Multi-sensor characterization for an improved identification of polymers in WEEE recycling

Polymers represent around 25% of total waste from electronic and electric equipment. Any successful recycling process must ensure that polymer-specific functionalities are preserved, to avoid downcycling. This requires a precise characterization of particle compounds moving at high speeds on conveyor belts in processing plants. We present an investigation using imaging and point measurement spectral sensors on 23 polymers including ABS, PS, PC, PE-types, PP, PVC, PET-types, PMMA, and PTFE to assess their potential to perform under the operational conditions found in recycling facilities. The techniques applied include hyperspectral imaging sensors (HSI) to map reflectance in the visible to near infrared (VNIR), short-wave (SWIR) and mid-wave infrared (MWIR) as well as point Raman, FTIR and spectroradiometer instruments. We show that none of the sensors alone can identify all the compounds while meeting the industry operational requirements. HSI sensors successfully acquired simultaneous spatial and spectral information for certain polymer types. HSI, particularly the range between (1600-1900) nm, is suitable for specific identification of transparent and light-coloured (non-black) PC, PE-types, PP, PVC and PET-types plastics; HSI in the MWIR is able to resolve specific spectral features for certain PE-types, including black HDPE, and light-coloured ABS. Fast-acquisition Raman spectroscopy (down to 500 ms) enabled the identification of all polymers regardless their composition and presence of black pigments, however, it exhibited limited capacities in mapping applications. We therefore suggest a combination of both imaging and point measurements in a sequential design for enhanced robustness on industrial polymer identification.

Merging citizen science with epidemiology: design of a prospective feasibility study of health events and air pollution in Cologne, Germany

BACKGROUND

Citizen science as an approach to merge society and science is not a new paradigm. Yet it is not common in public health, epidemiology, or medical sciences. SMARAGD (Sensors for Measuring Aerosols and ReActive Gases to Deduce health effects) assesses air pollution at participants' homes or workplaces in Cologne, Germany, as feasibility study with a citizen science approach. Personal exposure to air pollutants is difficult to study, because the distribution of pollutants is heterogeneous, especially in urban areas. Targeted data collection allows to establish connections between air pollutant concentration and the health of the study population. Air pollution is among the most urgent health risks worldwide. Yet links of individualized pollution levels and respiratory infections remain to be validated, which also applies for the feasibility of the citizen science approach for epidemiological studies.

METHODS

We co-designed a prospective feasibility study with two groups of volunteers from Cologne, Germany. These citizen scientists and researchers determined that low-cost air-quality sensors (hereafter low-cost sensors) were to be mounted at participants' homes/workplaces to acquire stationary data. The advantage of deploying low-cost sensors is the achievable physical proximity to the participants providing health data. Recruitment started in March 2021 and is currently ongoing (as of 09/22). Sensor units specifically developed for this study using commercially available electronic sensor components will measure particulate matter and trace gases such as ozone, nitrogen oxides, and carbon monoxide. Health data are collected using the eResearch system "Prospective Management and Monitoring-App" (PIA). Due to the ongoing SARS-CoV-2 pandemic, we also focus on COVID-19 as respiratory infection.

DISCUSSION

Citizen science offers many benefits for science in general but also for epidemiological studies. It provides scientific information to society, enables scientific thinking in critical discourses, can counter anti-scientific ideologies, and takes into account the interests of society. However, it poses many challenges, as it requires extensive resources from researchers and society and can raise concerns regarding data protection and methodological challenges such as selection bias.

An autonomous microchip for real-time, label-free immune cell analysis

Characterization of cell populations and identification of distinct subtypes based on surface markers are needed in a variety of applications from basic research and clinical assays to cell manufacturing. Conventional immunophenotyping techniques such as flow cytometry or fluorescence microscopy require immunolabeling of cells, expensive and complex instrumentation, skilled operators, and are therefore incompatible with field deployment and automated cell manufacturing systems. In this work, we introduce an autonomous microchip that can electronically quantify the immunophenotypical composition of a cell suspension. Our microchip identifies different cell subtypes by capturing each in different microfluidic chambers functionalized against the markers of the target populations. All on-chip activity is electronically monitored by an integrated sensor network, which informs an algorithm determining subpopulation fractions from chip-wide immunocapture statistics in real time. Moreover, optimal operational conditions within the chip are enforced through a closed-loop feedback control on the sensor data and the cell flow speed, and hence, the antibody-antigen interaction time is maintained within its optimal range for selective immunocapture. We apply our microchip to analyze a mixture of unlabeled CD4⁺ and CD8⁺ T cell sub-populations and then validated the results against flow cytometry measurements. The demonstrated capability to quantitatively analyze immune cells with no labels has the potential to enable not only autonomous biochip-based immunoassays for remote testing but also cell manufacturing bioreactors with built-in, adaptive quality control.

High-density volatile organic compound monitoring network for identifying pollution sources

The elusive sources of air pollution have hampered effective control across all sectors, with long-term consequences for the greenhouse effect and human health. Multiple monitoring systems have been highly desired for locating the sources. However, when faced with extensive sources, diverse air environments and meteorological conditions, the low spatiotemporal resolution, poor reliability and high cost of existing monitors were significant obstacles to their applications. Extending our previous demonstration of sensitive and reliable electrochemical sensors, we here present a machine-learning-assisted sensor arrays for monitoring typical volatile organic compounds (VOCs), which shows the consistent response with gas chromatography-mass spectrometry in the actual air environment. As a proof-of-concept, a low-cost and high-resolution VOC network of 152 sets of monitors across ~55 km² of mixed-used land is established in southwest Beijing. Benefiting from the strong reliability, the pollution sources are revealed by the VOC network and supported by the joint mobile sampling of a vehicle-mounted gas chromatography-mass spectrometry system. With the sustained help of the network, the sources polluted by the local industrial facilities, traffic, and restaurants are effectively site-specific abatement by the local authorities and enterprises during the next half-year. Our findings open up a promising path toward more effective tracing of regional pollution sources, as well as accelerate the long-term transformation of industry and cities.

AI-enabled prediction of video game player performance using the data from heterogeneous sensors

The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated an attention mechanism improves the generalization of the network and provides a straightforward feature importance as well. The best model achieves Area Under the Receiver Operating Characteristic Curve (ROC AUC) score 0.73 in predicting whether a player will perform better or worse in the next 240 seconds based on in-game metrics. The prediction of the performance of a particular player is realized although their data are not utilized in the training set. The proposed solution has a number of promising applications for professional eSports teams and amateur players, such as a learning tool or performance monitoring system.

Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants

For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM_2.5 and 1040 pseudo networks for NO₂. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM_2.5, improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO₂ less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits for understanding population exposure.

A sensing network involving citizens for high spatio-temporal resolution monitoring of fugitive emissions from a petroleum pre-treatment plant

In this study an innovative sensing network consisting of eight photoionization detectors, meteorological sensors, a video camera and a telephonic system able to systematize the population complaints was developed for the monitoring of odor emissions. The development of monitoring approaches with high temporal and spatial resolution and actively involving citizens, is strategic in areas where relevant and also short-term emissive events frequently occur and the conventional approaches fail due to the high variability of fugitive emissions. Moreover, even if unpleasant odors are not necessarily direct triggers of health effects, they could be associated with the release of other harmful compounds. Monitoring approaches also involving citizens are thus strategic tools because odors annoyance perceived by population may be a potential health risk warning. Therefore, the developed sensing network was set up in Val d'Agri (Basilicata, Italy) where a petroleum pre-treatment plant (COVA) rises in a rural and inhabited area. The data collected during the monitoring campaign from the 16th February to the 30th July 2017, showed Total Volatile Organic Compounds (TVOCs) concentrations decreasing moving away from the plant and up to five times higher than levels registered in the closest municipality (Viggiano). Moreover, recurrent short-term critical events characterized by concentration values far above the average of the period and with maximum values ranging from 0.92 to 1.89 ppm, were registered in correspondence with high levels of benzene (up to 23.9 μg/m³) and anemometric conditions able to transport pollutants from COVA to each receptor site. The spatial and temporal distribution of TVOC concentrations proved to be affected by the distance from COVA, wind direction and industrial activities verified using video reportage and citizen claims. Therefore, the developed approach has proven to be a useful tool to credit people's perception of odors and also to quantify citizen exposure to VOCs during short-term events.

Three-dimensional acceleration of the body center of mass in people with transfemoral amputation: Identification of a minimal body segment network

BACKGROUND

The analysis of biomechanical parameters derived from the body center of mass (BCoM) 3D motion allows for the characterization of gait impairments in people with lower-limb amputation, assisting in their rehabilitation. In this context, magneto-inertial measurement units are promising as they allow to measure the motion of body segments, and therefore potentially of the BCoM, directly in the field. Finding a compromise between the accuracy of computed parameters and the number of required sensors is paramount to transfer this technology in clinical routine.

RESEARCH QUESTION

Is there a reduced subset of instrumented segments (BSN) allowing a reliable and accurate estimation of the 3D BCoM acceleration transfemoral amputees?

METHODS

The contribution of each body segment to the BCoM acceleration was quantified in terms of weight and similarity in ten people with transfemoral amputation. First, body segments and BCoM accelerations were obtained using an optoelectronic system and a full-body inertial model. Based on these findings, different scenarios were explored where the use of one sensor at pelvis/trunk level and of different networks of segment-mounted sensors for the BCoM acceleration estimation was simulated and assessed against force plate-based reference acceleration.

RESULTS

Trunk, pelvis and lower-limb segments are the main contributors to the BCoM acceleration in transfemoral amputees. The trunk and shanks BSN allows for an accurate estimation of the sagittal BCoM acceleration (Normalized RMSE ≤ 13.1 %, Pearson's correlations r ≥ 0.86), while five segments are necessary when the 3D BCoM acceleration is targeted (Normalized RMSE ≤ 13.2 %, Pearson's correlations r ≥ 0.91).

SIGNIFICANCE

A network of three-to-five segments (trunk and lower limbs) allows for an accurate estimation of 2D and 3D BCoM accelerations. The use of a single pelvis- or trunk-mounted sensor does not seem advisable. Future studies should be performed to confirm these results where inertial sensor measured accelerations are considered.

Distributed finite time cubature information filtering with unknown correlated measurement noises

This paper addresses the distributed state estimation problem for a class of discrete nonlinear system over sensor networks subject to unknown correlated measurement noises. Firstly, under the condition of network connectivity, a novel communication protocol is developed to ensure every sensor node can gather the information distributed throughout the network within finite communication time. Then a fully distributed estimator is designed by periodically fusing the local information and neighbor's information according to the covariance intersection fusion strategy. Theoretically, it is proved that the distributed estimator in each sensor node is stable with the exponentially bounded estimation error in mean square. Finally, some numerous simulations are performed to illustrate the practical effectiveness and superiority of the proposed state estimator.

TreeMMoSys: A low cost sensor network to measure wind-induced tree response

Severe storms caused the largest amount of damaged timber in European forests in the past 70 years. Storm damage occurs when wind loads exceed the failure limits of trees. A decisive factor in assessing storm damage is comprehensive knowledge of interactions between the aerial parts of trees and the high-impact airflow. This paper describes the inexpensive multiple sensor system TreeMMoSys that can measure aerial tree parts' wind-induced reactions, including branches and the stem. The output of TreeMMoSys includes acceleration and angular rate data converted to tilt angles in the post-processing. The system consists of a scalable number of light-weight tree response sensors and ground receivers that communicate through a WLAN network. The weatherproofed system is highly portable, reusable, and allows for an efficient monitoring and a maximization of the number of study trees. Due to the stable measurement performance and accuracy of TreeMMoSys, it can be deployed in the field for long-term monitoring of single tree reactions or neighboring trees' reactions to wind excitation.

