1,852 research outputs found
Towards Continuous Subject Identification Using Wearable Devices and Deep CNNs
Ā© 2020 IEEE. Subject identification has several applications. In transportation companies, knowing who is driving their vehicles might prevent theft or other ill-intended actions. On the other hand, privacy concerns, and the respective legislation, hinder the applicability of several traditional recognition techniques based on invasive technologies, such as video cameras. Moreover, in order to keep the driver's distractions to a minimum, this technologies must be non-disruptive, that is, they must be able to identify the subject seamlessly without them taking any action. In this context, we propose using deep learning applied to smart watch data for recognizing the person driving a vehicle based on a training set. Our proposal relies on the possibility of using transfer learning to avoid long training sessions for new drivers and to deliver a solution which can be deployed in practice. In this paper, we describe the convolutional neural network used in the solution and present results according to a real data-set collected by us, achieving accuracies ranging from 75 to 94%
UNCTAD/ICC Rules for Multimodal Transport Documents = Pravila UNCTAD/ICC za isprave multimodalnog prijevoza : [translation, parallel texts]
Prijevod dokumentacij
Awareness of Hypertension among Public Secondary School Teachers in a Local Government Area of Ekiti State, Nigeria
Hypertension affects about one billion people and kills about nine million globally. One in every 10
Nigerian adults has high blood p
ressure, less than a third of those with high blood pressure are aware of the
fact that they have hypertension. A descriptive cross sectional study design was employed. The target population
were teachers employed in public secondary schools in a Local Gov
ernment Area in Nigeria. A semi
-
structured
questionnaire was used for data collection. Majority of the respondents were aged 40
ā
49 years (49.3%),
females (68.5%), Christians (86.2%), married (84.7%), Yoruba (98%) and Bachelorās degree holders (46.3%).
Al
l the respondents (100%) have heard about hypertension and majority (55.7%) got the information through
health campaign. The risk factors of hypertension
reported were cigarette smoking (31.5%), alcohol
consumption (43.4%), salt intake (59.6%) and lack of
exercise (60.6%). About 15.8% were aware they were
hypertensive. Eighty three
respondents
(40.9%) consume alcohol. Adding extra salt to food was common among
7.9%, about one
-
tenth (9.4%) smoke cigarette and 27.1% have family history
of hypertension. The fi
ndings of
this study show that hearing
about
is quite different from having a knowledge
of a disease condition. There is
need to
enlighten the population about the risk factors of hypertension
Kitchen Activity Detection for Healthcare using a low-power Radar-Enabled Sensor Network
Human activity detection plays a crucial role in the recognition of activities of daily living (ADLs). In the past ten years, research on activity detection in the home was achieved through the data aggregation from several different sensors (presence sensors, door contacts, appliances tagging, cameras, wearable beacons, mobile phones, etc.). However, the cost of deployment and maintenance of a multitude of sensor devices and the intrusiveness they can infer are quite high. Research on minimal and non-intrusive sensing for recognition of ADLs are vital for the future of remote care. In this paper, we propose a minimal and non-intrusive low-power low-cost radar-based sensing network system that uses an innovative approach for recognizing human activity in the home. We applied our novel approach to the challenging problem of kitchen activity recognition and investigated fifteen different activities. We designed and trained a deep convolutional neural network (DCNN) that classifies different activities based on their distinct micro-Doppler signatures. We achieved an overall classification rate of 92.8% in activity recognition. Most importantly, in nearly real-time, our approach successfully recognized human activities in more than 89% of the time
Social Movements and the Globalisation of Environmental Governance
Summary Environmental governance has become globalised as part of a wider agenda of global governance building within key institutions such as the World Trade Organisation (WTO). The perceived need for global solutions to global environmental problems and calls for sustainable development have put environmental issues onto the agenda of the institutions of global governance, which, some argue, are becoming democratised through consultation with global civil society. This sphere of global civil society, however, is not unproblematic, hosting a diversity of actors such as social movements, business and industry who are clearly not on an equal footing. This article focuses on social movements attempts to influence the international trade agenda, but also takes a look at radical grassroots resistance movements that do not fit easily within this sphere. Their calls for structural transformation of the WTO highlight the lack of democracy and accountability within the WTO that will not be remedied merely through consultation with global civil society
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