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 _a ), 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 _a ^w , 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 _x ^w ,a _y ^w ,a _z ^w ). 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 _a ^w ) and 98.4% accuracy for 6D acceleration and angular velocities (a _x ^w ,a _y ^w ,a _z ^w ,ω _x ^w ,ω _y ^w ,ω _z ^w ). The Precision, Recall and F1-score remained at 98% in both cases.

Emotions are a crucial part of our daily lives, and they are defined as an organism’s complex reaction to significant objects or events, which include subjective and physiological components. Human emotion recognition has a variety of commercial applications, including intelligent automobile systems, affect-sensitive systems for customer service and contact centres, and the entertainment sector. In this work, we present a novel deep neural network of the Convolutional Neural Network - Bidirectional Gated Recurrent Unit (CNN-RNN) that can classify six basic emotions with an accuracy of above 95%. The deep model was trained on human gait data captured with body-mounted inertial sensors. We also proposed a reduction in the input space by utilizing 1D magnitudes of 3D accelerations and 3D angular velocities ($\widehat{ma{g}_a}$, $\widehat{ma{g}_{\omega }}$), which not only minimizes the computational complexity but also yields better classification accuracies. We compared the performance of the proposed model with existing methodologies and observed that the model outperforms the state-of-the-art.

The aim of this comment paper is to identify a technical error in the published article of Nizam Chew et al.