An unsupervised behavioral modeling and alerting system based on passive sensing for elderly care

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort

HIGH PERFORMANCE THERMAL INTERFACE TECHNOLOGY OVERVIEW

An overview on recent developments in thermal interfaces is given with a focus on a novel thermal interface technology that allows the formation of 2-3 times thinner bondlines with strongly improved thermal properties at lower assembly pressures. This is achieved using nested hierarchical surface channels to control the particle stacking with highly particle-filled materials. Reliability testing with thermal cycling has also demonstrated a decrease in thermal resistance after extended times with longer overall lifetime compared to a flat interface

Extended tensor description to design non-uniform heat-removal in interlayer cooled chip stacks

Interlayer cooling is a heat removal concept that scales with the number of stacked tiers. Uniform fluid cavities result only in moderate heat removal performance. A substantial improvement could be expected for nonuniform, hot-spot-aware fluid cavities. Hence, we propose an extension of our multi-scale modeling framework to support nonuniform fluid cavity designs. The chip stack with its cavities and the silicon dies are represented by field-coupled porous and solid domains, respectively. Detailed sub-domain modeling using two pairs of periodic boundary conditions for fully and half populated pin-fin arrays with 100 m height and pitch was performed. Permeability and convective thermal resistance values with respect to arbitrary flow directions were extracted. These values are used in the chip stack model to predict the mass and the energy transport within the fluid cavity and between the domains, respectively. Three mathematical permeability descriptions are benchmarke d against each other and are experimentaly validated. The extended tensor description predicts the mass flow and maximum junction temperature best at an accuracy of better than 20%. We could also demonstrate the extension of interlayer cooling to TSV pitches of 50 m with hot-spot heat fluxes of up to 250W/cm 2 by pin-fin-density modulation and four-port fluid delivery