A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach.

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson's disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson's disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability.

A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson s disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson s disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability.

Preliminary results of ON/OFF detection using an integrated system for Parkinson's disease monitoring

This paper describes the experimental set up of a system composed by a set of wearable sensors devices for the recording of the motion signals and software algorithms for the signal analysis. This system is able to automatically detect and assess the severity of bradykinesia, tremor, dyskinesia and akinesia motor symptoms. Based on the assessment of the akinesia, the ON-OFF status of the patient is determined for each moment. The assessment performed through the automatic evaluation of the akinesia is compared with the status reported by the patients in their diaries. Preliminary results with a total recording period of 32 hours with two PD patients are presented, where a good correspondence (88.2 +/- 3.7 %) was observed. Best (93.7 por ciento) and worst (87 por ciento) correlation results are illustrated, together with the analysis of the automatic assessment of the akinesia symptom leading to the status determination. The results obtained are promising, and if confirmed with further data, this automatic assessment of PD motor symptoms will lead to a better adjustment of medication dosages and timing, cost savings and an improved quality of life of the patients.

Lessons learned on home energy monitoring and management: Smartcity Málaga

The Smartcity Málaga project is one of Europe?s largest ecoefficient city initiatives. The project has implemented a field trial in 50 households to study the effects of energy monitoring and management technologies on the residential electricity consumption. This poster presents some lessons learned on energy consumption trends, smart clamps reliability and the suitability of power contracted by users, obtained after six months of data analysis.

CO2and Rn degassing from the natural analog of Campo de Calatrava(Spain): Implications for monitoring of CO2storage sites

Natural analogs offer a valuable opportunity to investigate the long-term impacts associated with thepotential leakage in geological storage of CO2.Degassing of CO2and radon isotopes (222Rn?220Rn) from soil, gas vents and thermal water dischargeswas investigated in the natural analog of Campo de Calatrava Volcanic Field (CCVF; Central Spain) todetermine the CO2?Rn relationships and to assess the role of CO2as carrier gas for radon. Furthermore,radon measurements to discriminate between shallow and deep gas sources were evaluated under theperspective of their applicability in monitoring programs of carbon storage projects.CO2flux as high as 5000 g m?2d?1and222Rn activities up to 430 kBq m?3were measured;220Rn activi-ties were one order of magnitude lower than those of222Rn. The222Rn/220Rn ratios were used to constrainthe source of the Campo de Calatrava soil gases since a positive correlation between radon isotopic ratiosand CO2fluxes was observed. Thus, in agreement with previous studies, our results indicate a deepmantle-related origin of CO2for both free and soil gases, suggesting that carbon dioxide is an efficientcarrier for Rn. Furthermore, it was ascertained that the increase of222Rn in the soil gases was likely pro-duced by two main processes: (i) direct transport by a carrier gas, i.e., CO2and (ii) generation at shallowlevel due to the presence of relatively high concentrations of dissolved U and Ra in the thermal aquiferof Campo de Calatrava.The diffuse CO2soil flux and radon isotopic surveys carried out in the Campo de Calatrava VolcanicFields can also be applicable to geochemical monitoring programs in CCS (Carbon Capture and Storage)areas as these parameters are useful to: (i) constrain CO2leakages once detected and (ii) monitor both theevolution of the leakages and the effectiveness of subsequent remediation activities. These measurementscan also conveniently be used to detect diffuse leakages.

Development of a human movement monitoring system based on wearable devices

It is essential to remotely and continuously monitor the movements of individuals in many social areas, for example, taking care of aging people, physical therapy, athletic training etc. Many methods have been used, such as video record, motion analysis or sensor-based methods. Due to the limitations in remote communication, power consumption, portability and so on, most of them are not able to fulfill the requirements. The development of wearable technology and cloud computing provides a new efficient way to achieve this goal. This paper presents an intelligent human movement monitoring system based on a smartwatch, an Android smartphone and a distributed data management engine. This system includes advantages of wide adaptability, remote and long-term monitoring capacity, high portability and flexibility. The structure of the system and its principle are introduced. Four experiments are designed to prove the feasibility of the system. The results of the experiments demonstrate the system is able to detect different actions of individuals with adequate accuracy.

Unified representation of monitoring information across federated cloud infrastructures

Nowadays one of the issues hindering the potential of federating cloud-based infrastructures to reach much larger scales is their standard management and monitoring. In particular, this is true in cases where these federated infrastructures provide emerging Future Internet and Smart Cities-oriented services, such as the Internet of Things (IoT), that benefit from cloud services. The contribution of this paper is the introduction of a unified monitoring architecture for federated cloud infrastructures accompanied by the adoption of a uniform representation of measurement data. The presented solution is capable of providing multi-domain compatibility, scalability, as well as the ability to analyze large amounts of monitoring data, collected from datacenters and offered through open and standardized APIs. The solution described herein has been deployed and is currently running on a community of 5 infrastructures within the framework of the European Project XIFI, to be extended to 12 more infrastructures.

A proposal for an Internet of Things-based monitoring system composed by low capability, open source and open hardware devices

The Internet of Things makes use of a huge disparity of technologies at very different levels that help one to the other to accomplish goals that were previously regarded as unthinkable in terms of ubiquity or scalability. If the Internet of Things is expected to interconnect every day devices or appliances and enable communications between them, a broad range of new services, applications and products can be foreseen. For example, monitoring is a process where sensors have widespread use for measuring environmental parameters (temperature, light, chemical agents, etc.) but obtaining readings at the exact physical point they want to be obtained from, or about the exact wanted parameter can be a clumsy, time-consuming task that is not easily adaptable to new requirements. In order to tackle this challenge, a proposal on a system used to monitor any conceivable environment, which additionally is able to monitor the status of its own components and heal some of the most usual issues of a Wireless Sensor Network, is presented here in detail, covering all the layers that give it shape in terms of devices, communications or services.

Multi-distributed wireless sensors for monitoring a long distance transport in a reefer container

The study of the temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport and for minimising losses. This work presents an analysis of the temperatures during the refrigerated transport of 4,320 kg of blueberries in a reefer (set point temperature at ?1ºC) on a container ship from Montevideo (Uruguay) to Verona (Italy). The monitoring was performed by using semi-passive RFID loggers (TurboTag cards). The objective was to carry out a multi-distributed supervision using low-cost, wireless and autonomous sensors for the characterisation of the distribution and spatial gradients of temperatures during a long distance transport. Data analysis shows spatial (phase space) and temporal sequencing diagrams and reveals a significant heterogeneity of temperature at different locations in the container, which highlights the ineffectiveness of a temperature control system based on a single sensor, as is usually done.

Prospectives of monitoring biological activity in a red-legged partridge incubator with a carbon dioxide probe

This study focuses on the relationship between CO2 production and the ultimate hatchability of the incubation. A total amount of 43316 eggs of red-legged partridge (Alectoris rufa) were supervised during five actual incubations: three in 2012 and two in 2013. The CO2 concentration inside the incubator was monitored over a 20-day period, showing sigmoidal growth from ambient level (428 ppm) up to 1700 ppm in the incubation with the highest hatchability. Two sigmoid growth models (logistic and Gompertz) were used to describe the CO2 production by the eggs, with the result that the logistic model was a slightly better fit (r2=0.976 compared to r2=0.9746 for Gompertz). A coefficient of determination of 0.997 between the final CO2 estimation (ppm) using the logistic model and hatchability (%) was found.