3m Innovative Properties Company

Gemalto Sa

3m Electronic Monitoring Ltd

Attenti Electronic Monitoring Ltd

Kiran S Kanukurthy

Steven T Awiszus

Eric C Lobner

Michael G Wurm

Micayla A Johnson

Ronald David Jesme

Matthew J Blackford

Caroline M Ylitalo

Neal A Rakow

Jia Hu

Nathan J Anderson

Keith G Mattson

Robert J Quintero

Milo G Oien-Rochat

James E Nash

Benjamin A Johnson

Marc A Egeland

Charles P Jents

Kenneth J Gamberini

Neeraj Sharma

Kui Chen-Ho

Glenn E Casner

Gary E Dwyer

Thomas W Holmquist-Brown

John C Hulteen

Madeleine E Filloux

Britton G Billingsley

Suman K Patel

James W Howard

Travis L Potts

Richard L Rylander

James B Snyder

Tien Yi T H Whiting

Paul L Acito

Arthur Scheffler

Charles Allen Shaklee

Katja Hansen

James L C Werness, Jr

Michael Bahners

Robert Bialluch

Frank T Herfort

Andrzej F Rybacha

Zeljko Zupanc

Nadine E Brueckmann

Markus Gunther Wilfried Lierse

John Allen Wheatley

Kenneth A Epstein

Stephen A Johnson

Duane Douglas Fansler

Philip Edwin Watson

John C Schultz

John M Kruse

Michael E Hamerly

James P DiZio

Raj Rajagopal

Ann R Fornof

Justin M Johnson

Paul A Martinson

Matthew David Moore

Robert W Shannon

Robert D Lorentz

Scott E Brigham

Andrew H Tilstra

James D Brown

Saber Taghvaeeyan

Kandyce M Bohannon

Nicholas Andrew Asendorf

Onur Sinan Yordem

Emil R Eriksson

Matthew D Moore

Derek S Baker

Cameron J Fackler

Elliott H Berger

Marie D Manner

Lydia R Carlson

Anton P Backlund

Daniel E G Gullberg

Patric Anvegård

Steven R Digre

Susan R Johnson

Darcey L Holloway

Ted K Madison

Philip J Savoie

Pageen S Smith

Lauraine L Wells

Aspects of the present disclosure relate to a method of identifying a worker, a system that performs the method, or non-transitory computer-readable storage medium that performs the method. The method can include acquiring, by a computer having one or more processors, a video of at least part of a worker from a camera system. The method also includes identifying, with the computer, a first worker profile based on personal protective equipment of the worker in the video. The method includes identifying, with the computer, a second worker profile based on a worker movement characteristic of the worker in the video. The method also includes determining, with the computer, an identity of the worker within the video based on the correspondence between the first worker profile and the second worker profile. The method also includes performing at least one action in response to determining the identity of the worker.

Identification of workers using personal protective equipment and worker movement characteristic analysis

In some examples, a system includes an article of personal protective equipment (PPE) comprising one or more sensors, the one or more sensors configured to generate usage data that is indicative of an operation of the article of PPE; and at least one computing device comprising a memory and one or more computer processors that: receive the usage data that is indicative of the operation of the article of PPE; apply the usage data to a safety learning model that predicts a likelihood of an occurrence of a safety event associated with the article of PPE based at least in part on previously generated usage data that corresponds to the safety event; and perform, based at least in part on predicting the likelihood of the occurrence of the safety event, at least one operation.

Personal protective equipment system having analytics engine with integrated monitoring, alerting, and predictive safety event avoidance

In some examples, a system includes a plurality of articles of personal protected equipment (PPE) that are each assigned to a particular worker. The system may also include a data hub that detects an input that initiates a broadcast of diagnostic self-check messages; identifies, in response to the input, each article of PPE of the plurality of articles of PPE; broadcasts, based on identifying each article of PPE, the diagnostic self-check messages to the respective articles of PPE, wherein each article of PPE receives its respective self-check message at its communication component; in response to receiving a set of diagnostic acknowledgement messages from one or more of the plurality of articles of PPE that have performed a diagnostic self-check, determines whether the set of diagnostic acknowledge messages satisfy one or more self-check criteria; and performs one or more operations based on whether the self-check criteria are satisfied.

