Learning temporal context for activity recognition

We investigate how incremental learning of long-term human activity patterns improves the accuracy of activity classification over time. Rather than trying to improve the classification methods themselves, we assume that they can take into account prior probabilities of activities occurring at a particular time. We use the classification results to build temporal models that can provide these priors to the classifiers. As our system gradually learns about typical patterns of human activities, the accuracy of activity classification improves, which results in even more accurate priors. Two datasets collected over several months containing hand-annotated activity in residential and office environments were chosen to evaluate the approach. Several types of temporal models were evaluated for each of these datasets. The results indicate that incremental learning of daily routines leads to a significant improvement in activity classification

Top-1 CORSMAL Challenge 2020 Submission: Filling Mass Estimation Using Multi-modal Observations of Human-robot Handovers

Human-robot object handover is a key skill for the future of human-robot collaboration. CORSMAL 2020 Challenge focuses on the perception part of this problem: the robot needs to estimate the filling mass of a container held by a human. Although there are powerful methods in image processing and audio processing individually, answering such a problem requires processing data from multiple sensors together. The appearance of the container, the sound of the filling, and the depth data provide essential information. We propose a multi-modal method to predict three key indicators of the filling mass: filling type, filling level, and container capacity. These indicators are then combined to estimate the filling mass of a container. Our method obtained Top-1 overall performance among all submissions to CORSMAL 2020 Challenge on both public and private subsets while showing no evidence of overfitting. Our source code is publicly available: https://github.com/v-iashin/CORSMALComment: Code: https://github.com/v-iashin/CORSMAL Docker: https://hub.docker.com/r/iashin/corsma

Automatic Detection of Human Interactions from RGB-D Data for Social Activity Classification

We present a system for temporal detection of social interactions. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications, it is important to be able to move to more realistic data. For this reason, the proposed approach temporally detects intervals where individual or social activity is occurring. Recognition of human activities is a key feature for analysing the human behaviour. In particular, recognition of social activities is useful to trigger human-robot interactions or to detect situations of potential danger. Based on that, this research has three goals: (1) define a new set of descriptors, which are able to characterise human interactions; (2) develop a computational model to segment temporal intervals with social interaction or individual behaviour; (3) provide a public dataset with RGB-D data with continuous stream of individual activities and social interactions. Results show that the proposed approach attained relevant performance with temporal segmentation of social activities

Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data

Social activity based on body motion is a key feature for non-verbal and physical behavior defined as function for communicative signal and social interaction between individuals. Social activity recognition is important to study human-human communication and also human-robot interaction. Based on that, this research has threefold goals: (1) recognition of social behavior (e.g. human-human interaction) using a probabilistic approach that merges spatio-temporal features from individual bodies and social features from the relationship between two individuals; (2) learn priors based on physical proximity between individuals during an interaction using proxemics theory to feed a probabilistic ensemble of activity classifiers; and (3) provide a public dataset with RGB-D data of social daily activities including risk situations useful to test approaches for assisted living, since this type of dataset is still missing. Results show that using the proposed approach designed to merge features with different semantics and proximity priors improves the classification performance in terms of precision, recall and accuracy when compared with other approaches that employ alternative strategies

Social Activity Recognition on Continuous RGB-D Video Sequences

Modern service robots are provided with one or more sensors, often including RGB-D cameras, to perceive objects and humans in the environment. This paper proposes a new system for the recognition of human social activities from a continuous stream of RGB-D data. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications it is important to be able to move to more realistic scenarios in which such activities are not manually selected. For this reason, it is useful to detect the time intervals when humans are performing social activities, the recognition of which can contribute to trigger human-robot interactions or to detect situations of potential danger. The main contributions of this research work include a novel system for the recognition of social activities from continuous RGB-D data, combining temporal segmentation and classification, as well as a model for learning the proximity-based priors of the social activities. A new public dataset with RGB-D videos of social and individual activities is also provided and used for evaluating the proposed solutions. The results show the good performance of the system in recognising social activities from continuous RGB-D data

Management of QT prolongation induced by anti-cancer drugs: Target therapy and old agents. Different algorithms for different drugs

The side effects of anticancer drugs still play a critical role in survival and quality of life. Although the recent progresses of cancer therapies have significantly improved the prognosis of oncologic patients, side effects of antineoplastic treatments are still responsible for the increased mortality of cancer survivors. Cardiovascular toxicity is the most dangerous adverse effect induced by anticancer therapies. A survey conducted by the National Health and Nutrition Examination, showed that 1807 cancer survivors followed up for seven years: 51% died of cancer and 33% of heart disease (Vejpongsa and Yeh, 2014). Moreover, the risk of cardiotoxicity persists even with the targeted therapy, the newer type of cancer treatment, due to the presence of on-target and off-target effects related to this new class of drugs. The potential cardiovascular toxicity of anticancer agents includes: QT prolongation, arrhythmias, myocardial ischemia, stroke, hypertension (HTN), thromboembolism, left ventricular dysfunction and heart failure (HF). Compared to other cardiovascular disorders, the interest in QT prolongation and its complications is fairly recent. However, oncologists have to deal with it and to evaluate the risk-benefit ratio before starting the treatment or during the same. Electrolyte abnormalities, low levels of serum potassium and several drugs may favour the acquired QT prolongation. Treatment of marked QT prolongation includes cardiac monitoring, caution in the use or suspension of cancer drugs and correction of electrolyte abnormalities (hypokalaemia, hypomagnesaemia, hypocalcaemia). Syndrome of QT prolongation can be associated with potentially fatal cardiac arrhythmias and its treatment consists of intravenous administration of magnesium sulphate and the use of electrical cardioversion

Thalamo-cortical network activity between migraine attacks. Insights from MRI-based microstructural and functional resting-state network correlation analysis

BACKGROUND: Resting state magnetic resonance imaging allows studying functionally interconnected brain networks. Here we were aimed to verify functional connectivity between brain networks at rest and its relationship with thalamic microstructure in migraine without aura (MO) patients between attacks. METHODS: Eighteen patients with untreated MO underwent 3 T MRI scans and were compared to a group of 19 healthy volunteers (HV). We used MRI to collect resting state data among two selected resting state networks, identified using group independent component (IC) analysis. Fractional anisotropy (FA) and mean diffusivity (MD) values of bilateral thalami were retrieved from a previous diffusion tensor imaging study on the same subjects and correlated with resting state ICs Z-scores. RESULTS: In comparison to HV, in MO we found significant reduced functional connectivity between the default mode network and the visuo-spatial system. Both HV and migraine patients selected ICs Z-scores correlated negatively with FA values of the thalamus bilaterally. CONCLUSIONS: The present results are the first evidence supporting the hypothesis that an abnormal resting within networks connectivity associated with significant differences in baseline thalamic microstructure could contribute to interictal migraine pathophysiology

MEASUREMENT OF MACRO-SCALE INDENTATION MODULUS USING THE PRIMARY HARDNESS STANDARD MACHINES AT INRIM

In this paper it is described the experimental procedure and the statistical method for the measurement of indentation modulus by using the primary hardness standard machine at INRIM, in the macro-scale range. Indentation modulus is calculated on the basis of Doerner-Nix linear model and from accurate measurements of indentation load, displacement, contact stiffness and Vickers hardness impression imaging. Load is provided by dead-weight masses and displacement is measured by a laser-interferometric system, perpendicular with respect to the Vickers pyramid vertex. The geometrical dimension of the Diamond Pyramid Hardness (DPH) impression is measured by means of a micro-mechanical system and optical microscopy imaging technique. Applied force and indentation depth are measured simultaneously, 16 Hz of sampling rate, and the resulting indentation curve is obtained. Preliminary tests are performed on metals and alloys samples. Considerations and comments on the accuracy of the proposed method and analysis are discussed

Integrating tools for an effective testing of connected and automated vehicles technologies

The development of connected and automated driving functions involves that the interaction of autonomous/ automated vehicles with the surrounding environment will increase. Accordingly, there is a necessity for an improvement in the usage of traditional tools of the automotive development process. This is a critical problem since the classic development process used in the automotive field uses a very simplified driver model and the traffic environment, while nowadays it should contemplate a realistic representation of these elements. To overcome this issue, the authors proposed an integrated simulation environment, based on the co-simulation of Matlab/Simulink environment with simulation of urban mobility, which allows for a realistic model of vehicle dynamic, control logics, driver behaviour and traffic conditions. Simulation tests have been performed to prove the reasoning for such a tool, and to show the capabilities of the instrument. By using the proposed platform, vehicles may be modelled with a higher level of details (with respect to microscopic simulators), while the autonomous/automated driving functions can be tested in realistic traffic scenarios where the features of the road traffic environment can be varied to verify in a realistic way the level of robustness of the on-board implemented functions

Entropy-based abnormal activity detection fusing RGB-D and domotic sensors

The automatic detection of anomalies in Active and Assisted Living (AAL) environments is important for monitoring the wellbeing and safety of the elderly at home. The integration of smart domotic sensors (e.g. presence detectors) with those ones equipping modern mobile robots (e.g. RGBD camera) provides new opportunities for addressing this challenge. In this paper, we propose a novel solution to combine local activity levels detected by a single RGBD camera with the global activity perceived by a network of domotic sensors. Our approach relies on a new method for computing such a global activity using various presence detectors, based on the concept of entropy from information theory. This entropy effectively shows how active a particular room or environment’s area is. The solution includes also a new application of Hybrid Markov Logic Networks (HMLNs) to merge different information sources for local and global anomaly detection. The system has been tested with RGBD data and a comprehensive domotic dataset containing data entries from 37 different domotic sensors (presence, temperature, light, energy consumption, door contact), which is made publicly available. The experimental results show the effectiveness of our approach and the potential for complex anomaly detection in AAL settings