Modeling and mobile home monitoring of behavioral and psychological symptoms of dementia (BPSD).

With the increasing global aging population, dementia care has rapidly become a major social problem. Current diagnosis of Behavior and Psychological Symptoms of Dementia (BPSD) relies on clinical interviews, and behavioral rating scales based on a period of behavior observation, but these methods are not suitable for identification of occurrence of BPSD in the daily living, which is necessary for providing appropriate interventions for dementia, though, has been studied by few research groups in the literature. To address these issues, in this study developed a BPSD monitoring system consisting of a Psycho-Cognitive (PsyCo) BPSD model, a Behavior-Physio-Environment (BePhyEn) BPSD model, and an implementation platform. The PsyCo BPSD model provides BPSD assessment support to caregivers and care providers, while the BePhyEn BPSD model provides instantaneous alerts for BPSD enabled by a 24-hour home monitoring platform for early intervention, and thereby alleviation of burden to patients and caregivers. Data for acquiring the models were generated through extensive literature review and regularity determined. A mobile robot was utilized as the implementation platform for improving sensitivity of sensors for home monitoring, and elderly individual following algorithms were investigated. Experiments in a virtual home environment showed that, a virtual BPSD elderly individual can be followed safely by the robot, and BPSD occurrence could be identified accurately, demonstrating the possibility of modeling and identification of BPSD in home environment.

Solving variability: Accurately extracting feature components from ballistocardiograms.

Objective: A ballistocardiogram (BCG) is a vibration signal generated by the ejection of the blood in each cardiac cycle. The BCG has significant variability in amplitude, temporal aspects, and the deficiency of waveform components, attributed to individual differences, instantaneous heart rate, and the posture of the person being measured. This variability may make methods of extracting J-waves, the most distinct components of BCG less generalizable so that the J-waves could not be precisely localized, and further analysis is difficult. This study is dedicated to solving the variability of BCG to achieve accurate feature extraction. Methods: Inspired by the generation mechanism of the BCG, we proposed an original method based on a profile of second-order derivative of BCG waveform (2ndD-P) to capture the nature of vibration and solve the variability, thereby accurately localizing the components especially when the J-wave is not prominent. Results: In this study, 51 recordings of resting state and 11 recordings of high-heart-rate from 24 participants were used to validate the algorithm. Each recording lasts about 3 min. For resting state data, the sensitivity and positive predictivity of proposed method are: 98.29% and 98.64%, respectively. For high-heart-rate data, the proposed method achieved a performance comparable to those of low-heart-rate: 97.14% and 99.01% for sensitivity and positive predictivity, respectively. Conclusion: Our proposed method can detect the peaks of the J-wave more accurately than conventional extraction methods, under the presence of different types of variability. Higher performance was achieved for BCG with non-prominent J-waves, in both low- and high-heart-rate cases.

Multivariate and multi-interface insights into carbon and energy recovery and conversion characteristics of hydrothermal carbonization of biomass waste from duck farm.

High lipid, high nitrogen duck manure (DM) with high lipid, high lignocellulosic litter materials (LM) are the main wet biomass wastes from duck farms and both are naturally abundant carbon resources. The synthesis of duck farming biomass waste into carbon-rich materials for high value utilization by hydrothermal carbonization (HTC), which can directly treat wet biomass, has not been investigated. In this study, the physicochemical properties of hydrochar derived from co-HTC of DM and LM and its carbon and energy recovery patterns were systematically investigated under multivariate conditions of raw materials ratios, solids contents, temperatures and residence times. The application of synchrotron-based near-edge X-ray adsorption fine structure technique (C K-edge NEXAFS) combined with gas chromatography-mass spectrometry (GC-MS) to the hydrochar and hydrothermal liquid, respectively. At multiple interfaces provided an in-depth analysis of the important material transformations of the co-HTC process and the structure of the hydrochar. Extending residence time (180 min) and increasing LM ratio (M@4%) in co-HTC reaction of DM and LM is beneficial to achieve hydrochar containing higher carbon content (44.84%) at lower reaction temperatures (180 °C). The heating value (HHV) of the hydrochar ranges between 17.12 and 25.05 MJ/kg. The carbon recovery rate of the co-HTC of DM and LM all exceeded 55% and was more closely related to the carbon content of the hydrochar than to its yield. Additionally, the model ERR=0.97±0.01CRR+2.40±0.71 (R2 = 0.99, P < 0.01) was developed to predict energy recovery rate (ERR) based on carbon recovery rate (CRR). Esters were an important intermediate during co-HTC of DM and LM, and the derived hydrochar consisted of a wide range of polycyclic aromatic hydrocarbons, alkanes and N-aromatic heterocycles as well as polyfuran, pyrrole and pyridine structures.