Fast human motion prediction for human-robot collaboration with wearable interfaces

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset

Highly Efficient Regression for Scalable Person Re-Identification

Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available. This work proposes a truly scalable solution to re-id by addressing both problems. Specifically, a Highly Efficient Regression (HER) model is formulated by embedding the Fisher's criterion to a ridge regression model for very fast re-id model learning with scalable memory/storage usage. Importantly, this new HER model supports faster than real-time incremental model updates therefore making real-time active learning feasible in re-id with human-in-the-loop. Extensive experiments show that such a simple and fast model not only outperforms notably the state-of-the-art re-id methods, but also is more scalable to large data with additional benefits to active learning for reducing human labelling effort in re-id deployment

THE EFFECT OF SUPERVISED FEATURE EXTRACTION TECHNIQUES ON THE FACIES CLASSIFICATION USING MACHINE LEARNING

The widely accepted supervised machine learning classification algorithms are used for the semi-automating of the feature extraction process. In the machine learning facies classification process, each wireline log is a feature in the feature space. Since features are important in classification decisions, using suitable features improves the performance of a classification algorithm. In this study, three feature sets are compared containing the original conventional features (well-logs), and the extracted features from the unsupervised PCA and supervised FDA methods, using two classifier algorithms, namely SVM and RF. The FDA showed an improvement in the performance of facies classifiers while PCA can even deteriorate the results. An F1 score of 0.61 averaged over the available 20 folds for the combination of FDA feature extractor and RF classifier is achieved. This represents a 5% improvement in the prediction accuracy, compared to the conventional use of wells information as features with an F1 score of 0.56. Moreover, the conventional method uses all seven well-logs while with the FDA we only use three features