Self-check for personal protective equipment

In some examples, a system includes an article of personal protective equipment (PPE) having at least one sensor configured to generate a stream of usage data; and an analytical stream processing component comprising: a communication component that receives the stream of usage data; a memory configured to store at least a portion of the stream of usage data and at least one model for detecting a safety event signature, wherein the at least one model is trained based as least in part on a set of usage data generated by one or more other articles of PPE of a same type as the article of PPE; and one or more computer processors configured to: detect the safety event signature in the stream of usage data based on processing the stream of usage data with the model, and generate an output in response to detecting the safety event signature.

Personal protective equipment (PPE) with analytical stream processing for safety event detection

In some examples, a system includes: an article of personal protective equipment (PPE) that includes a communication component; a computing device communicatively coupled to the article of PPE, wherein the computing device: receives context data that is based on one or more of the article of PPE, a work environment for the article of PPE, or a worker assigned to the article of PPE; selects, based at least in part on the context data, a set of programmable safety rules that are contextually associated with the at least one article of PPE; sends the programmable safety rules to one or more of the article of PPE or a data hub communicatively coupled to the article of PPE; and wherein the programmable safety rules are configured at the article of PPE or the data hub to perform one or more operations based at least in part on PPE data.

Context-based programmable safety rules for personal protective equipment

In some examples, a system includes a head-mounted device, a visor that includes a reflective object that is substantially transparent in a visible light spectrum and embodied at a physical surface of the visor, at least one visor attachment assembly that couples the visor to the head-mounted device; at least one optical sensor; and at least one computing device communicatively coupled to the at least one optical sensor, the at least one computing device comprising a memory and one or more computer processors that: receive, from the optical sensor, an indication of light outside of a visible light spectrum that is reflected from the reflective object; determine, based at least in part on the light outside of a visible light spectrum that is reflected from the reflective object, a position of the visor; and perform, based at least in part on the position of the visor, at least one operation.

Pixel optical sensing of visibly transparent object utilizing reflective materials for personal protective equipment

In some examples, a system () includes a hearing protector (); at least one position sensor; at least one sound monitoring sensor; at least one computing device configured to: receive, from the at least one sound monitoring sensor and over a time duration, indications of sound levels to which a worker is exposed; determine, from the at least one position sensor and during the time duration, that the hearing protector is not positioned at one or more ears of the worker to attenuate the sound levels; and generate, in response to the determination that at least one of the sound levels satisfies an exposure threshold during the time duration and the hearing protector is not positioned at one or more ears of the worker to attenuate the sound levels, an indication for output.

Hearing protector with positional and sound monitoring sensors for proactive sound hazard avoidance

In general, techniques are described for a personal protective equipment (PPE) management system (PPEMS) that uses images of optical patterns embodied on articles of personal protective equipment (PPEs) to identify safety conditions that correspond to usage of the PPEs. In one example, an article of personal protective equipment (PPE) includes a first optical pattern embodied on a surface of the article of PPE; a second optical pattern embodied on the surface of the article of PPE, wherein a spatial relation between the first optical pattern and the second optical pattern is indicative of an operational status of the article of PPE.

Personal protective equipment management system using optical patterns for equipment and safety monitoring

In some examples, a system includes: a plurality of different articles of personal protection equipment (PPE) that are all controlled by a particular user, and a computing device. The computing device may include one or more computer processors configured to receive sets of data from each of the different articles of PPE, wherein each set of data is based at least in part on a type of each of the different articles of PPE; generate for display a user interface that contemporaneously includes a plurality of graphical elements that are based at least in part on at least two sets of data that correspond to at least two different articles of PPE of the plurality of different articles of PPE; and in response to receiving an indication of user input that corresponds to at least one of the plurality of graphical elements, perform at least one operation.

Remote interface for digital configuration and security of safety equipment

Growing community of inventors

Cottage Grove, MN, United States of America

Kiran S Kanukurthy