Evaluation Of Human Activity Research Articles

CARBON FOOTPRINT EVALUATION AND ENVIRONMENTAL SUSTAINABILITY IMPROVEMENT THROUGH CAPACITY OPTIMIZATION

The burning of fossil fuel for power generation emits Greenhouse gases into the environment. Greenhouse Gases (GHGs) emission is the principal cause of global warming. In order to regulate the emissions of these gases, the emissions need to be assessed and quantified. Carbon footprint is the evaluation of human activities that lead to GHGs emissions. The Covenant University Electricity Network during periods when utility supply fail runs on diesel powered generators located at different centers within the campus. These generators emit carbon-based compounds into the environment. Assessing the quantity of carbon dioxide which is the principal GHGs emitted per year gives 33.14-tonnes. Analysis of the load profiles in the centers showed that all the eight generators are not supposed to be running simultaneously as it is now. This study developed a new network model where all the generators were integrated into a DC microgrid that ensured resource sharing. The model was simulated using energy management and optimization techniques resulting in reduced micro-generators engagement, Green House Gases emission and fuel consumption. Consequently, carbon dioxide emitted per year dropped to 18.44-tonnes from 33.14-tonnes. The developed model improved the carbon footprint of the campus by as much as 44.3%.

Automatic Extraction and Evaluation of Human Activity Using Conditional Random Fields and Self-Supervised Learning

In our definition, human activity can be expressed by five basic attributes: actor, action, object, time and location. The goal of this paper is describe a method to automatically extract all of the basic attributes and the transition between activities derived from sentences in Japanese web pages. However, previous work had some limitations, such as high setup costs, inability to extract all attributes, limitation on the types of sentences that can be handled, and insufficient consideration interdependency among attributes. To resolve these problems, this paper proposes a novel approach that uses conditional random fields and self-supervised learning. Given a small corpus sample as input, it automatically makes its own training data and a feature model. Based on the feature model, it automatically extracts all of the attributes and the transition between the activities in each sentence retrieved from the Web corpus. This approach treats activity extraction as a sequence labeling problem, and has advantages such as domain-independence, scalability, and does not require any human input. Since it is unnecessary to fix the number of elements in a tuple, this approach can extract all of the basic attributes and the transition between activities by making only a single pass. Additionally, by converting to simpler sentences, the approach can deal with complex sentences retrieved from the Web. In an experiment, this approach achieves high precision (activity: 88.9%, attributes: over 90%, transition: 87.5%).

Evaluation of human activities and sleep-wake identification using wrist actigraphy.

The purpose of this study is to investigate the count-characteristics of wrist actigraphy in basic human activities and to discuss the agreement of sleep-wake identification between polysomnography (PSG) and wrist actigraphy during nocturnal sleep. There was a distinct distribution of actigraphy counts over the studied activities. The evaluation of sleep-wake scoring using the wrist actigraphy agreed 96.9% with the polysomnographic scoring during nocturnal sleep.

PS-48-8 Evaluation of human activities by long period measurement of EMG and body movement

PS-48-8 Evaluation of human activities by long period measurement of EMG and body movement