reduce the processing speed of LSTM in real-time applications. Therefore, dimension reduction is required to create low-dimensional feature space. As it is experimented in previous study, LSTM with dimension reduction yielded the worst performance among other classifiers, which are not deep learning methods. Therefore, in this paper, a novel scale and rotation invariant human activity recognition system, which can also work in low dimensional feature space is presented. For this purpose, Kinect depth sensor is employed to obtain skeleton joints. Since angles are used, proposed system is already scale invariant. In order to provide rotation invariance, body relative direction in egocentric coordinates is calculated. The 3D vector between right hip and left hip is used to get the horizontal axis and its cross product with the vertical axis of global coordinate system assumed to be the depth axis of the proposed local coordinate system. Instead of using 3D joint angles, 8 number of limbs and their corresponding 3D angles with X, Y, and Z axes of the proposed coordinate system are compressed with several dimension reduction methods such as averaging filter, Haar wavelet transform (HWT), and discrete cosine transform (DCT) and employed as the feature vector. Finally, extracted features are trained and tested with LSTM (long short-term memory) network, which is an artificial recurrent neural network (RNN) architecture. Experimental and benchmarking results indicate that proposed framework boosts the performance of LSTM by approximately 30% accuracy in low-dimensional feature space.

Assessment of Deep Learning Models for Human Activity Recognition on Multi-variate Time Series Data and Non-targeted Adversarial Attack

2021 ◽  
pp. 129-159
Author(s):  
Mahbuba Tasmin ◽  
Sharif Uddin Ruman ◽  
Taoseef Ishtiak ◽  
Arif-ur-Rahman Chowdhury Suhan ◽  
Redwan Hasif ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Time Series Data ◽  
Human Activity Recognition ◽  
Series Data ◽  
Learning Models ◽  
Adversarial Attack

scholarly journals Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 319 ◽  
Author(s):  
Mario Munoz-Organero ◽  
Ramona Ruiz-Blazquez
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Generative Model ◽  
Acceleration Time
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