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Human activity recognition is attracting increasing research attention. Many activity recognition datasets have been created to support the development and evaluation of new algorithms. Given the lack of datasets collected in real environments (In The Wild) to support human activity recognition in public spaces, we introduce a large-scale video dataset for activity recognition In The Wild: POLIMI-ITW-S. The fully labeled dataset consists of 22,161 RGB video clips (about 46 h) including 37 activity classes performed by 50 K+ subjects in real shopping malls. We evaluated the state-of-the-art models on this dataset and get relatively low accuracy. We release the dataset including the annotations composed by person tracking bounding boxes, 2-D skeleton, and activity labels for research use at: https://airlab.deib.polimi.it/polimi-itw-s-a-shopping-mall-dataset-in-the-wild.

In this article, a data generated from impacts of objects with different shapes, masses, and impact velocities on a developed dummy head. The mass considered was in the range of 0.3–0.5 kg while the shapes considered were cube, wedge, and cylinder. The impact velocities levels were in the range of 1–3 m/s. A total of 144 experiments were conducted and the corresponding videos and raw data were analyzed for impact velocity, peak head linear acceleration, 3 ms criterion, and the Head Injury Criterion (HIC). This dataset includes the raw acceleration data and a summary of the overall processed data. The data is available on Harvard Dataverse: https://doi.org/10.7910/DVN/AVC8GG.

The data is related to unwanted interactions between a person and a small robotic toy based on acceleration sensor embedded within the robotic toy. Three toys were considered namely, a stuffed panda, a stuffed robot, and an excavator. Each toy was embedded with an accelerometer to record the interactions. Five different unwanted interactions were performed by adult participants and children. The considered interactions were hit, shake, throw, pickup, drop, and idle for the no interaction case. The collected data contains the magnitude of the resultant acceleration from the interactions. The data was processed by extracting the instances of interactions. A secondary dataset was created from the original one by creating artificial sequences. This data article contains the processed data that can be used to explore different machine learning models and techniques in classifying such interactions. Online repository contains the files: https://doi.org/10.7910/DVN/FHOO0Q.

Anthropomorphic test dummies are commonly used to evaluate the potential harm to humans due to dangerous scenarios, such as that due to car accidents. Furthermore, they have been used in sports to evaluate the efficacy of protective gears in mitigating harm due to impacts. Recently, they have been considered in industrial and collaborative robotics to assess risks due interactions between a human and a robot. In this article, we describe the development of a low-cost dummy head impact rig. The motivation behind this project is to quantify the potential harm to a child’s head due to impacts with a small robotic toy. Three severity indices can be estimated, namely, Head Acceleration Criterion (HIC), 3 ms criterion, and peak head acceleration. Furthermore, the artificial skin of the dummy head can be used to assess the potential for tissue injuries. 3D-printed parts were used to develop the head. A tri-axial accelerometer embedded inside the head was used to measure the changes in accelerations. The developed head was placed inside a dedicated experimental bench. A data acquisition card that is connected to a computer system was used to acquire the raw data and then store it. A script was used to postprocess the stored data for the three severity indices. A video camera recording in slow-motion was used to record the impacts. The calculation of the impact velocities was based on the analysis of the video recordings using an open-source software. The developed experimental setup was validated by producing comparable results to that of relevant previous studies. Keywords: Head safety, Severity indices, Children safety, Small robots, Test rig

The purpose of this data is to investigate the effect of different thicknesses of different soft materials samples added to an object on the resultant head acceleration of a developed dummy head upon impact. The object was a cylinder (10 × 10 cm2, diameter and height) and weighs 0.4 kg. The investigated materials were Ecoflex, Dragon Skin, and Clay while the thickness were 1 mm, 2 mm, 3 mm, and 5 mm. The velocities of the impacts for the 108 experiments were between 1 m/s and 3 m/s. Three severity indices (i.e. peak head linear acceleration, 3 ms criterion and the Head Injury Criterion (HIC)) were calculated from the raw acceleration data. The impact velocities were tabulated from the video recordings. A summary of the processed data and the raw data are included in this dataset. Online repository contains the files: https://doi.org/10.7910/DVN/TXOPUH. Keywords: Soft materials, Severity indices, Head impact, Safety

Physically-Interactive RoboGames (PIRG) are an emerging application whose aim is to develop robotic agents able to interact and engage humans in a game situation. In this framework, learning a model of players’ activity is relevant both to understand their engagement, as well as to understand specific strategies they adopted, which in turn can foster game adaptation. Following such directions and given the lack of quantitative methods for player modeling in PIRG, we propose a methodology for representing players as a mixture of existing player’s types uncovered from data. This is done by dealing both with the intrinsic uncertainty associated with the setting and with the agent necessity to act in real time to support the game interaction. Our methodology first focuses on encoding time series data generated from player-robot interaction into images, in particular Gramian angular field images, to represent continuous data. To these, we apply latent Dirichlet allocation to summarize the player’s motion style as a probabilistic mixture of different styles discovered from data. This approach has been tested in a dataset collected from a real, physical robot game, where activity patterns are extracted by using a custom three-axis accelerometer sensor module. The obtained results suggest that the proposed system is able to provide a robust description for the player interaction.

"Giochiamo con i robot" e' un laboratorio interattivo per grandi e piccini realizzato per l'edizione 2007 del Festival della Scienza di Genova. Lungo un percorso che va dalla telerobotica alla robotica evolutiva, il laboratorio sviluppa il tema di dare intelligenza ai robot. Questo percorso, le cui tappe sono le varie installazioni, si conclude nella "bottega" dove e' possibile costruire e programmare i propri robot o smontare e modificare quelli esposti durante il percorso didattico. I visitatori sono coinvolti in attivita' ludiche grazie alle quali possonoentrare in contatto con alcune delle idee potenti della robotica,

Le gare di robotica sono state introdotte fin dagli anni '80 nella comunita' scientifica, con lo scopo di confrontare i risultati ottenuti da diversi ricercatori su un terreno comune e condiviso. Nelle gare e' richiesto che i robot svolgano le attivita' definite dalle regole della gara e si misura la qualita' delle prestazioni in modo obiettivo e/o condiviso. Tra gli aspetti interessanti di questo tipo di confronto scientifico vogliamo porne in evidenza alcuni, di rilevanza anche per le gare utilizzate a fini didattici