Smart-Wearable Sensors and CNN-BiGRU Model: A Powerful Combination for Human Activity Recognition

Human Activity Recognition is a key component of ambient assisted living and one of the most active areas of research in the Internet of Things. The use of wearable and embedded sensors in human activity recognition overcomes the limitations of conventional approaches relying on machine vision and environmental sensors. We offer a novel, lightweight Convolutional Neural Network – Bidirectional Gated Recurrent Unit (CNN-BiGRU) model that classifies human activities using the inertial sensor data collected with body-mounted smartwatches and smartphones. Unlike the traditional approaches, the presented model is trained on the magnitude of the 3D acceleration (mag <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> ), which significantly minimizes the input space 1D. The deep learner has been validated using two different publicly available datasets from the Wireless Sensor Data Mining (WISDM) lab and different evaluation parameters such as Recall/Sensitivity, Precision, accuracy, and F1-scores are computed. A comparison with the existing studies reveals that our proposed learner surpasses the existing methodologies. Using magnitude of 3D acceleration (mag <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> , 1D input signal), we have achieved 97.29% accuracy for all 6 activities of WISDM 2011 dataset and 98.81% accuracy for 3D acceleration (a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ,a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ,a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ). The Precision, Recall and F1-score remained at 97% for the 1D case and 99% for the 3D case. When evaluated on the data of all 18 smartwatch-based activities in WISDM 2019 dataset, we have achieved 97.5% accuracy with the magnitude of 3D acceleration (mag <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ) and 98.4% accuracy for 6D acceleration and angular velocities (a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ,a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ,a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ,ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ,ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ,ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> ). The Precision, Recall and F1-score remained at 98% in both cases.