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Content available remote Smart urban objects to enhance safe participation in major events for the elderly
IoT increasingly permeates the public area, e.g., in traffic control and public transport. We propose to equip conventional urban objects with IoT technology to transform them into Smart Urban Objects (SUO's). While there exists some research exploring the potentials, specific solutions to enhance safety for the elderly outdoors are still lacking. The elderly's safety is threatened due to declining physical conditions. As a consequence, the elderly may be excluded from outdoor activities such as participating in major events. Against this backdrop, we design SUOs for adaptive indications of urban hazards, barrierfree passages and for smart reservation of seats to enhance resting possibilities. We report on our solution using Bluetooth technology for remote sensing of older pedestrians serving as input for the objects' adaptive capacities. The SUOs have been installed for test purposes on a major event in a larger German city.
Content available remote Sleep-related breathing biomarkers as a predictor of vital functions
Because an average human spends one third of his life asleep, it is apparent that the quality of sleep has an important impact on the overall quality of life. To properly understand the influence of sleep, it is important to know how to detect its disorders such as snoring, wheezing, or sleep apnea. The aim of this study is to investigate the predictive capability of a dual-modality analysis scheme for methods of sleep-related breathing disorders (SRBDs) using biosignals captured during sleep. Two logistic regressions constructed using backward stepwise regression to minimize the Akaike information criterion were extensively considered. To evaluate classification correctness, receiver operating characteristic (ROC) curves were used. The proposed classification methodology was validated with constructed Random Forests methodology. Breathing sounds and electrocardiograms of 15 study subjects with different degrees of SRBD were captured and analyzed. Our results show that the proposed classification model based on selected parameters for both logistic regressions determine the different types of acoustic events during sleep. The ROC curve indicates that selected parameters can distinguish normal versus abnormal events during sleep with high sensitivity and specificity. The percentage of prediction for defined SRBDs is very high. The initial assumption was that the quality of result is growing with the number of parameters included in the model. The best recognition reached is more than 89% of good predictions. Thus, sleep monitoring of breath leads to the diagnosis of vital function disorders. The proposed methodology helps find a way of snoring rehabilitation, makes decisions concerning future treatment, and has an influence on the sleep quality.
As the contribution of specific parameters is not known and significant intersubject variability is expected, a decision system allowing adaptation for subject and environment conditions has to be designed to evaluate biomedical signal classification. A decision support system has to be trained in its desirable functionality prior to being used for patient monitoring evaluation. This paper describes a decision system based on data mining with Random Forests, allowing the adaptation for subject and environment conditions. This methodology may lead to specific system scoring by an artificial intelligence-supported patient monitoring evaluation system, which may help find a way of making decisions concerning future treatment and have influence on the quality of patients’ life.
The article presents the concept of hospital telemetric system. The goal of the project is a model of early warning systems for patients outside intensive care wards. Proposed system is based on constant telemetric monitoring using objective physiological parameters. Using low-distance sensor network which covers body of a patient, so-called BAN (Body Area Network) is the main innovation of the project. Some preliminary results of ECG analysis and interpretation modules and units of proposed system will be presented.
Sensors that perform the task of measuring the physical quantity of acceleration are discussed. Applications for such measurements and thus of accelerometers, range from early diagnosis procedures for tremor-related diseases (e.g. Parkinsons) to monitoring daily patterns of patient activity using telemetry systems. The system-level requirements in such applications are considered and two novel neural network transducer designs developed by the authors are presented which aim to satisfy such requirements. Both designs are based on a micromachined sensing element with capacitive signal pick-off. The first is an open-loop design utilising a direct inverse control strategy, whilst the second is a closed-loop design where electrostatic actuation is used as a form of feedback. Both transducers are nonlinearly compensated, capable of self-test and provide digital outputs.
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