Augmented Smart Research Articles

Forecasting energy power consumption using federated learning in edge computing devices

Several studies in the literature propose using machine learning algorithms to forecast consumers’ energy consumption. However, such data is sensitive and has privacy constraints. On the other hand, federated learning is a technique in which the training of machine learning algorithms is performed locally, where the data is generated. In this context, this article presents a hybrid neural network architecture, named CNN-LSTM FED, trained using the public Smart* and The Building Data Genome Project 2 datasets. Additionally, an augmented Smart* dataset was generated using Generative Adversarial Networks (GANs). The performance of CNN-LSTM FED was evaluated by comparing it against the MultiLayer Perceptron (MLP), which serves as a baseline, and against a non-federated version of the CNN-LSTM FED, named CNN-LSTM. Our approach was able to generalize the model even when less than 1% of buildings participated in the modeling process, forecasting with good results the energy consumption of other buildings. Furthermore, the deployment of this architecture in an edge computing device, with limited computational resources for training, is evaluated.

Augmented Smart Insoles – Prototyping a Mobile Application: Usage Preferences of Healthcare Professionals and People with Foot Deformities

Abstract The augmentation of smart insoles gained increased interest due to the possibilities in foot-related rehabilitation, medical treatment, and postural risk prevention. As shown by previous work, the technology offers a personalized solution for individual foot morphology and biomechanics, which can monitor the health status and healing process. However, the success of this mobile technology depends on its usability and acceptance by healthcare professionals and patients. Therefore, an exploratory qualitative user study was conducted to investigate the preferences of healthcare professionals and patients regarding augmented smart insoles (ASI) usage. In focus groups, experts and patients explored their needs and suggested potential design solutions. The thematic analysis of the participants’ feedback revealed useful information regarding user interface (UI) designs for the development of a mobile application supporting smart insoles. We found that patients and experts share similar data preferences for a mobile application supporting ASI and that both user groups desired the presence of complete pressure point monitoring, active feedback for posture correction, and personalized training videos for postural risk prevention. However, healthcare professionals expressed concerns regarding the lack of scientific evidence validating the effectiveness of this technology and suggested further research.

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model (R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100–2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.

Survey on: Applications of Smart Wearable Technology in Health Insurance

Humanity is considered as the most intelligent species on the earth and are health reactive. Today, due to increase in sluggish behavior there is reduction in physical activity and that leads to health problems. To overcome this cause, wearable technology helps human being to get updates about physical health behavior e.g. heart rate, blood pressure, steps count, BMI, calories and many more. Wearables available in market helps to measure all kind of metrics related to health. The aim of this research is to study and analyze the research to check whether wearable technologies with augmented smart AI solutions help to predict health status and its outcome. Leveraging Artificial Intelligence (AI) and wearable technology could help health insurance to cover the risks associated with the uncertainties in the health, increased life span and high cost of treatments. In this study, we first discuss the works where wearable technology and AI can create value in the field of healthcare and importantly discuss the use case of leveraging the insurance domain with a smart health-monitoring service for the customers. Although, there are inhibitions in implementing a smart and fitness monitoring system in health-insurance sector like privacy concerns and hesitations towards sharing the data, health-insurance sector can look at this use-case as an incentivization to the fitness conscious clients.

Augmented Smart Rice Cooker Technology System (ASRCTS)

Augmented Smart Rice Cooker Technology System using AT89S52 microcontroller is an exclusive project which allows the ASRCS to be cooked without human intervention. The aim of this project is to fully automization of the rice cooker to cook the rice without human intervention. When you are late from work and at the same time when you return your home, you want your food to be ready to eat. From this concept, Augmented Smart Rice Cooker is born. In this project, we use storage chamber for rice and water tank fully integrated with the conventional rice cooker. When we press the switch or mobile key, the rice will be poured in the main cooking chamber and at the same time, water will also be poured for some period of time, say 10 seconds for 1 person. After that, the rice will be cooked and stop and remain in warm condition when cooking is over. This project helps to reduce the human effort in great extend.

Measuring weight and location of individual bites using a sensor augmented smart plate.

In this work, a novel plate system that can detect weight and location of individual bites during meals is presented. The system consists of a base station with sensors and a detachable off-the-shelf polymer plate with three com- partments. By combining data from multiple weight sensors, the weight of individual bites can be accurately measured and localized on the plate to determine the compartment from which they were taken. With prior knowledge of the weight of the food in each compartment at the start of the meal, the system can estimate the nutritional value of the consumed food. In a test conducted in a controlled home environment, the system was able to measure the weight of consumed food in each compartment with a maximum relative error of 1.4%. The goal of the system is to replace traditional monitoring tools and to automatically monitor the amount of consumption.

Close Integration of a UHF-RFID Transponder Into a Limb Prosthesis for Tracking and Sensing

The technology of structural radio systems that is well assessed in the avionic and naval communications is here applied to obtain antenna functionality out of a limb prosthesis with minimal changes to the device, in specific, an orthopedic nail. A microchip transponder based on the ultrahigh frequency-radio-frequency identification (RFID) communication standard is connected to the nail by means of a central notch forming a towel-bar-like antenna. The resulting device, called prosthetic structural tag, is such to preserve the mechanical continuity of the original nail, but it is also capable of energy harvesting and RFID. The electrical and geometrical control parameters for impedance tuning were identified using computer simulation and laboratory tests. The radiation performance is mostly dependent on the geometry of the notch, while it is rather unaffected by the length of the nail, so that the proposed layout may also be applied to different kinds of prosthesis. The experimented read distance at 870-960 MHz was more than 35-cm far from the limb surface. The augmented smart prosthesis is, hence, suitable to be monitored using an external non-contacting antenna for application to tracking and, in the near future, to monitor the prosthesis health status.

Constructivist Research in Smart Tourism

Smart tourism is a social phenomenon arising from the convergence of information technology with the tourism experience. New ways of doing business, new patterns of experience and new problems concerning tourism destination image management and marketing are emerging due to the ubiquitous presence and influence of the internet and mobile devices. New conceptual tools are also available to enable researchers to further understand the social implications as well as the practical implementation of these new virtual and augmented smart tourism ecosystems. To this effect this paper introduces the constructivist paradigm and associated research methodologies as another toolbox for interpreting how smart tourism works as a form of soft power. The implications revealed by constructivism are that through smart tourism ecosystems, destination commodification and commoditization, experience and image formation are increasingly self-per-petuating, autonomous and organic social constructions. Researchers in information technology can use con-structivist research to further explore these dynamic developments in smart tourism.