Development of Temperature Monitoring System of Hospital Cold Storages Based on Wireless Network and its Database Management

Storing bio-specimens in adequate temperatures is an important task in hospitals. Usually an assigned employee records manually the temperatures of the hospital cold storages such as refrigerators and freezers that keep them at regular intervals. In this research, a low power wireless Bluetooth Low Energy network is applied where the central monitoring personal computer, receives the temperature data and stores in a database. The system consists of many beacons which are wirelessly sending the measured temperature data, and the central monitoring computer which allows the user to monitor that data. In the case of wireless signals getting blocked due to obstacles, repeaters called bridges send the data to the central computer forming a so-called scatter net. Once the data is received by the Bluetooth module connected to the monitoring computer, an application saves the data into a database. This web application forms a website where the users holding the authentication information can log in and monitor the temperature data in the form of tables and graphs. The same information can be viewed by a smartphone and a person in charge receives a warning SMS message. This system also provides a scheduled backup system where the database is automatically backed up periodically. The suggested system has the advantage of managing reagent records with reduced manpower whilst coping for emergency situations automatically.

Development of a real-time automated monitoring system for managing the hazardous environmental pollutants at the construction site

The management of noise, vibration, and dust, which are hazardous pollutants from construction sites, is essential to minimize the health damage of the nearby residents and the economic damage of construction companies due to pollutants from construction sites. For the effective management of hazardous pollutants, their emissions from construction sites must be identified immediately and accurately. Therefore, this study developed a real-time automated monitoring system named "MOnitoring for Noise, Vibration, and Dust (MONVID)" for comprehensively measuring the hazardous environmental pollutants and managing them in real-time. Toward this end, the optimal design of MONVID was planned and customized considering mobility, usability, and economy. Also, for the field application of the developed MONVID, its feasibility was verified by comparing its techno-economic performance with that of the conventional measurement system through experiments. Based on the results of the experiment and performance evaluation, it was concluded that MONVID is a feasible and economical construction pollutant measurement system with reliable technical performance and improved mobility and usability compared to the conventional measurement system. This study has significant contributions to the development of the first platform (including hardware, sensor network, and software) for the integrated real-time automated monitoring of the environmental performance of construction sites.

Online sensor validation in sensor networks for bioprocess monitoring using swarm intelligence

Sensor faults can impede the functionality of monitoring and control systems for bioprocesses. Hence, suitable systems need to be developed to validate the sensor readings prior to their use in monitoring and control systems. This study presents a novel approach for online validation of sensor readings. The basic idea is to compare the original sensor reading with predictions for this sensor reading based on the remaining sensor network's information. Deviations between original and predicted sensor readings are used to indicate sensor faults. Since especially batch processes show varying lengths and different phases (e.g., lag and exponential phase), prediction models that are dependent on process time are necessary. The binary particle swarm optimization algorithm is used to select the best prediction models for each time step. A regularization approach is utilized to avoid overfitting. Models with high complexity and prediction errors are penalized, resulting in optimal predictions for the sensor reading at each time step (5% mean relative prediction error). The sensor reliability is calculated by the Kullback-Leibler divergence between the distribution of model-based predictions and the distribution of a moving window of original sensor readings (moving window size = 10 readings). The developed system allows for the online detection of sensor faults. This is especially important when sensor data are used as input to soft sensors for critical quality attributes or the process control system. The proof-of-concept is exemplarily shown for a turbidity sensor that is used to monitor a Pichia pastoris-batch process.

Scaling code-multiplexed electrode networks for distributed Coulter detection in microfluidics

Microfluidic devices can discriminate particles based on their properties and map them into different locations on the device. For distributed detection of these particles, we have recently introduced a multiplexed sensing technique called Microfluidic CODES, which combines code division multiple access with Coulter sensing. Our technique relies on micromachined sensor geometries to produce distinct waveforms that can uniquely be linked to specific locations on the microfluidic device. In this work, we investigated the scaling of the code-multiplexed Coulter sensor network through theoretical and experimental analysis. As a model system, we designed and fabricated a microfluidic device integrated with a network of 10 code-multiplexed sensors, each of which was characterized and verified to produce a 31-bit orthogonal digital code. To predict the performance of the sensor network, we developed a mathematical model based on communications and coding theory, and calculated the error rate for our sensor network as a function of the network size and sample properties. We theoretically and experimentally demonstrated the effect of electrical impedance on the signal-to-noise ratio and developed an optimized device. We also introduced a computational approach that can process the sensor network data with minimal input from the user and demonstrated system-level operation by processing suspensions of cultured human cancer cells. Taken together, our results demonstrated the feasibility of deploying large-scale code-multiplexed electrode networks for distributed Coulter detection to realize integrated lab-on-a-chip devices.

Outlier Detection in Urban Air Quality Sensor Networks

Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. Recently installed low-cost urban sensors, however, are more prone to result in erroneous data than conventional monitors, e.g., leading to outliers. Commonly applied outlier detection methods are unsuitable for air pollutant measurements that have large spatial and temporal variations as occur in urban areas. We present a novel outlier detection method based upon a spatio-temporal classification, focusing on hourly NO₂ concentrations. We divide a full year's observations into 16 spatio-temporal classes, reflecting urban background vs. urban traffic stations, weekdays vs. weekends, and four periods per day. For each spatio-temporal class, we detect outliers using the mean and standard deviation of the normal distribution underlying the truncated normal distribution of the NO₂ observations. Applying this method to a low-cost air quality sensor network in the city of Eindhoven, the Netherlands, we found 0.1-0.5% of outliers. Outliers could reflect measurement errors or unusual high air pollution events. Additional evaluation using expert knowledge is needed to decide on treatment of the identified outliers. We conclude that our method is able to detect outliers while maintaining the spatio-temporal variability of air pollutant concentrations in urban areas.

Event-triggered Kalman-consensus filter for two-target tracking sensor networks

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol.

Analysis of the optimal sampling rate for state estimation in sensor networks with delays

When addressing the problem of state estimation in sensor networks, the effects of communications on estimator performance are often neglected. High accuracy requires a high sampling rate, but this leads to higher channel load and longer delays, which in turn worsens estimation performance. This paper studies the problem of determining the optimal sampling rate for state estimation in sensor networks from a theoretical perspective that takes into account traffic generation, a model of network behaviour and the effect of delays. Some theoretical results about Riccati and Lyapunov equations applied to sampled systems are derived, and a solution was obtained for the ideal case of perfect sensor information. This result is also interesting for non-ideal sensors, as in some cases it works as an upper bound of the optimisation solution.

Open source platform for collaborative construction of wearable sensor datasets for human motion analysis and an application for gait analysis

Nearly every practical improvement in modeling human motion is well founded in a properly designed collection of data or datasets. These datasets must be made publicly available for the community could validate and accept them. It is reasonable to concede that a collective, guided enterprise could serve to devise solid and substantial datasets, as a result of a collaborative effort, in the same sense as the open software community does. In this way datasets could be complemented, extended and expanded in size with, for example, more individuals, samples and human actions. For this to be possible some commitments must be made by the collaborators, being one of them sharing the same data acquisition platform. In this paper, we offer an affordable open source hardware and software platform based on inertial wearable sensors in a way that several groups could cooperate in the construction of datasets through common software suitable for collaboration. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118Hz. Also, a proof-of-concept dataset is provided comprising sampled data from 12 subjects suitable for gait analysis.

Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data

Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatiotemporal combinatorial nature of SCP. In this paper, we propose a two-stage method called Assembler. In the first stage, Assembler filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, Assembler generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that Assembler is effective, efficient, and scalable.

Distributed weighted least-squares estimation with fast convergence for large-scale systems

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods.

A consensus-based multi-agent approach for estimation in robust fault detection

This paper is devoted to distributed estimation in robust fault detection for sensor networks with networked-induced delays and packet dropouts by using a consensus-based multi-agent approach. Utilizing the information interaction and coordination among the neighboring networks based on multi-agent theory, we design novel and multiple agent-based robust fault detection filters (RFDFs) subject to only partial estimated and measured information. Asymptotically stable sufficient conditions for the innovative constructed filters are derived in the form of linear matrix inequality (LMI) and the threshold fit for each agent-based RFDF is determined. An illustrative example is given to demonstrate the effectiveness of the consensus-based multi-agent approach for the estimation in robust fault detection.

