Real-world applications focused on interpreting human behavior often require multiple computer vision tasks to be addressed simultaneously. Multitask learning typically achieves this by jointly training a single deep neural network to learn shared representations, providing efficiency and improving generalization. Although action and gesture recognition are closely related tasks, since they focus on body and hand movements, current state-of-the-art methods handle them separately. In this paper, we show that employing a multi-task learning paradigm for action and gesture recognition results in more efficient, robust and generalizable visual representations, by leveraging the synergies between these tasks. Experiments on multiple action and gesture datasets demonstrate that handling actions and gestures in a single architecture can achieve better performance for both tasks in comparison to their single-task learning variants.

Extra High Voltage Centers are critical to power transmission, yet their maintenance still relies on manual, time-based inspections with handheld thermal cameras. This approach is labor-intensive, costly, and prone to missing early faults. In this paper we present ENORASI, an autonomous robotic platform for condition-based inspection of EHVCs. ENORASI integrates 3D LiDAR-based mapping, visibility scoring, and traversability analysis to enable safe and efficient navigation in complex substation terrain. A decision support system enables smart scheduling and report generation, while an optimal path planner solves a Generalized Traveling Salesman Problem for efficient routing. Component faults are detected by combining RGB-based object recognition with thermal image analysis. Initial deployments demonstrate that ENORASI can increase inspection frequency, reduce operational costs, and support the transition from time-based to condition-based maintenance. These results validate ENORASI as a scalable and effective alternative to manual inspection for critical power infrastructure.

Robots can play a significant role as assistive devices for people with movement impairment and mild cognitive deficit. In this paper we present an overview of the lightweight i-Walk intelligent robotic rollator, which offers cognitive and mobility assistance to the elderly and to people with light to moderate mobility impairment. The utility, usability, safety and technical performance of the device is investigated through a clinical study, which took place at a rehabilitation center in Greece involving real patients with mild to moderate cognitive and mobility impairment. This first evaluation study comprised a set of scenarios in a number of pre-defined use cases, including physical rehabilitation exercises, as well as mobility and ambulation involved in typical daily living activities of the patients. The design and implementation of this study is discussed in detail, along with the obtained results, which include both an objective and a subjective evaluation of the system operation, based on a set of technical performance measures and a validated questionnaire for the analysis of qualitative data, respectively. The study shows that the technical modules performed satisfactory under real conditions, and that the users generally hold very positive views of the platform, considering it safe and reliable.

Robotic rollators can play a significant role as assistive devices for people with impaired movement and mild cognitive deficit. This paper presents an overview of the i-Walk concept; an intelligent robotic rollator offering cognitive and ambulatory assistance to people with light to moderate movement impairment, such as the elderly. We discuss the two robotic prototypes being developed, their various novel functionalities, system architecture, modules and function scope, and present preliminary experimental results with actual users.

Nowadays, the interaction between humans and robots is constantly expanding, requiring more and more human motion recognition applications to operate in real time. However, most works on temporal action detection and recognition perform these tasks in offline manner, i.e. temporally segmented videos are classified as a whole. In this paper, based on the recently proposed framework of Temporal Recurrent Networks, we explore how temporal context and human movement dynamics can be effectively employed for online action detection. Our approach uses various state-of-the-art architectures and appropriately combines the extracted features in order to improve action detection. We evaluate our method on a challenging but widely used dataset for temporal action localization, THUMOS'14. Our experiments show significant improvement over the baseline method, achieving state-of-the art results on THUMOS'14.

Robotic rollators can play a significant role as assistive devices for people with impaired movement and mild cognitive deficit. This paper presents an overview of the i-Walk concept; an intelligent robotic rollator offering cognitive and ambulatory assistance to people with light to moderate movement impairment, such as the elderly. We discuss the two robotic prototypes being developed, their various novel functionalities, system architecture, modules and function scope, and present preliminary experimental results with actual users.

In this paper we present a prototype integrated robotic system, the I-Support bathing robot, that aims at supporting new aspects of assisted daily-living activities on a real-life scenario. The paper focuses on describing and evaluating key novel technological features of the system, with the emphasis on cognitive human–robot interaction modules and their evaluation through a series of clinical validation studies. The I-Support project on its whole has envisioned the development of an innovative, modular, ICT-supported service robotic system that assists frail seniors to safely and independently complete an entire sequence of physically and cognitively demanding bathing tasks, such as properly washing their back and their lower limbs. A variety of innovative technologies have been researched and a set of advanced modules of sensing, cognition, actuation and control have been developed and seamlessly integrated to enable the system to adapt to the target population abilities. These technologies include: human activity monitoring and recognition, adaptation of a motorized chair for safe transfer of the elderly in and out the bathing cabin, a context awareness system that provides full environmental awareness, as well as a prototype soft robotic arm and a set of user-adaptive robot motion planning and control algorithms. This paper focuses in particular on the multimodal action recognition system, developed to monitor, analyze and predict user actions with a high level of accuracy and detail in real-time, which are then interpreted as robotic tasks. In the same framework, the analysis of human actions that have become available through the project’s multimodal audio–gestural dataset, has led to the successful modeling of Human–Robot Communication, achieving an effective and natural interaction between users and the assistive robotic platform. In order to evaluate the I-Support system, two multinational validation studies were conducted under realistic operating conditions in two clinical pilot sites. Some of the findings of these studies are presented and analyzed in the paper, showing good results in terms of: (i) high acceptability regarding the system usability by this particularly challenging target group, the elderly end-users, and (ii) overall task effectiveness of the system in different operating modes.

