A Framework for Generating Summaries from Temporal Personal Health Data

Although it has become easier for individuals to track their personal health data (e.g., heart rate, step count, and nutrient intake data), there is still a wide chasm between the collection of data and the generation of meaningful summaries to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work toward striving closer to their health goals. We aim to bridge the gap between data collection and summary generation by mining the data for interesting behavioral findings that may provide hints about a user’s tendencies. Our focus is on improving the explainability of temporal personal health data via a set of informative summary templates, or “protoforms.” These protoforms span both evaluation-based summaries that help users evaluate their health goals and pattern-based summaries that explain their implicit behaviors. In addition to individual-level summaries, the protoforms we use are also designed for population-level summaries. We apply our approach to generate summaries (both univariate and multivariate) from real user health data and show that the summaries our system generates are both interesting and useful.

Heart failure monitoring using the wearable cardioverter defibrillator in patients with newly diagnosed heart failure

Funding Acknowledgements Type of funding sources: None. Background The wearable cardioverter-defibrillator (WCD) can be prescribed in patients with newly diagnosed heart failure. The WCD provides additional heart failure parameters, like heart rate, step count and body position, accessible via remote monitoring. The purpose of this study was to evaluate clinical relevance of additionally recorded data in patients using the WCD. Methods Patients with newly diagnosed heart failure and WCD, an average wear time with at least 20 hours per day and available heart failure parameters were included. The heart failure parameters were provided in 5-minute data intervals. An approximate for the heart rate variability was calculated via the standard deviation of the cycle length of the given heart rate per 5-minute data interval (HRV5). Results 276 patients (68% male) were included between 04/2013 and 12/2017. Mean age was 57.4 ± 15.3 years. 174 patients (63%) suffered from non-ischemic and 102 patients (37%) from ischemic cardiomyopathy. Mean NYHA functional class at prescription was 2.6 ± 0.8. Mean left ventricular ejection fraction (LVEF) was 25.3 ± 8.5%. Mean wear time of the WCD was 111.8 ± 74.5 days. Recorded median heart rate using the WCD was 70.8 (IQR 63.1 - 78.7) beats per minute on the first wear day and 64.5 (IQR 59.7 - 71.3) on the last wear day. Median step count amounted to 4294 (IQR 2283 - 7092) steps on the first wear day compared to 5688 (IQR 3153 - 8263) steps on the last wear day. Median HRV5 was 85.4 (IQR 60.1 - 109.8) ms on the first wear day and 110.4 (IQR 78.6 - 134.9) ms on the last wear day. Between the first and last seven days of usage, median heart rate was significantly reduced (69.5 (IQR 62.0 - 76.8) to 65.9 (IQR 60.4 - 72.2) beats per minute; p < 0.001), while median step counts per day (4657 (IQR 2778 – 6918) to 5562 (IQR 3890 – 8446) steps; p < 0.001) and HRV5 (89.0 (IQR 64.8 - 110.7) to 111.0 (IQR 83.7 - 134.7) ms; p < 0.001) were significantly elevated. A higher delta of heart rate correlated with a higher delta of HRV5A (p < 0,001; rs = 0.488) between the first and last seven days of usage. A higher delta of step counts per day in the first and last seven days correlated with a higher HRV5 (p < 0.001; rs = 0.320). Patients with a higher delta of step count per day (p = 0,005; rs = 0,189) and patients with a higher delta of HRV5 (p = < 0.001; rs = 0.255) showed a higher delta of LVEF measured at prescription and three months follow-up. Conclusion The WCD provides heart failure monitoring using additional heart failure parameters. Patients with newly diagnosed heart failure show a significant difference in heart rate, step count per day and heart rate variability approximate between beginning and end of prescription time. Step count and heart rate variability correlate with LVEF reverse remodeling. Remote monitoring for parameters regarding heart failure might be helpful for close monitoring and further heart failure therapy optimization during WCD wearing.

