scholarly journals Application of Smartwatches in Elderly Care with Indoor Localization Functionality

2021 ◽  
Vol 15 (05) ◽  
pp. 174
Author(s):  
Laszlo Arvai
Keyword(s):  
Mobile Technology ◽  
Indoor Localization ◽  
Elderly Care ◽  
Health Condition ◽  
General Purpose ◽  
Fine Tuning ◽  
Motion Patterns ◽  
Motion Activity ◽  
Processing Algorithms ◽  
Tuning Methods

The recent achievements in mobile technology and wearable OS makes possible to create comfortably wearable and very capable smartwatches. They have many different sensors and powerful hardware combined with general purpose OS and all this available for reasonable price. It makes it ideal device for elderly care. Monitoring the elderly’s basic health condition is very straightforward, but using smartwatch as an indoor localization device, monitoring the motion activity, recognizing the typical motion patterns of wandering is not simple. Even those watches are really capable devices, they are not equipped with direct indoor localization sensors and we would like to avoid installing special equipment’s, markers, transmitters in the home of elderly. Using only a commercially available smartwatch hardware for indoor localization is a challenging task, several filtering and data processing algorithms needs to be combined in order to provide acceptable indoor localization function. The algorithms, their connection and fine-tuning methods are explained in this article.

Listen2Cough

2021 ◽  
Vol 5 (1) ◽  
pp. 1-22
Author(s):  
Xuhai Xu ◽  
Ebrahim Nemati ◽  
Korosh Vatanparvar ◽  
Viswam Nathan ◽  
Tousif Ahmed ◽  
...  
Keyword(s):  
Health Assessment ◽  
Ground Truth ◽  
Health Condition ◽  
Fine Tuning ◽  
Detection Model ◽  
Assessment Tasks ◽  
Augmentation Techniques ◽  
In The Wild ◽  
The Rich ◽  
Lung Health

The prevalence of ubiquitous computing enables new opportunities for lung health monitoring and assessment. In the past few years, there have been extensive studies on cough detection using passively sensed audio signals. However, the generalizability of a cough detection model when applied to external datasets, especially in real-world implementation, is questionable and not explored adequately. Beyond detecting coughs, researchers have looked into how cough sounds can be used in assessing lung health. However, due to the challenges in collecting both cough sounds and lung health condition ground truth, previous studies have been hindered by the limited datasets. In this paper, we propose Listen2Cough to address these gaps. We first build an end-to-end deep learning architecture using public cough sound datasets to detect coughs within raw audio recordings. We employ a pre-trained MobileNet and integrate a number of augmentation techniques to improve the generalizability of our model. Without additional fine-tuning, our model is able to achieve an F1 score of 0.948 when tested against a new clean dataset, and 0.884 on another in-the-wild noisy dataset, leading to an advantage of 5.8% and 8.4% on average over the best baseline model, respectively. Then, to mitigate the issue of limited lung health data, we propose to transform the cough detection task to lung health assessment tasks so that the rich cough data can be leveraged. Our hypothesis is that these tasks extract and utilize similar effective representation from cough sounds. We embed the cough detection model into a multi-instance learning framework with the attention mechanism and further tune the model for lung health assessment tasks. Our final model achieves an F1-score of 0.912 on healthy v.s. unhealthy, 0.870 on obstructive v.s. non-obstructive, and 0.813 on COPD v.s. asthma classification, outperforming the baseline by 10.7%, 6.3%, and 3.7%, respectively. Moreover, the weight value in the attention layer can be used to identify important coughs highly correlated with lung health, which can potentially provide interpretability for expert diagnosis in the future.