We explore new aspects on assistive living via smart social human-robot interaction (HRI) involving automatic recognition of multimodal gestures and speech in a natural interface, providing social features in HRI. We discuss a whole framework of resources, including datasets and tools, briefly shown in two real-life use cases for elderly subjects: a multimodal interface of an assistive robotic rollator and an assistive bathing robot. We discuss these domain specific tasks, and open source tools, which can be used to build such HRI systems, as well as indicative results. Sharing such resources can open new perspectives in assistive HRI.

Within the context of assistive robotics we develop an intelligent interface that provides multimodal sensory processing capabilities for human action recognition. Human action is considered in multimodal terms, containing inputs such as audio from microphone arrays, and visual inputs from high definition and depth cameras. Exploring state-of-the-art approaches from automatic speech recognition, and visual action recognition, we multimodally recognize actions and commands. By fusing the unimodal information streams, we obtain the optimum multimodal hypothesis which is to be further exploited by the active mobility assistance robot in the framework of the MOBOT EU research project. Evidence from recognition experiments shows that by integrating multiple sensors and modalities, we increase multimodal recognition performance in the newly acquired challenging dataset involving elderly people while interacting with the assistive robot.

We explore new directions for automatic human gesture recognition and human joint angle estimation as applied for human-robot interaction in the context of an actual challenging task of assistive living for real-life elderly subjects. Our contributions include state-of-the-art approaches for both low- and mid-level vision, as well as for higher level action and gesture recognition. The first direction investigates a deep learning based framework for the challenging task of human joint angle estimation on noisy real world RGB-D images. The second direction includes the employment of dense trajectory features for online processing of videos for automatic gesture recognition with real-time performance. Our approaches are evaluated both qualitative and quantitatively on a newly acquired dataset that is constructed on a challenging real-life scenario on assistive living for elderly subjects.

We introduce a new framework to build human-computer interfaces that provide online automatic audio-gestural command recognition. The overall system allows the construction of a multimodal interface that recognizes user input expressed naturally as audio commands and manual gestures, captured by sensors such as Kinect. It includes a component for acquiring multimodal user data which is used as input to a module responsible for training audio-gestural models. These models are employed by the automatic recognition component, which supports online recognition of audio-visual modalities. The overall framework is exemplified by a working system use case. This demonstrates the potential of the overall software platform, which can be employed to build other new human-computer interaction systems. Moreover, users may populate libraries of models and/or data, that can be shared in the network. In this way users may reuse or extend existing systems.

Motivated by the recent advances in human-robot interaction we present a new dataset, a suite of tools to handle it and state-of-the-art work on visual gestures and audio commands recognition. The dataset has been collected with an integrated annotation and acquisition web-interface that facilitates on-the-way temporal ground-truths for fast acquisition. The dataset includes gesture instances in which the subjects are not in strict setup positions, and contains multiple scenarios, not restricted to a single static configuration. We accompany it by a valuable suite of tools as the practical interface to acquire audio-visual data in the robotic operating system, a state-of-the-art learning pipeline to train visual gesture and audio command models, and an online gesture recognition system. Finally, we include a rich evaluation of the dataset providing rich and insightfull experimental recognition results.

We present a novel video representation for human action recognition by considering temporal sequences of visual words. Based on state-of-the-art dense trajectories, we introduce temporal bundles of dominant, that is most frequent, visual words. These are employed to construct a complementary action representation of ordered dominant visual word sequences, that additionally incorporates fine grained temporal information. We exploit the introduced temporal information by applying local sub-sequence alignment that quantifies the similarity between sequences. This facilitates the fusion of our representation with the bag-of-visual-words (BoVW) representation. Our approach incorporates sequential temporal structure and results in a low-dimensional representation compared to the BoVW, while still yielding a descent result when combined with it. Experiments on the KTH, Hollywood2 and the challenging HMDB51 datasets show that the proposed framework is complementary to the BoVW representation, which discards temporal order.

One of the main objectives of the EU project MOBOT [1], which generally aims at the development of an intelligent active mobility assistance robot, is to provide multimodal sensory processing capabilities for human action recognition. Specifically, a reliable multimodal information processing and action recognition system needs to be developed, that will detect, analyze and recognize the human user actions based on the captured multimodal sensory signals and with a reasonable level of accuracy and detail within the context of the MOBOT framework for intelligent assistive robotics. Different sensory modalities need to be combined into an integrated human action recognition system. One of the main thrusts in the above effort is the development of robust and effective computer vision techniques to achieve the visual processing goals based on multiple cues such as spatiotemporal RGB appearance data as well as depth data from Kinect sensors. Another major challenge is the integration of recognizing specific verbal and gestural commands in the considered human-robot interaction context. In this presentation we summarize advancements in three tasks of the above multimodal processing system for humanrobot interaction (HRI): action recognition, gesture recognition and spoken command recognition.