Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

New methods for stress assessment and monitoring at the workplace

The topic of stress is nowadays a very important one, not only in research but on social life in general. People are increasingly aware of this problem and its consequences at several levels: health, social life, work, quality of life, etc. This resulted in a significant increase in the search for devices and applications to measure and manage stress in real-time. Recent technological and scientific evolution fosters this interest with the development of new methods and approaches. In this paper we survey these new methods for stress assessment, focusing especially on those that are suited for the workplace: one of today’s major sources of stress. We contrast them with more traditional methods and compare them between themselves, evaluating nine characteristics. Given the diversity of methods that exist nowadays, this work facilitates the stakeholders’ decision towards which one to use, based on how much their organization values aspects such as privacy, accuracy, cost-effectiveness or intrusivenes

New methods for stress assessment and monitoring at the workplace

The topic of stress is nowadays a very important one, not only in research but on social life in general. People are increasingly aware of this problem and its consequences at several levels: health, social life, work, quality of life, etc. This resulted in a significant increase in the search for devices and applications to measure and manage stress in real-time. Recent technological and scientific evolution fosters this interest with the development of new methods and approaches. In this paper we survey these new methods for stress assessment, focusing especially on those that are suited for the workplace: one of today’s major sources of stress. We contrast them with more traditional methods and compare them between themselves, evaluating nine characteristics. Given the diversity of methods that exist nowadays, this work facilitates the stakeholders’ decision towards which one to use, based on how much their organization values aspects such as privacy, accuracy, cost-effectiveness or intrusivenes

Applications of cooperative WSN in homecare systems

Cooperation plays the crucial role in shared space of the homecare processes. It is a rather hard task to ensure effective cooperation in home care environment. This is due to variability of schedules, tasks and mobility of both patients and carers. In this paper, we discuss sensor network technology that can facilitate and improve home care cooperation scenarios. We present methodology, recommendations and applications for incorporating a WSN based solution in various areas of Homecare. We argue that even the most difficult areas of cooperation between patients and their carers such as: information retrieval, information dissemination, scheduling, coordination of short and long-term treatment can be supported by WSN based solutions. Finally, we discuss sensor network design approaches for incorporating smart communication devices and sensors to support health care workers and their patients in their daily activities. The network of smart sensors can help to maintain awareness of the activities of all stakeholders and the need to integrate communication and computer technology with the requirements of effective aged care infrastructure. © 2008 IEEE

Monitoring of gas emissions at landfill sites using autonomous gas sensors

Executive Summary This report details the work carried out during the Smart Plant project (2005-AIC-MS-43-M4). As part of this research, an autonomous platform for monitoring greenhouse gases (methane (CH4), carbon dioxide (CO2)) has been developed, prototyped and field validated. The modular design employed means that the platform can be readily adapted for a variety of applications involving these and other target gases such as hydrogen sulfide (H2S), ammonia (NH3) and carbon monoxide (CO) and the authors are in the process of completing several short demonstrator projects to illustrate the potential of the platform for some of these applications. The field validation for the greenhouse gas monitoring platform was carried out at two landfill sites in Ireland. The unit was used to monitor the concentration of CO2 and CH4 gas at perimeter borehole wells. The final prototype was deployed for over 4 months and successfully extracted samples from the assigned perimeter borehole well headspace, measured them and sent the data to a database via a global system for mobile (GSM) communications. The data were represented via an updating graph in a web interface. Sampling was carried out twice per day, giving a 60-fold increase on current monitoring procedures which provide one gas concentration measurement per month. From additional work described in this report, a number of conclusions were drawn regarding lateral landfill gas migration on a landfill site and the management of this migration to the site’s perimeter. To provide frequent, reliable monitoring of landfill gas migration to perimeter borehole wells, the unit needs to: • Be fully autonomous; • Be capable of extracting a gas sample from a borehole well independently of personnel; • Be able to relay the data in near real time to a base station; and • Have sensors with a range capable of adequately monitoring gas events accurately at all times. The authors believe that a unit capable of such monitoring has been developed and validated. This unit provides a powerful tool for effective management of landfill site gases. The effectiveness of this unit has been recognised by the site management team at the long-term deployment trial site, and the data gathered have been used to improve the day-to-day operations and gas management system on-site. The authors make the following recommendations: 1. The dynamics of the landfill gas management system cannot be captured by taking measurements once per month; thus, a minimum sampling rate of once per day is advised. 2. The sampling protocol should be changed: (i) Borehole well samples should not be taken from the top of the well but should be extracted at a depth within the headspace (0.5–1.0 m). The measurement depth will be dependent on the water table and headspace depth within the borehole well. (ii) The sampling time should be increased to 3 min to obtain a steady-state measurement from the headspace and to take a representative sample; and (iii) For continuous monitoring on-site, the extracted sample should be recycled back into the borehole well. However, for compliance monitoring, the sample should not be returned to the borehole well. 3. Devices should be placed at all borehole wells so the balance on the site can be maintained through the gas management system and extraction issues can be quickly recognised and addressed before there are events of high gas migration to the perimeter. 4. A pilot study should be carried out by the EPA using 10 of these autonomous devices over three to five sites to show the need and value for this type of sampling on Irish landfill sites

Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively