Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner, Peter; Smith, Daniel; Rana, Mashud; Kapoor, Reena; Tartaglia, Elena; Schutt, Andreas; Rahman, Ashfaqur; Taylor, John; Dunstall, Simon

doi:10.48550/arxiv.2210.01344

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…The extraction and utilisation of knowledge from spatiotemporal tracking data can provide meaningful solutions to decision makers for optimising the production processes in manufacturing and developing domain specific applications [18]. Szabo et al [19] utilised indoor positioning data collected based on Real Time Location System (RTLS) from an automotive company to identify the bottlenecks different production zones.…”

Section: Studies Utilising Indoor Positioning Datamentioning

confidence: 99%

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Rahman

Rana

Baumgartner

2023

Preprint

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Indoor positioning systems provide a platform to track multiple objects concurrently in an indoor environment. The primary purpose of these systems is to locate objects. However, the tracks generated by moving objects leave characteristic footprints and can be used to infer valuable information for informed decision making. In this paper, we demonstrate how insightful information can be inferred from indoor tracking data of workers in a manufacturing assembly line setup. We utilise two indoor tracking data sets: (i) a real world (and publicly) available data set which contains the tracking data of multiple workers working together on a tricycle assembly line, and (ii) a synthetic data set of a computer assembly line. We aim to develop a set of data analytics methods to infer several Key Performance Indicators (KPIs) or metrices from the tracking data of the workers that include: resource utilization, worker interaction, deviation from ideal workflow and state of the assembly line. While the methods are developed for analysing data from tricycle and synthetic computer assembly line, they can be generalised to any manufacturing assembly line.

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Section: Studies Utilising Indoor Positioning Datamentioning

confidence: 99%

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Rahman

Rana

Baumgartner

2023

Preprint

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“…Moving objects leave characteristic signatures in their spatial trajectories that can be used to understand their activity or behaviour (Baumgartner et al 2022). Such insights can be helpful in many applications: surveillance at airports, understanding worker e ciency in assembly lines and detecting illegal shing activities in oceans.…”

Section: Introductionmentioning

confidence: 99%

Indoor Movement Data Analytics based on Machine Learning for Manufacturing Assembly Line

Rana

Smith

Rahman

et al. 2023

Preprint

Self Cite

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The paper presents an analysis on how machine learning models can be used to extract information from indoor movement data in an assembly line. An Indoor Positioning System (IPS) is commonly used to track workers, moving objects and vehicles at indoor venues where GPS is not effective. Movements happen within an assembly line as certain sets of work activities are performed (e.g., delivering assembled units from one work bench to another, reworking at a work bench and transporting a trolley from one place to another) and anything beyond that can be considered as anomalous. A computer assembly line was simulated and the movement trajectories of five workers were generated to perform the following activities: working at their own station, moving between different workstations, helping at another workstation, working at storage, and moving to a storage zone. We then developed a set of machine learning models to investigate how accurately these behaviours can be detected. The machine learning pipeline for indoor tracking data involves: (a) segmenting worker trajectories using Self–Supervised Learning (SSL). SSL is a machine learning process where a model is trained to learn one part of the input data from another part without the use of manually acquired labels. SSL is used to find the change points of the trajectories (i.e., used as segment boundaries) by training a model to predict the future interval of a trajectory from its temporally adjacent past window. (b) classifying trajectory segments (i.e., sub-trajectories) into different categories of worker behaviour. We investigated different supervised machine learning models to learn from the labelled segments. (c) estimating the state of the assembly floor based on the inferred worker behaviour. The state provides a wholistic view of the factory floor including the collective behaviour of workers. We used a clustering approach to identify the dominant states (joint activity) of the factory floor. We received some promising results from our analysis. We present the methods and results in detail in the paper.

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“…Moving objects leave characteristic signatures in their spatial trajectories that can be used to understand their activity or behaviour (Baumgartner et al 2022). Such insights can be helpful in many applications: surveillance at airports, understanding worker efficiency in assembly lines and detecting illegal fishing activities in oceans.…”

Section: Introductionmentioning

confidence: 99%

Indoor movement data analytics based on machine learning for manufacturing assembly line

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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The paper presents an analysis on how machine learning models can be used to extract information from indoor movement data in an assembly line. An Indoor Positioning System (IPS) is commonly used to track workers, moving objects and vehicles at indoor venues where GPS is not effective. Movements happen within an assembly line as certain sets of work activities are performed (e.g., delivering assembled units from one work bench to another, reworking at a work bench and transporting a trolley from one place to another) and anything beyond that can be considered as anomalous.A computer assembly line was simulated and the movement trajectories of five workers were generated to perform the following activities: working at their own station, moving between different workstations, helping at another work station, working at storage and moving to a storage zone. We then developed a set of machine learning models to investigate how accurately these behaviours can be detected.The machine learning pipeline for indoor tracking data involves:(a) segmenting worker trajectories using Self-Supervised Learning (SSL). SSL is a machine learning process where a model is trained to learn one part of the input data from another part without the use of manually acquired labels. SSL is used to find the change points of the trajectories (i.e., used as segment boundaries) by training a model to predict the future interval of a trajectory from its temporally adjacent past window.(b) classifying trajectory segments (i.e., sub-trajectories) into different categories of worker behaviour. We investigated different supervised machine learning models to learn from the labelled segments.(c) estimating the state of the assembly floor based on the inferred worker behaviour. The state provides a wholistic view of the factory floor including the collective behaviour of workers. We used a clustering approach to identify the dominant states (joint activity) of the factory floor.We received some promising results from our analysis. We present the methods and results in detail in the paper.

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Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Cited by 4 publications

References 35 publications

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Indoor Movement Data Analytics based on Machine Learning for Manufacturing Assembly Line

Indoor movement data analytics based on machine learning for manufacturing assembly line

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