Pedestrian counting with grid-based binary sensors based on Monte Carlo method

ABSTRACT

In this paper, we propose a method for estimating the number of pedestrians walking in opposite directions, as in cases of a shopping street or a sidewalk in a downtown area. The proposed method utilizes a compound-eye sensor that is constructed by placing two binary sensors for the pedestrians' movement direction and multiple binary sensors for the vertical direction of the pedestrians' movement direction. A number of Monte Carlo simulations about the movement of pedestrians are conducted, and the output history of the compound-eye sensor is obtained in each simulation. The simulation scenario with a small difference of the output history of the compound-eye sensor is selected to estimate the number of pedestrians. Evaluation results show that in the field whose width is 8 [m] the relative error in the proposed method is the smallest by using 2×8 binary sensors.

Sensor grid resource management: model and implementation issues

This paper studies optimal sensor resource management in sensor grids. We formalize the problem using nonlinear optimization theory, which incorporates sensor resource constraint, energy, and expense budget. The paper also presents a pricing-based iterative algorithm for sensor management which balances the sensor user׳ QoS requirements to achieve a sensor system optimization based on the preference of the sensor service users. The paper discusses implementation issues of sensor management. Simulations reveal that the proposed sensor management algorithms can obtain better performance than a previous approach.

Smart home technology for telemedicine and emergency management

With the ageing population, mobility is an important issue and it deters the elderlies to visit health clinics on a regular basis. Individuals with disabilities also face the same obstacles for their out-of-home medical visits. In addition, people living in remote areas often do not get the needed health care attention unless they are willing to spend the time, effort and cost to travel. Advances in information and telecommunication technologies have made telemedicine possible. Using the latest sensor technologies, a person's vital data can be collected in a smart home environment. The bio-information can then be transferred wirelessly or via the Internet to medical databases and the healthcare professionals. Using the appropriate sensing apparatus at a smart home setting, patients, elderlies and people with disabilities can have their health signals and information examined on a real-time and archival basis. Recovery process can be charted on a regular basis. Remote emergency alerts can be intercepted and responded quickly. Health deterioration can be monitored closely enabling corrective actions. Medical practitioners can therefore provide the necessary health-related services to more people. This paper surveys and compiles the state-of-the-art smart home technologies and telemedicine systems.

Innovations in health care services: the CAALYX system

PURPOSE

This paper describes proposed health care services innovations, provided by a system called CAALYX (Complete Ambient Assisted Living eXperiment). CAALYX aimed to provide healthcare innovation by extending the state-of-the-art in tele-healthcare, by focusing on increasing the confidence of elderly people living autonomously, by building on the knowledge base of the most common disorders and respective characteristic vital sign changes for this age group.

METHODS

A review of the state-of-the-art on health care services was carried out. Then, extensive research was conducted on the particular needs of the elderly in relation to home health services that, if offered to them, could improve their day life by giving them greater confidence and autonomy. To achieve this, we addressed issues associated with the gathering of clinical data and interpretation of these data, as well as possibilities of automatically triggering appropriate clinical measures. Considering this initial work we started the identification of initiatives, ongoing works and technologies that could be used for the development of the system. After that, the implementation of CAALYX was done.

FINDINGS

The innovation in CAALYX system considers three main areas of contribution: (i) The Roaming Monitoring System that is used to collect information on the well-being of the elderly users; (ii) The Home Monitoring System that is aimed at helping the elders independently living at home being implemented by a device (a personal computer or a set top box) that supports the connection of sensors and video cameras that may be used for monitoring and for interaction with the elder; (iii) The Central Care Service and Monitoring System that is implemented by a Caretaker System where attention and care services are provided to elders, where actors as Caretakers, Doctors and Relatives are logically linked to elders. Innovations in each of these areas are presented here.

CONCLUSIONS

The ageing European society is placing an added burden on future generations, as the 'elderly-to-working-age-people' ratio is set to steadily increase in the future. Nowadays, quality of life and fitness allows for most older persons to have an active life well into their eighties. Furthermore, many older persons prefer to live in their own house and choose their own lifestyle. The CAALYX system can have a clear impact in increasing older persons' autonomy, by ensuring that they do not need to leave their preferred environment in order to be properly monitored and taken care of.