Abstract--The destruction of the crystal structure of kaolinite caused by mechanical forces was investigated by X-ray diffraction, thermal analysis, infrared spectroscopy, and specific surface area determination. Attention was also directed to the change of thermal reactions of milled kaolinite. Grinding experiments for 5 rain, 10 rain, and 1, 2, 4, 6, and 10 h were carried out in an AGO I planetary mill. After 1 h of grinding, the crystalline order of kaolin is destroyed; but the amorphization continues in the course of prolonged grinding. Grinding for 1 h produces a favorable state for forming mullite-type crystals after heating even at 1000*C.

The automated detection of stress is a central problem for ambient assisted living solutions. The paper presents the concepts and results of two studies targeted at stress detection with a low cost heart rate sensor, a chest belt. In the device validation study (n = 5), we compared heart rate data and other features from the belt to those measured by a gold standard device to assess the reliability of the sensor. With simple synchronization and data cleaning algorithm, we were able to select highly (>97%) correlated, low average error (2.2%) data segments of considerable length from the chest data for further processing. The protocol for the clinical study (n = 46) included a relax phase followed by a phase with provoked mental stress, 10 minutes each. We developed a simple method for the detection of the stress using only three time-domain features of the heart rate signal. The method produced accuracy of 74.6%, sensitivity of 75.0%, and specificity of 74.2%, which is impressive compared to the performance of two state-of-the-art methods run on the same data. Since the proposed method uses only time-domain features, it can be efficiently implemented on mobile devices.

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Introduction: Lifestyle assessment, especially nutrition counseling may have a great impact on the health state of everybody. The MenuGene expert system provides services for the logging and assessment of nutrition and physical activity. Aim: This paper focuses on how the harmony of a dietary log can be analyzed using harmony rules. Such an assessment can be used to assess a log as well as drive an evolutionary search process that constructs a personalized menu. Methods: Expert knowledge is formalized in two parts, sets of foods and dishes and rules that fire at a pre-defined pattern of sets. To tackle the large rule search space, a simple conflict resolution strategy is used.Results: We implemented several hierarchies of sets to support the definition of rules, and also an Android based lifestyle assistant application. We validated the completeness of the dietary database in a survey. Conclusion:The system may prove very useful for a real improvement of the quality of life and general health state especially for patients with chronic diseases like diabetes. We plan to conduct clinical trials to prove this early next year.

Background Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices. Methods Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed. Results The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. Conclusion The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

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