State of the art on ethical, legal, and social issues linked to audio- and video-based AAL solutions - Uploaded on December 29, 2021

Ambient assisted living (AAL) technologies are increasingly presented and sold as essential smart additions to daily life and home environments that will radically transform the healthcare and wellness markets of the future. An ethical approach and a thorough understanding of all ethics in surveillance/monitoring architectures are therefore pressing. AAL poses many ethical challenges raising questions that will affect immediate acceptance and long-term usage. Furthermore, ethical issues emerge from social inequalities and their potential exacerbation by AAL, accentuating the existing access gap between high-income countries (HIC) and low and middle-income countries (LMIC). Legal aspects mainly refer to the adherence to existing legal frameworks and cover issues related to product safety, data protection, cybersecurity, intellectual property, and access to data by public, private, and government bodies. Successful privacy-friendly AAL applications are needed, as the pressure to bring Internet of Things (IoT) devices and ones equipped with artificial intelligence (AI) quickly to market cannot overlook the fact that the environments in which AAL will operate are mostly private (e.g., the home). The social issues focus on the impact of AAL technologies before and after their adoption. Future AAL technologies need to consider all aspects of equality such as gender, race, age and social disadvantages and avoid increasing loneliness and isolation among, e.g. older and frail people. Finally, the current power asymmetries between the target and general populations should not be underestimated nor should the discrepant needs and motivations of the target group and those developing and deploying AAL systems. Whilst AAL technologies provide promising solutions for the health and social care challenges, they are not exempt from ethical, legal and social issues (ELSI). A set of ELSI guidelines is needed to integrate these factors at the research and development stage

State of the art on ethical, legal, and social issues linked to audio- and videobased AAL solutions

Working Group 1. Social responsibility: Ethical, legal, social, data protection and privacy issuesAbstract Ambient assisted living (AAL) technologies are increasingly presented and sold as essential smart additions to daily life and home environments that will radically transform the healthcare and wellness markets of the future. An ethical approach and a thorough understanding of all ethics in surveillance/monitoring architectures are therefore pressing. AAL poses many ethical challenges raising questions that will affect immediate acceptance and long-term usage. Furthermore, ethical issues emerge from social inequalities and their potential exacerbation by AAL, accentuating the existing access gap between high-income countries (HIC) and low and middle-income countries (LMIC). Legal aspects mainly refer to the adherence to existing legal frameworks and cover issues related to product safety, data protection, cybersecurity, intellectual property, and access to data by public, private, and government bodies. Successful privacy-friendly AAL applications are needed, as the pressure to bring Internet of Things (IoT) devices and ones equipped with artificial intelligence (AI) quickly to market cannot overlook the fact that the environments in which AAL will operate are mostly private (e.g., the home). The social issues focus on the impact of AAL technologies before and after their adoption. Future AAL technologies need to consider all aspects of equality such as gender, race, age and social disadvantages and avoid increasing loneliness and isolation among, e.g. older and frail people. Finally, the current power asymmetries between the target and general populations should not be underestimated nor should the discrepant needs and motivations of the target group and those developing and deploying AAL systems. Whilst AAL technologies provide promising solutions for the health and social care challenges, they are not exempt from ethical, legal and social issues (ELSI). A set of ELSI guidelines is needed to integrate these factors at the research and development stage. Keywords Ethical principles, Privacy, Assistive Living Technologies, Privacy by Design, General Data Protection Regulation.publishedVersio

From A to Z: Wearable technology explained

Wearable technology (WT) has become a viable means to provide low-cost clinically sensitive data for more informed patient assessment. The benefit of WT seems obvious: small, worn discreetly in any environment, personalised data and possible integration into communication networks, facilitating remote monitoring. Yet, WT remains poorly understood and technology innovation often exceeds pragmatic clinical demand and use. Here, we provide an overview of the common challenges facing WT if it is to transition from novel gadget to an efficient, valid and reliable clinical tool for modern medicine. For simplicity, an A–Z guide is presented, focusing on key terms, aiming to provide a grounded and broad understanding of current WT developments in healthcare

TinyML based Deep Learning Model for Activity Detection

Our physical and emotional well-being are directly impacted by our body positions. In addition to promoting a confident, upright image, maintaining good body posture during various activities also ensures that our musculoskeletal system is properly aligned. On the other side, bad posture can result in a number of musculoskeletal conditions, discomfort, and reduced productivity. Accurate systems that can detect posture in real time, activity detection, are required due to the rising use of wearable technology and the growing interest in health and fitness tracking. The goal of this project is to create a TinyML model for wearable activity detection that will allow users to assess their posture and make necessary corrections in order to improve their health and general well-being. The project intends to contribute to the creation of useful posture detection technologies that can be quickly implemented on wearable devices for widespread usage by leveraging machine learning algorithms and wearable sensor data. For reliable posture categorization, the model architecture combines deep neural networks (DNN) and LSTM layers. With the development and implementation of the TinyML model, a significant decrease in the model's power consumption, memory, and latency was achieved without any compromise in the accuracy. This work can be used in the fields of health, wellness, rehabilitation, corporate life, sports and fitness to keep track of calories burned, activity duration, distance traveled, posture analysis, and real-time tracking

The Use of Wearable Technologies in Combating Non Communicable Disease among the Elders in Uganda - Case Study: Masaka City and District

Background:Non-communicable diseases (NCDs) is becoming a burden in developing countries and accelerating day and night. The capacity of health systems in most of these countries is overwhelmed by the huge number of communicable diseases. This has resulted into double burden of diseases for these countries. NCDs have contributed to impediments in development due to increased poverty that is associated with increased mobility, looking for medication. The demand for developing elderly care services increased, and this was aiming at deploying novel technologies in order to provide independent living. This is aimed at creating an environment where elderly can have a consistent and independent health monitor, in order to detect any immerging complications before they become worse. IoT and smart wearable Technologies could provide a promising solution to this problem. These technologies have the capacity to enhance elderly people’s quality of life, and also cut costs, and strains on the health care givers. A Survey strategy was used, supported by questionnaires, observations and interview, to support mixed research methodology’. A cross-sectional time horizon was adopted to support a one round data collection.  The findings indicated that, majority of the elderly people are suffering from hypertension, followed by diabetes. However, most of them are poor and cannot afford medication. Keywords: Non communicable diseases, Wearable devices/technologies, Internet of Things (IOT). DOI: 10.7176/IKM/13-6-02 Publication date:September 30th 202

Highly-efficient fog-based deep learning AAL fall detection system

[EN] Falls is one of most concerning accidents in aged population due to its high frequency and serious repercussion; thus, quick assistance is critical to avoid serious health consequences. There are several Ambient Assisted Living (AAL) solutions that rely on the technologies of the Internet of Things (IoT), Cloud Computing and Machine Learning (ML). Recently, Deep Learning (DL) have been included for its high potential to improve accuracy on fall detection. Also, the use of fog devices for the ML inference (detecting falls) spares cloud drawback of high network latency, non-appropriate for delay-sensitive applications such as fall detectors. Though, current fall detection systems lack DL inference on the fog, and there is no evidence of it in real environments, nor documentation regarding the complex challenge of the deployment. Since DL requires considerable resources and fog nodes are resource-limited, a very efficient deployment and resource usage is critical. We present an innovative highly-efficient intelligent system based on a fog-cloud computing architecture to timely detect falls using DL technics deployed on resource-constrained devices (fog nodes). We employ a wearable tri-axial accelerometer to collect patient monitoring data. In the fog, we propose a smart-IoT-Gateway architecture to support the remote deployment and management of DL models. We deploy two DL models (LSTM/GRU) employing virtualization to optimize resources and evaluate their performance and inference time. The results prove the effectiveness of our fall system, that provides a more timely and accurate response than traditional fall detector systems, higher efficiency, 98.75% accuracy, lower delay, and service improvement.This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's Horizon 2020 research and innovation program as part of the ACTIVAGE project under Grant 732679.Sarabia-Jácome, D.; Usach, R.; Palau Salvador, CE.; Esteve Domingo, M. (2020). Highly-efficient fog-based deep learning AAL fall detection system. Internet of Things. 11:1-19. https://doi.org/10.1016/j.iot.2020.100185S11911“World Population Ageing.” [Online]. Available: http://www.un.org/esa/population/publications/worldageing19502050/. [Accessed: 23-Sep-2018].“Falls, ” World Health Organization. [Online]. Available: http://www.who.int/news-room/fact-sheets/detail/falls. 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Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security

Ethics of the health-related internet of things: a narrative review

The internet of things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related internet of things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research data collection. H-IoT promises many benefits for health and healthcare. However, it also raises a host of ethical problems stemming from the inherent risks of Internet enabled devices, the sensitivity of health-related data, and their impact on the delivery of healthcare. This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT

Intuitive IoT-based H2U Healthcare System for Elderly People

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