Identifying human intention during assembly operations using wearable motion capturing systems including eye focus

Manns, Martin; Tuli, Tadele Belay; Schreiber, Florian

doi:10.1016/j.procir.2021.11.155

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Hence in this context, it is important to estimate the duration of the human activities as proposed by [106], in which a framework prediction takes into account duration of the current and future actions. The paper [6] proposed a real-time simulation for human activity identification and prediction, based on the probability of the human body intersections with the bounding colliders defined in the working space. Wearable sensors tracked body movements during human activity.…”

Section: The Approaches Used In Hap/harmentioning

confidence: 99%

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Predicting Human Activity – State of the Art

Awong¹,

Zielińska²

2023

PAR

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Predicting human actions is a very actual research field. Artificial intelligence methods are commonly used here. They enable early recognition and classification of human activities. Such knowledge is extremely needed in the work on robots and other interactive systems that communicate and cooperate with people. This ensures early reactions of such devices and proper planning of their future actions. However, due to the complexity of human actions, predicting them is a difficult task. In this article, we review state-of-the-art methods and summarize recent advances in predicting human activity. We focus in particular on four approaches using machine learning methods, namely methods using: artificial neural networks, support vector machines, probabilistic models and decision trees. We discuss the advantages and disadvantages of these approaches, as well as current challenges related to predicting human activity. In addition, we describe the types of sensors and data sets commonly used in research on predicting and recognizing human actions. We analyze the quality of the methods used, based on the prediction accuracy reported in scientific articles. We describe the importance of the data type and the parameters of machine learning models. Finally, we summarize the latest research trends. The article is intended to help in choosing the right method of predicting human activity, along with an indication of the tools and resources necessary to effectively achieve this goal.

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Section: The Approaches Used In Hap/harmentioning

confidence: 99%

“…1). Nevertheless, both play a significant role in human-human interaction [5], human-object interaction [6], and human-machine interaction [7,8]. This research domain has highly contributed in many areas including sports [9], robotics [10], security [11], healthcare [12].…”

Section: Introductionmentioning

confidence: 99%

Predicting Human Activity – State of the Art

Awong¹,

Zielińska²

2023

PAR

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“…Recognition of human motion and prediction of the motion behavior in real-time simulation is key for human-machine collaborations, such as with robots in manual operations. For this in [25], on a shop floor, spatial region-based activity identification with real-time simulation (Unity3D) and wearable human motion captures is presented. For simple and less frequent maintenance tasks, Pratticò et al [26] presented a VR simulator developed in Unity, using HTC Vive Pro as the head-mounted display for the users.…”

Section: Related Work 21 Assembly Assistance Systemsmentioning

confidence: 99%

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

et al. 2022

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This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in some cases all the other models. Nevertheless, an assembly assistance system meant to support factory workers needs to embed multiple models to exhibit valuable predictive capabilities.

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“…In the realm of robotic automation, work monitoring technologies prove efficacious in tasks such as aggregating and analyzing human task data for robot training [8,9], or discerning human intentions to facilitate seamless human-robot collaboration [10,11]. However, to harness these technologies, unobtrusive methods for monitoring worker behavior during tasks are imperative.…”

Section: Introductionmentioning

confidence: 99%

Highly sensitive and easy-to-attach wearable sensor for measuring finger force based on curvature changes in an ellipse-shaped finger ring

Ozaki,

Ohta,

Fujiyoshi

2024

Smart Mater. Struct.

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Technologies aimed at digitizing worker actions to enhance human labor tasks and mitigate accidents and disabilities have garnered significant attention. This study focuses on monitoring the force exerted by the fingers and developing a wearable fingertip force sensor based on a simple elliptical ring structure in conjunction with a commercially available resistive bend sensor. Resembling a ring accessory, the sensor is easy to attach and detach, and exhibits high sensitivity, with a resistance change of approximately 9% for a fingertip load of 1N. Furthermore, to mitigate crosstalk during finger flexion, we propose a combined configuration employing this ring-shaped sensor alongside another sensor designed for measuring and rectifying finger flexion angles. Additionally, we introduce an empirically derived fitting function and a straightforward calibration procedure to extract the function’s parameters. The results indicate that the proposed system achieves an average RMS error of 0.53 N for force estimations of approximately 5 N, even amidst finger bending and postural changes.

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Identifying human intention during assembly operations using wearable motion capturing systems including eye focus

Cited by 9 publications

References 24 publications

Predicting Human Activity – State of the Art

Predicting Human Activity – State of the Art

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

Highly sensitive and easy-to-attach wearable sensor for measuring finger force based on curvature changes in an ellipse-shaped finger ring

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