One of the primary purposes of video is to capture people and their unique activities. It is often the case that the experience of watching the video can be enhanced by adding a musical soundtrack that is in-sync with the rhythmic features of these activities.

Detecting and preventing falls in humans is a critical component of assistive robotic systems. While significant progress has been made in detecting falls, the prediction of falls before they happen, and analysis of the transient state between stability and an impending fall remain unexplored. In this paper, we propose a anticipatory fall detection method that utilizes a hybrid model combining Dynamic Graph Neural Networks (DGNN) with Long Short-Term Memory (LSTM) networks that decoupled the motion prediction and gait classification tasks to anticipate falls with high accuracy. Our approach employs real-time skeletal features extracted from video sequences as input for the proposed model. The DGNN acts as a classifier, distinguishing between three gait states: stable, transient, and fall. The LSTM-based network then predicts human movement in subsequent time steps, enabling early detection of falls. The proposed model was trained and validated using the OUMVLP-Pose and URFD datasets, demonstrating superior performance in terms of prediction error and recognition accuracy compared to models relying solely on DGNN and models from literature. The results indicate that decoupling prediction and classification improves performance compared to addressing the unified problem using only the DGNN. Furthermore, our method allows for the monitoring of the transient state, offering valuable insights that could enhance the functionality of advanced assistance systems.

One of the primary purposes of video is to capture people and their unique activities. It is often the case that the experience of watching the video can be enhanced by adding a musical soundtrack that is in-sync with the rhythmic features of these activities. Such a problem is challenging since little is known about capturing the rhythmic nature of free body movements. In this work, we explore this problem and propose a novel system, called RhythmicNet', which takes as an input a video which includes human movements and generates a soundtrack for it. RhythmicNet works directly with human movements by extracting skeleton keypoints and implements a sequence of models which translate the keypoints to rhythmic sounds.RhythmicNet follows the natural process of music improvisation which includes the prescription of streams of the beat, the rhythm and the melody.

An AI that helps humanoid robots mirror a person's movement could allow robots to walk, dance and fight in more convincingly human ways. The most agile and fluid robotic movements, such as Boston Dynamics's impressive demonstrations of robot acrobatics, are typically narrow, pre-programmed sequences. Teaching robots to perform a wider repertoire of convincingly human movements is still difficult. To overcome this hurdle, Xuanbin Peng at the University of California, San Diego, and his colleagues have developed an artificial intelligence system called ExBody2, which lets robots copy and smoothly perform many different human movements in more lifelike ways. Peng and his team first created a database of actions that a humanoid robot might be capable of performing, from simple movements like standing or walking to more complex manoeuvres, such as tricky dance moves.

Safety in industrial robotic environments is a hot research topic in the area of human-robot interaction (HRI). Up to now, a robotic arm on an assembly line interacts with other machines away from human workers. Nowadays, robotic arm manufactures are aimed to their robots could increasingly perform tasks collaborating with humans. One of the ways to improve this collaboration is by making the movement of robots more humanlike. This way, it would be easier for a human to foresee the movement of the robot and approach it without fear of contact. The main difference between the movement of a human and of a robotic arm is that the former has a bell-shaped speed profile while the latter has a uniform speed one. To generate this speed profile, the kinematic theory of rapid human movements and its Sigma-Lognormal model has been used. This model is widely used to explain most of the basic phenomena related to the control of human movements. Both human-like and robotic-like movements are transferred to the UR3 robot. In this paper we detail the how the UR3 robot was programmed to produce both kinds of movement. The dissimilarities result between the input motion and output motion to the robot confirm the possibility to develop human-like velocities in the UR3 robot.

The HTC VIVE Ultimate Tracker, utilizing inside-out tracking with internal stereo cameras providing 6 DoF tracking without external cameras, offers a cost-efficient and straightforward setup for motion tracking. Initially designed for the gaming and VR industry, we explored its application beyond VR, providing source code for data capturing in both C++ and Python without requiring a VR headset. This study is the first to evaluate the tracker's precision across various experimental scenarios. To assess the robustness of the tracking precision, we employed a robotic arm as a precise and repeatable source of motion. Using the OptiTrack system as a reference, we conducted tests under varying experimental conditions: lighting, movement velocity, environmental changes caused by displacing objects in the scene, and human movement in front of the trackers, as well as varying the displacement size relative to the calibration center. On average, the HTC VIVE Ultimate Tracker achieved a precision of 4.98 mm +/- 4 mm across various conditions. The most critical factors affecting accuracy were lighting conditions, movement velocity, and range of motion relative to the calibration center. For practical evaluation, we captured human movements with 5 trackers in realistic motion capture scenarios. Our findings indicate sufficient precision for capturing human movements, validated through two tasks: a low-dynamic pick-and-place task and high-dynamic fencing movements performed by an elite athlete. Even though its precision is lower than that of conventional fixed-camera-based motion capture systems and its performance is influenced by several factors, the HTC VIVE Ultimate Tracker demonstrates adequate accuracy for a variety of motion tracking applications. Its ability to capture human or object movements outside of VR or MOCAP environments makes it particularly versatile.

Collaborative robots or cobots interact with humans in a common work environment. In cobots, one under investigated but important issue is related to their movement and how it is perceived by humans. This paper tries to analyze whether humans prefer a robot moving in a human or in a robotic fashion. To this end, the present work lays out what differentiates the movement performed by an industrial robotic arm from that performed by a human one. The main difference lies in the fact that the robotic movement has a trapezoidal speed profile, while for the human arm, the speed profile is bell-shaped and during complex movements, it can be considered as a sum of superimposed bell-shaped movements. Based on the lognormality principle, a procedure was developed for a robotic arm to perform human-like movements. Both speed profiles were implemented in two industrial robots, namely, an ABB IRB 120 and a Universal Robot UR3. Three tests were used to study the subjects' preference when seeing both movements and another analyzed the same when interacting with the robot by touching its ends with their fingers.

Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited.

Pedestrian trajectory prediction plays an important role in autonomous driving systems and robotics. Recent work utilising prominent deep learning models for pedestrian motion prediction makes limited a priori assumptions about human movements, resulting in a lack of explainability and explicit constraints enforced on predicted trajectories. This paper presents a dynamics-based deep learning framework where a novel asymptotically stable dynamical system is integrated into a deep learning model. Our novel asymptotically stable dynamical system is used to model human goal-targeted motion by enforcing the human walking trajectory converges to a predicted goal position and provides a deep learning model with prior knowledge and explainability. Our deep learning model utilises recent innovations from transformer networks and is used to learn some features of human motion, such as collision avoidance, for our proposed dynamical system. The experimental results show that our framework outperforms recent prominent models in pedestrian trajectory prediction on five benchmark human motion datasets.

The analysis of human movements has been extensively studied due to its wide variety of practical applications, such as human-robot interaction, human learning applications, or clinical diagnosis. Nevertheless, the state-of-the-art still faces scientific challenges when modeling human movements. To begin, new models must account for the stochasticity of human movement and the physical structure of the human body in order to accurately predict the evolution of full-body motion descriptors over time. Second, while utilizing deep learning algorithms, their explainability in terms of body posture predictions needs to be improved as they lack comprehensible representations of human movement. This paper addresses these challenges by introducing three novel methods for creating explainable representations of human movement. In this study, human body movement is formulated as a state-space model adhering to the structure of the Gesture Operational Model (GOM), whose parameters are estimated through the application of deep learning and statistical algorithms. The trained models are used for the full-body dexterity analysis of expert professionals, in which dynamic associations between body joints are identified, and for generating artificially professional movements.