The CLARITY modular ambient health and wellness measurement platform

Emerging healthcare applications can benefit enormously from recent advances in pervasive technology and computing. This paper introduces the CLARITY Modular Ambient Health and Wellness Measurement Platform:, which is a heterogeneous and robust pervasive healthcare solution currently under development at the CLARITY Center for Sensor Web Technologies. This intelligent and context-aware platform comprises the Tyndall Wireless Sensor Network prototyping system, augmented with an agent-based middleware and frontend computing architecture. The key contribution of this work is to highlight how interoperability, expandability, reusability and robustness can be manifested in the modular design of the constituent nodes and the inherently distributed nature of the controlling software architecture.Emerging healthcare applications can benefit enormously from recent advances in pervasive technology and computing. This paper introduces the CLARITY Modular Ambient Health and Wellness Measurement Platform:, which is a heterogeneous and robust pervasive healthcare solution currently under development at the CLARITY Center for Sensor Web Technologies. This intelligent and context-aware platform comprises the Tyndall Wireless Sensor Network prototyping system, augmented with an agent-based middleware and frontend computing architecture. The key contribution of this work is to highlight how interoperability, expandability, reusability and robustness can be manifested in the modular design of the constituent nodes and the inherently distributed nature of the controlling software architecture

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000

Frequency based Classification of Activities using Accelerometer Data

This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.Comment: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 200

Multi Medical Data Visualizations for Maintaining Wireless Body Networks (WBNs) Capability

The current progress on the study of wireless body networks (WBNs) has gained tremendous achievements to be applied in various modern e-Health technologies. The manuscript examines a typical WBN configuration incorporated with the corresponding multi-medical data visualization window to maintain the patient medical data recording. Several numbers of the basic time-series data visualization approaches are adopted. The WBN was constructed to operate at 2.4 GHz ISM unlicensed band. The utilization of the designed WBN allows the physician or authorized health officers to perform routine health checks, anytime and anywhere, to a patient who is in the intensive health care status. The e-Health monitoring network is very suitable to place in the ICU/emergency rooms or it can be used by patients who are under regular treatment from a remote location. One unit WBN can be connected to the multi medical sensors of the different functions such as an ECG sensor to measure heart rate; a pulse sensor to measure the blood pressure; a temperature LM35DZ sensor to collect the human body temperature; a respiration sensor to measure the patient's breath and a video camera to observe the patient physical condition. An extensive evaluation of the designed WBN will be discussed more detailed later

A pulse sensor interface design for FPGA based multisensor health monitoring platform

The FPGA-based platform is critical for producing an inexpensive early validation platform design. In past years, sensor nodes based on the FPGA platform have been proposed to be IoT low-end devices. In this study, we present the FPGA based IoT low-end reconfigurable pulse sensor interface design that can be integrated with a multi-sensor healthcare platform to monitor a human pulse vital sign and be able to distinguish between user normal, Bradycardia, or Tachycardia heart rate. The pulse sensor interface is implemented by VHDL programming and FPGA technology. The designed pulse sensor peripheral interface is reliable and reconfigurable. It can collect vital body signs with the accuracy of a 15 nanoseconds period. The peripheral in FPGAs embedded system has been tested by placing the biosensor on the user’s fingertips. The BPM can be updated every 15 seconds

Faults Affecting Energy-Harvesting Circuits of Self-Powered Wireless Sensors and Their Possible Concurrent Detection

We analyze the effects of faults on an energy-harvesting circuit (EHC) providing power to a wireless biomedical multisensor node. We show that such faults may prevent the EHC from producing the power supply voltage level required by the multisensor node. Then, we propose a low-cost (in terms of power consumption and area overhead) additional circuit monitoring the voltage level produced by the EHC continuously, and concurrently with the normal operation of the device. Such a monitor gives an error indication if the generated voltage falls below the minimum value required by the sensor node to operate correctly, thus allowing the activation of proper recovery actions to guarantee system fault tolerance. The proposed monitor is self-checking with regard to the internal faults that can occur during its in-field operation, thus providing an error signal when affected by faults itself

Human activity recognition using multisensor data fusion based on Reservoir Computing

Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploiting both the data sampled by sensors embedded in a smartphone carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded sensors on the smartphone and environmental sensors before processing the RSS stream. To this end, we model the RSS stream, obtained from a Wireless Sensor Network (WSN), using Recurrent Neural Networks (RNNs) implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing (RC) paradigm. We targeted the system for the EvAAL scenario, an international competition that aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living (AAL) solutions. In this paper, the performance of the proposed activity recognition system is assessed on a purposely collected real-world dataset, taking also into account a competitive neural network approach for performance comparison. Our results show that, with an appropriate configuration of the information fusion chain, the proposed system reaches a very good accuracy with a low deployment cost

Wireless Sensor Network for Forest Fire Detection

 Forest fires are one of problems that threaten sustainability of the forest. Early prevention system for indications of forest fires is absolutely necessary. The extent of the forest to be one of the problems encountered in the forest condition monitoring. To overcome the problems of forest extent, designed a system of forest fire detection system by adopting the Wireless Sensor Network (WSN) using multiple sensor nodes. Each sensor node has a microcontroller, transmitter/receiver and three sensors. Measurement method is performed by measuring the temperature, flame, the levels of methane, hydrocarbons, and CO2 in some forest area and the combustion of peat in a simulator. From results of measurements of temperature, levels of methane, a hydrocarbon gas and CO2 in an open area indicates there are no signs of fires due to the value of the temperature, methane, hydrocarbon gas, and CO2 is below the measurement in the space simulator

Fusing actigraphy signals for outpatient monitoring

[EN] Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.This work was partially funded by the European Commission: Help4Mood (Contract No. FP7-ICT-2009-4: 248765). E. FusterGarcia acknowledges Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-12-05693).Fuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015). Fusing actigraphy signals for outpatient monitoring. Information Fusion. 23:69-80. https://doi.org/10.1016/j.inffus.2014.08.003S69802