Speech Emotion Recognition method using time-stretching in the Preprocessing Phase and Artificial Neural Network Classifiers

Govoreanu, Valentin Catalin; Neghina, Mihai

doi:10.1109/iccp51029.2020.9266265

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…), a front facing camera is used, whereas for the operator’s basic status detection (e.g., angry, happy, sad, neutral, etc.) the images from the front facing camera are used; depending on the experiment and required accuracy, additional data from sensors [ 47 , 48 ] (e.g., microphone embedded in the touchscreen) or biosensors worn by the user (e.g., GSR sensor, eye-tracking glasses, EMG, EEG etc.) can be used to estimate the user’s current state for the assembly instruction’s adaptation.…”

Section: Next Assembly Step Prediction Through Decision Trees and Ensemble Learningmentioning

confidence: 99%

Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.

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Section: Next Assembly Step Prediction Through Decision Trees and Ensemble Learningmentioning

confidence: 99%

Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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“…The assembly process can start with any of the components mentioned before, and it is not restricted to a single assembly recipe. More details about the prototype's concept and its subsystems and feature developments are detailed in [41][42][43]. Using the assembly system described above, data were collected from 68 participants (second-year BSc students) and 111 manufacturing workers.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Gellért

Sorostinean

Pirvu

2022

Sensors

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Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

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“…Compared to the operator's reaction speed, the system is orders of magnitude faste (real time), minimizing the wait time for the operator between the time the current assem bly task is finished and the time the next assembly task instruction is provided. More in formation about the concept of the prototype or other implementation details can be found in [70][71][72].…”

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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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Speech Emotion Recognition method using time-stretching in the Preprocessing Phase and Artificial Neural Network Classifiers

Cited by 7 publications

References 37 publications

Assembly Assistance System with Decision Trees and Ensemble Learning

Assembly Assistance System with Decision Trees and Ensemble Learning

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

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

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