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      Biometrics and Policing: A Protocol for Multichannel Sensor Data Collection and Exploratory Analysis of Contextualized Psychophysiological Response During Law Enforcement Operations

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          Abstract

          Background

          Stress experienced by law enforcement officers is often extreme and is in many ways unique among professions. Although past research on officer stress is informative, it is limited, and most studies measure stress using self-report questionnaires or observational studies that have limited generalizability. We know of no research studies that have attempted to track direct physiological stress responses in high fidelity, especially within an operational police setting. The outcome of this project will have an impact on both practitioners and policing researchers. To do so, we will establish a capacity to obtain complex, multisensor data; process complex datasets; and establish the methods needed to conduct idiopathic clinical trials on behavioral interventions in similar contexts.

          Objective

          The objective of this pilot study is to demonstrate the practicality and utility of wrist-worn biometric sensor-based research in a law enforcement agency.

          Methods

          We will use nonprobability convenience-based sampling to recruit 2-3 participants from the police department in Durham, North Carolina, USA.

          Results

          Data collection was conducted in 2016. We will analyze data in early 2017 and disseminate our results via peer reviewed publications in late 2017.

          Conclusions

          We developed the Biometrics & Policing Demonstration project to provide a proof of concept on collecting biometric data in a law enforcement setting. This effort will enable us to (1) address the regulatory approvals needed to collect data, including human participant considerations, (2) demonstrate the ability to use biometric tracking technology in a policing setting, (3) link biometric data to law enforcement data, and (4) explore project results for law enforcement policy and training.

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          Most cited references50

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          Recovery of inter-block information when block sizes are unequal

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            Small sample inference for fixed effects from restricted maximum likelihood.

            Restricted maximum likelihood (REML) is now well established as a method for estimating the parameters of the general Gaussian linear model with a structured covariance matrix, in particular for mixed linear models. Conventionally, estimates of precision and inference for fixed effects are based on their asymptotic distribution, which is known to be inadequate for some small-sample problems. In this paper, we present a scaled Wald statistic, together with an F approximation to its sampling distribution, that is shown to perform well in a range of small sample settings. The statistic uses an adjusted estimator of the covariance matrix that has reduced small sample bias. This approach has the advantage that it reproduces both the statistics and F distributions in those settings where the latter is exact, namely for Hotelling T2 type statistics and for analysis of variance F-ratios. The performance of the modified statistics is assessed through simulation studies of four different REML analyses and the methods are illustrated using three examples.
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              Detecting Stress During Real-World Driving Tasks Using Physiological Sensors

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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                March 2017
                17 March 2017
                : 6
                : 3
                : e44
                Affiliations
                [1] 1Digital Health & Clinical Informatics RTI International Research Triangle Park, NCUnited States
                [2] 2Policing Research Program RTI International Research Triangle Park, NCUnited States
                [3] 3Engineered Materials, Devices and Systems RTI International Research Triangle Park, NCUnited States
                [4] 4Behavioral & Urban Health Program RTI International Research Triangle Park, NCUnited States
                Author notes
                Corresponding Author: Robert D Furberg rfurberg@ 123456rti.org
                Author information
                http://orcid.org/0000-0002-3803-1879
                http://orcid.org/0000-0003-2040-1330
                http://orcid.org/0000-0001-9290-6165
                http://orcid.org/0000-0002-8121-1250
                http://orcid.org/0000-0002-0919-6906
                http://orcid.org/0000-0003-3584-9832
                http://orcid.org/0000-0002-9709-6808
                Article
                v6i3e44
                10.2196/resprot.7499
                5375974
                28314707
                95548b94-ab2d-4832-8bb3-8ca2cc6cb951
                ©Robert D Furberg, Travis Taniguchi, Brian Aagaard, Alexa M Ortiz, Meghan Hegarty-Craver, Kristin H Gilchrist, Ty A Ridenour. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 17.03.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 13 February 2017
                : 28 February 2017
                : 6 March 2017
                : 7 March 2017
                Categories
                Protocol
                Protocol

                psychophysiology,law enforcement,sensor, wearable,clinical trial,digital health

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