| Summary: | Human activity recognition (HAR) is the process of predicting different human
activity states (walking, running, walking, etc.) based on sensor data. HAR is mainly used
in healthcare for geriatric activity monitoring and human machine interaction. Recurrence
plot (RP) is visualization of a non-linear time series in a matrix form where the matrix
elements correspond to the recurrence of a dynamical state. Recurrent Quantification
Analysis (RQA) is a popular method to extract features from recurrence plot that can be used
to represent the underlying dynamics of the time series. RQA quantifies the number and
duration of recurrences of a dynamical system presented by its phase
space trajectory. Human activity monitoring generates continuous sensor data and RQA
computation is computationally expensive for long time series. To address this issue, in
this work we investigate the effectiveness of the image classification method to classify a
given recurrence plot. We use real-world human activity data to demonstrate the proposed approach. The
activities from human subjects are measured by accelerometers and gyroscopes positioned at
7 different body locations (head, forearm, upper arm, chest, thigh, shin, and waist). For
the classification task, we select running, walking, climbing up, and climb-ing down. The
sensor signals from the accelerometer and gyroscope are captured for the aforementioned
human activities. These sensor signals are converted into recurrence plots. The threshold
value is set as 0.5 and 0.8 for generating the recurrence plots. The size of the recurrence
plot is set to 32x32 pixels. Then a Convolutional Neural Network (CNN) is used to classify
the human activities using the recurrence plots. A Le-Net architecture is selected for the
deep learning process. Recurrence plots of different human activities are fed into the
LeNet5 algorithm in the form of epochs, with a batch size of 256 plots. We found that among all the sensor positions, the sensors fixed on shin thigh and
waist perform best and when the threshold is set as 0.8, the recognition will have a higher
ac-curacy. We also found that the signals from accelerometers perform better than those
collected by gyroscopes for the recognition task. The results indicate that the method of recurrence plot in combination with CNN
performs well in recognizing human activity with high accuracy and high efficiency. The
proposed method can be used for classification of non-linear time series for applications
other than HAR, providing an alternative to features generated using RQA.--Author's abstract
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