The Challenge of Organizing Elderly Care Programmes: Optimal Policy Design under Complete and Asymmetric Information

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Lucia Leporatti ◽  
Marcello Montefiori
Keyword(s):  
Policy Design ◽  
Elderly Care ◽  
Health Condition ◽  
Social Changes ◽  
Term Care ◽  
Sustainable Solutions ◽  
Care Services ◽  
Lump Sum ◽  
Working Age ◽  
Provision Of Care

Abstract The increasing life expectancy poses challenges on the future sustainability of long-term care services that today strongly depend on informal care provided within the family by working age children. Ongoing social changes are likely to weaken the informal provision of care. The paper derives optimal policies to help the policy-maker to choose innovative and sustainable solutions to support home care, taking into account the severity of health condition and the different opportunity costs of carers. Drawing inspiration from real world policies, the suitable policy combines lump-sum transfers, paid permissions from work and in-kind provisions. In some circumstances, benefits can favour higher rather than lower income individuals. In the context of information asymmetry, the implementation of the second-best outcome requires the level of care of the most subsidized households’ to be forced towards certain targets to avoid adverse selection.

Energy Harvesting for Smart Shoes: A Real Life Application

2013 ◽  
Author(s):  
Emanuele Frontoni ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Andrea Gatto
Keyword(s):  
Energy Harvesting ◽  
Power Efficiency ◽  
Indoor Localization ◽  
Electrical Power ◽  
Real Life ◽  
Reducing Power ◽  
Electrical Energy ◽  
General Purpose ◽  
Technical Developments ◽  
Microprocessor Technology

Advanced technical developments have increased the efficiency of devices in capturing trace amounts of energy from the environment (such as from human movements) and transforming them into electrical energy (e.g., to instantly charge mobile devices). In addition, advancements in microprocessor technology have increased power efficiency, effectively reducing power consumption requirements. In combination, these developments have sparked interest in the engineering community to develop more and more applications that utilize energy harvesting for power. The approach here described aims to designing and manufacturing an innovative easy-to-use and general-purpose device for energy harvesting in general purpose shoes. The novelty of this device is the integration of polymer and ceramic piezomaterials accomplished by injection molding. In this spirit, this paper examines different devices that can be built into a shoe, (where excess energy is readily harvested) and used for generating electrical power while walking. A Main purpose is the development of an indoor localization system embedded in shoes that periodically broadcasts a digital RFID as the bearer walks. Results are encouraging and real life test are conducted on the first series of prototypes.

scholarly journals "LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia

2020 ◽  
Author(s):  
Luis H. S. Vogado ◽  
Rodrigo M. S. Veras ◽  
Kelson R. T. Aires
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Data Augmentation ◽  
Fine Tuning ◽  
Current State ◽  
Leukemia Diagnosis ◽  
New Guidelines ◽  
Texture Contrast ◽  
Tuning Methods

Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.

scholarly journals Emerging trends: A gentle introduction to fine-tuning

2021 ◽  
Vol 27 (6) ◽  
pp. 763-778
Author(s):  
Kenneth Ward Church ◽  
Zeyu Chen ◽  
Yanjun Ma
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
General Purpose ◽  
Fine Tuning ◽  
Language Engineering ◽  
Training Models ◽  
Emerging Trends ◽  
Foundation Model ◽  
Programming Skills

AbstractThe previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.

scholarly journals Indoor Localization of Quadcopters in Industrial Environment

2020 ◽  
Author(s):  
Péter Troll ◽  
Károly Szipka ◽  
Andreas Archenti
Keyword(s):  
Computer Vision ◽  
Material Handling ◽  
Evaluation Method ◽  
Indoor Localization ◽  
Research Work ◽  
Fine Tuning ◽  
Measurement Unit ◽  
Positioning System ◽  
Estimation System ◽  
Slam Algorithm

