Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation (Preprint)

BACKGROUND The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. OBJECTIVE The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. METHODS We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. RESULTS Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. CONCLUSIONS Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.

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Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation

JMIR mhealth and uhealth ◽

10.2196/19874 ◽

2020 ◽

Vol 8 (10) ◽

pp. e19874

Author(s):

Gabriele Marini ◽

Benjamin Tag ◽

Jorge Goncalves ◽

Eduardo Velloso ◽

Raja Jurdak ◽

...

Keyword(s):

System Development ◽

Ground Truth ◽

Data Driven ◽

Clinical Settings ◽

Ground Truth Data ◽

Localization System ◽

Data Driven Approach ◽

Consistent Assessment ◽

Occupancy Rates ◽

New Strategies

Background The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. Objective The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. Methods We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. Results Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. Conclusions Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.

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WattScale

ACM/IMS Transactions on Data Science ◽

10.1145/3406961 ◽

2021 ◽

Vol 2 (1) ◽

pp. 1-25

Author(s):

Srinivasan Iyengar ◽

Stephen Lee ◽

David Irwin ◽

Prashant Shenoy ◽

Benjamin Weil

Keyword(s):

Cooling System ◽

Large Population ◽

Ground Truth ◽

Detection Algorithm ◽

Building Envelope ◽

Ground Truth Data ◽

Heating And Cooling ◽

Data Driven Approach ◽

Execution Mode ◽

The Individual

Buildings consume over 40% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this article, we present WattScale, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region. Unlike previous methods such as least-squares that use point estimates, WattScale uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the distribution of parameters that affect a building. Further, it compares them with similar homes in a given population. WattScale also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in various settings. WattScale has two execution modes—(i) individual and (ii) region-based, which we highlight using two case studies. For the individual execution mode, we present results from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults, respectively. For the region-based execution mode, we show that WattScale can be extended to millions of homes in the U.S. due to the recent availability of representative energy datasets.

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SHYBRID: A graphical tool for generating hybrid ground-truth spiking data for evaluating spike sorting performance

10.1101/734061 ◽

2019 ◽

Cited By ~ 4

Author(s):

Jasper Wouters ◽

Fabian Kloosterman ◽

Alexander Bertrand

Keyword(s):

Ground Truth ◽

Neural Recording ◽

Data Driven ◽

Sorting Algorithm ◽

Spike Sorting ◽

Recording Device ◽

Ground Truth Data ◽

Sorting Algorithms ◽

Graphical Tool ◽

Fine Tune

AbstractSpike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.

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Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

The Astrophysical Journal ◽

10.3847/1538-4357/ab6b1f ◽

2020 ◽

Vol 890 (2) ◽

pp. 103 ◽

Cited By ~ 5

Author(s):

Shin Toriumi ◽

Shinsuke Takasao ◽

Mark C. M. Cheung ◽

Chaowei Jiang ◽

Yang Guo ◽

...

Keyword(s):

Comparative Study ◽

Ground Truth ◽

Data Driven ◽

Flux Emergence ◽

Data Set ◽

Ground Truth Data ◽

Coronal Field ◽

Solar Coronal

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Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

Artificial Life ◽

10.1162/artl_a_00330 ◽

2021 ◽

Vol 26 (4) ◽

pp. 484-506

Author(s):

Toby Howison ◽

Simon Hauser ◽

Josie Hughes ◽

Fumiya Iida

Keyword(s):

Real World ◽

Large Scale ◽

Ground Truth ◽

Data Driven ◽

Target System ◽

Automated Assembly ◽

Soft Robots ◽

Ground Truth Data ◽

Modular Systems ◽

Model Free

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

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