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.

Time-series data can be generated by wearable sensors such as accelerometers, gyroscopes, and magnetometers. This data may be used to classify various everyday life activities using machine learning or deep learning models. Athletics, education, child monitoring, ambient assisted living, and other applications benefit from human activity recognition. Human activity recognition includes sports activity recognition. A typical sports activity is any action that is often employed in a variety of sports. Walking, jogging, sprinting, and leaping are basic sports motions. A unique sports action exists exclusively in one sport, such as a badminton smash. We proposed a shallow convolution neural network for sports activity recognition. It has just 1251 trainable parameters. The test accuracy attained is 98.387%. The average F1 score, recall, and precision are 98.0, 98.7, and 98.0%, respectively. We have also trained the model on a benchmark human activity recognition dataset from WISDM lab for performance evaluation and comparison of the model.

Human Activity Recognition (HAR) is an essential component of ambient assistive living. HAR has traditionally relied on computer vision techniques. However, it has several drawbacks, including lack of privacy, higher operational costs, and being constrained by the number of spaces available for cameras, so it can't be used for applications that require long-term monitoring of people. The use of initial sensors is proving to be a vital solution for HAR. Smartphones and Smartwatches have embedded accelerometers and gyroscope sensors that help deep neural networks for reliable activity recognition and classification problems. In this paper, we have presented a novel deep neural network model which is opposite to the traditional models. It has a Gated Recurrent Unit (GRU) layer followed by different kernel-sized convolutional layers. Our model has been evaluated on an openly available dataset by the (Wireless Sensor Data Mining) WISDM lab and comparison with previously existing models proves that it outperforms them.

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 Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person’s psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.

Applications like Big Data, Machine Learning, Deep Learning and even other Engineering and Scientific research requires a lot of computing power; making High-Performance Computing (HPC) an important field. But access to Supercomputers is out of range from the majority. Nowadays Supercomputers are actually clusters of computers usually made-up of commodity hardware. Such clusters are called Beowulf Clusters. The history of which goes back to 1994 when NASA built a Supercomputer by creating a cluster of commodity hardware. In recent times a lot of effort has been done in making HPC Clusters of even single board computers (SBCs). Although the creation of clusters of commodity hardware is possible but is a cumbersome task. Moreover, the maintenance of such systems is also difficult and requires special expertise and time. The concept of cloud is to provide on-demand resources that can be services, platform or even infrastructure and this is done by sharing a big resource pool. Cloud computing has resolved problems like maintenance of hardware and requirement of having expertise in networking etc. An effort is made of bringing concepts from cloud computing to HPC in order to get benefits of cloud. The main target is to create a system which can develop a capability of providing computing power as a service which to further be referred to as Supercomputer as a service. A prototype was made using Raspberry Pi (RPi) 3B and 3B+ Single Board Computers. The reason for using RPi boards was increasing popularity of ARM processors in the field of HPC