The research work in this paper was carried out to reach advanced positioning capabilities of unmanned aerial vehicles (UAVs) for indoor applications. The paper includes the design of a quadcopter and the implementation of a control system with the capability to position the quadcopter indoor using onboard visual pose estimation system, without the help of GPS. The project also covered the design and implementation of quadcopter hardware and the control software. The developed hardware enables the quadcopter to raise at least 0.5kg additional payload. The system was developed on a Raspberry single-board computer in combination with a PixHawk flight controller. OpenCV library was used to implement the necessary computer vision. The Open-source software-based solution was developed in the Robotic Operating System (ROS) environment, which performs sensor reading and communication with the flight controller while recording data about its operation and transmits those to the user interface. For the vision-based position estimation, pre-positioned printed markers were used. The markers were generated by ArUco coding, which exactly defines the current position and orientation of the quadcopter, with the help of computer vision. The resulting data was processed in the ROS environment. LiDAR with Hector SLAM algorithm was used to map the objects around the quadcopter. The project also deals with the necessary camera calibration. The fusion of signals from the camera and from the IMU (Inertial Measurement Unit) was achieved by using Extended Kalman Filter (EKF). The evaluation of the completed positioning system was performed with an OptiTrack optical-based external multi-camera measurement system. The introduced evaluation method has enough precision to be used to investigate the enhancement of positioning performance of quadcopters, as well as fine-tuning the parameters of the used controller and filtering approach. The payload capacity allows autonomous material handling indoors. Based on the experiments, the system has an accurate positioning system to be suitable for industrial application.

scholarly journals Monitor for Anti-Aircraft Guidance and Observation Systems

2019 ◽  
Vol 10 (2) ◽  
pp. 143-150
Author(s):  
Rafał KRUK ◽  
Zbigniew REMPAŁA
Keyword(s):  
Signal Processing ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Performance Comparison ◽  
Digital Data ◽  
Data Filtering ◽  
Observation Systems ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Graphics Processing

The paper presents a discussion on the issue of possible acceleration of radiolocation signal processing algorithms in seekers using graphics processing units. A concept and implementation examples of algorithms performing digital data filtering on general purpose central and graphics processing units are introduced. The results of performance comparison of central and graphics processing units during computing discrete convolution are presented at the end of the paper.

scholarly journals Detection of Manipulated Face Videos over Social Networks: A Large-Scale Study

2021 ◽  
Vol 7 (10) ◽  
pp. 193
Author(s):  
Federico Marcon ◽  
Cecilia Pasquini ◽  
Giulia Boato
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Forensic Analysis ◽  
General Purpose ◽  
Fine Tuning ◽  
Specific Technique ◽  
Multimedia Forensics ◽  
Shared Data ◽  
Social Media Platforms ◽  
The Web

The detection of manipulated videos represents a highly relevant problem in multimedia forensics, which has been widely investigated in the last years. However, a common trait of published studies is the fact that the forensic analysis is typically applied on data prior to their potential dissemination over the web. This work addresses the challenging scenario where manipulated videos are first shared through social media platforms and then are subject to the forensic analysis. In this context, a large scale performance evaluation has been carried out involving general purpose deep networks and state-of-the-art manipulated data, and studying different effects. Results confirm that a performance drop is observed in every case when unseen shared data are tested by networks trained on non-shared data; however, fine-tuning operations can mitigate this problem. Also, we show that the output of differently trained networks can carry useful forensic information for the identification of the specific technique used for visual manipulation, both for shared and non-shared data.

scholarly journals Acceleration of Signal Processing Algorithms in Seekers Using Graphics Processing Units

2019 ◽  
Vol 10 (1) ◽  
pp. 91-98
Author(s):  
Piotr TUREK
Keyword(s):  
Signal Processing ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Performance Comparison ◽  
Digital Data ◽  
Data Filtering ◽  
Discrete Convolution ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Graphics Processing

The paper presents a discussion on the issue of possible acceleration of radiolocation signal processing algorithms in seekers using graphics processing units. A concept and implementation examples of algorithms performing digital data filtering on general purpose central and graphics processing units are introduced. The results of performance comparison of central and graphics processing units during computing discrete convolution are presented at the end of the paper.

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