Going, going, gone: competitive decision-making in Dutch auctions

In a Dutch auction, an item is offered for sale at a set maximum price. The price is then gradually lowered over a fixed interval of time until a bid is made, securing the item for the bidder at the current price. Bidders must trade-off between certainty and price: bid early to secure the item and you pay a premium; bid later at a lower price but risk losing to another bidder. These properties of Dutch auctions provide new opportunities to study competitive decision-making in a group setting. We developed a novel computerised Dutch auction platform and conducted a set of experiments manipulating volatility (fixed vs varied number of items for sale) and price reduction interval rate (step-rate). Triplets of participants ($$N=66$$ N = 66 ) competed with hypothetical funds against each other. We report null effects of step-rate and volatility on bidding behaviour. We developed a novel adaptation of prospect theory to account for group bidding behaviour by balancing certainty and subjective expected utility. We show the model is sensitive to variation in auction starting price and can predict the associated changes in group bid prices that were observed in our data.

Obtention of Nb47Ti and Ti13Nb13Zr Alloys Powders by Hydride-Dehydride Process for Additive Manufacturing Applications

In this work, the hydride-dehydride process (HDH) parameters to obtain Nb47Ti and Ti13Nb13Zr alloys powders were investigated, aiming the production of orthopedic implants by additive manufacturing (AM). Nb47Ti and Ti13Nb13Zr alloys were previously obtained by electron beam melting furnace (EBMF). During the hydriding step, the alloys were heated at two activation temperatures, 800 and 1000 °C, under constant hydrogen pressure (1.8 bar), for 40 min followed by a controlled cooling rate step (2 °C/min). The hydride alloys were milled in a ring-type mill, for milling times ranging from 2 to 6 min, until to achieve the required particle size range (between 15 and 45 μm). The dehydriding step of the alloys was carried out under high vacuum at 700 °C for times up to 5 h. The alloys treated under distinct HDH steps were characterized by X-ray diffraction, scanning electron microscopy, dynamic image analysis, inert gas fusion and gravimetry. The alloys hydrides (δTiHx phase) were detected in both investigated activation temperatures, with hydrogen (H) contents up to 3.04 and 3.62 wt.% for the Nb47Ti and Ti13Nb13Zr alloys, respectively. During the hydriding step it was also observed a significant increase of nitrogen (N) and oxygen (O) contents regarding he as-cast alloys. The Nb47Ti alloy showed a lower embrittlement degree than the Ti13Nb13Zr alloy, resulting in higher milling times to reach the required particle size distribution. The higher oxygen pick up was observed during the milling step. After the dehydriding step, the HDH powders showed H contents lower than 0.01 wt.%, beside a significant N decreasing. Particles with irregular (or angular) shapes were obtained. However, the particle size was in the required range.

Meperidine pharmacokinetics and effects on physiologic parameters and thermal threshold following intravenous administration of three doses to horses

Background Meperidine is a synthetic opioid that belongs to the phenylpiperidine class and is a weak mu receptor agonist. In horses there are a limited number of published studies describing the analgesic effects of systemically administered meperidine in horses. The objective of this study was to describe the pharmacokinetics, behavioral and physiologic effects and effect on thermal threshold of three doses of intravenously administered meperidine to horses. Eight University owned horses (four mares and four geldings, aged 3–8 years were studied using a randomized balanced 4-way cross-over design. Horses received a single intravenous dose of saline, 0.25, 0.5 and 1.0 mg/kg meperidine. Blood was collected before administration and at various time points until 96 hours post administration. Plasma and urine samples were analyzed for meperidine and normeperidine by liquid chromatography-mass spectrometry and plasma pharmacokinetics determined. Behavioral and physiologic data (continuous heart rate, step counts, packed cell volume, total plasma protein and gastrointestinal sounds) were collected at baseline through 6 hours post administration. The effect of meperidine administration on thermal nociception was determined and thermal excursion calculated. Results Meperidine was rapidly converted to the metabolite normeperidine. The volume of distribution at steady state and systemic clearance (mean ± SD) ranged from 0.829 ± 0.138–1.58 ± 0.280 L/kg and 18.0 ± 1.4–22.8 ± 3.60 mL/min/kg, respectively for 0.5–1.0 mg/kg doses. Adverse effects included increased dose-dependent central nervous excitation, heart rate and cutaneous reactions. Significant effects on thermal nociception were short lived (up to 45 minutes at 0.5 mg/kg and 15 minutes at 1.0 mg/kg). Conclusions Results of the current study do not support routine clinical use of IV meperidine at a dose of 1 mg/kg to horses. Administration of 0.5 mg/kg may provide short-term analgesia, however, the associated inconsistent and/or short-term adverse effects suggest that its use as a sole agent at this dose, at best, must be cautiously considered.

Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study

The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE > 15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.

Low-cost fitness and activity trackers for biometric authentication

Fitness and activity tracking devices acquire, process and store rich behavioural data that are consumed by the end-user to learn health insights. This rich data source also enables a secondary use of being part of a biometric authentication system. However, there are many open research challenges with the use of data generated by fitness and activity trackers as a biometric source. In this article, the challenge of using data acquired from low-cost devices is tackled. This includes investigating how to best partition the data to deduce repeatable behavioural traits, while maximizing the uniqueness between participant datasets. In this exploratory research, 3 months’ worth of data (heart rate, step count and sleep) for five participants is acquired and utilized in its raw form from low-cost devices. It is established that dividing the data into 14-h segments is deemed the most suitable based on measuring coefficients of variance. Several supervised machine learning algorithms are then applied where the performance is evaluated by six metrics to demonstrate the potential of employing this data source in biometric-based security systems.

Optimization of Material Removal Rate and Dimensional Errors in Subtractive Rapid Prototyping of Polycarbonate Material

None of the research found in the literature investigates and optimizes the subtractive rapid prototyping process parameters in order to fabricate polycarbonate material. This research is conducted to optimize the subtractive rapid prototyping process parameters of polycarbonate material in order to achieve the maximum material rate removal without exceeding the dimensional error value of 1 mm, similar to the result of additive manufacturing. The response surface methodology is implemented in this research to optimized three process parameters, which are feed rate, step-over, and depth of cut. Two responses investigated in this research are material rate removal and dimensional error. The response optimizer is used to optimize the responses. The result shows that optimum material removal rate and dimensional error that can be achieved is 2.494 mm3/s and 0.97 mm respectively. A confirmation test experiment has been conducted to verify the optimization result.

Peramalan Curah Hujan Dengan Pendekatan Adaptive Neuro Fuzzy Inference System

Analisa peramalan curah hujan yang mendekati kenyataan berdasarkan akurasi yang akurat sangat diperlukan dalam berbagai aktivitas kehidupan manusia. Paper ini bertujuan untuk menerapkan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam peramalan curah hujan di Kota Samarinda, Kalimantan Timur. Beberapa parameter ANFIS seperti MF (Fungsi Keanggotaan), Input MF type (Tipe Fungsi Keanggotaan), Learning Rate (Step Size), dan rasio data telah digunakan. Berdasarkan hasil percobaan akurasi peramalan yang diperoleh cukup akurat dengan nilai MSE adalah 0.063290962 untuk rasio data 3:2 dan adalah 0.020498283 rasio data 4:1. Hasil ini menunjukkan bahwa metode ANFIS dapat menjadi alternative metode dalam meramalkan curah hujan di Kota Samarinda, Kalimantan Timur.

Measurement of Drilling Fluid Rheology and Modeling of Thixotropic Behavior

Drilling fluids perform a number of important functions during a drilling operation, including that of lifting drilled cuttings to the surface and balancing formation pressures. Drilling fluids are usually designed to be structured fluids exhibiting shear thinning and yield stress behavior, and most drilling fluids also exhibit thixotropy. Accurate modeling of drilling fluid rheology is necessary for predicting friction pressure losses in the wellbore while circulating, the pump pressure needed to resume circulation after a static period, and how the fluid rheology evolves with time while in static or near-static conditions. Although modeling the flow of thixotropic fluids in realistic geometries is still a formidable future challenge to be solved, considerable insights can still be gained by studying the viscometric flows of such fluids. We report a detailed rheological characterization of a water-based drilling fluid and an invert emulsion oilbased drilling fluid. The micro structure responsible for thixotropy is different in these fluids which results in different thixotropic responses. Measurements are primarily focused at transient responses to step changes in shear rate, but cover also steady state flow curves and stress overshoots during start-up of flow. We analyze the shear rate step change measurements using a structural kinetics thixotropy model.