Motion Recognition Employing Multiple Kernel Learning of Fisher Vectors Using Local Skeleton Features

Human movement models often divide movements into parts. In walking the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into section based on the primary direction of motion. When analyzing a movement, it is important to correctly locate the key events dividing portions. There exist methods for dividing certain actions using data from speci?c sensors. We introduce a generalized method for event annotation based on Hidden Markov Models. Genetic algorithms are used for feature selection and model parameterization. Further, collaborative techniques are explored. We validate this method on a walking dataset using inertial sensors placed on various locations on a human body. Our technique is computationally simple to allow it to run on resource constrained sensor nodes.

Effective inertial sensor quantity and locations on a body for deep learning-based worker's motion recognition

This chapter proposes a fuzzy qualitative (FQ) approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining FQ robot kinematics with human motion trackingHuman motion tracking and recognition algorithms. First, a data quantisation process is proposed to relax the computational complexity suffered from visual tracking algorithms. Secondly, a novel human motion representation, Qualitative Normalised Template (QNT), is developed in terms of the FQ robot kinematics framework to effectively represent human motion. The human skeleton is modelled as a complex kinematic chain, its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional Hidden Markov ModelHidden Markov model (HMM) and Fuzzy Hidden Markov Model (FHMM) shows that the proposed approach consistently outperforms both hidden Markov models in human motion recognition.

In this article, we present a novel approach for continuous operator authentication in teleoperated robotic processes based on Hidden Markov Models (HMM). While HMMs were originally developed and widely used in speech recognition, they have shown great performance in human motion and activity modeling. We make an analogy between human language and teleoperated robotic processes (i.e., words are analogous to a teleoperator’s gestures, sentences are analogous to the entire teleoperated task or process) and implement HMMs to model the teleoperated task. To test the continuous authentication performance of the proposed method, we conducted two sets of analyses. We built a virtual reality (VR) experimental environment using a commodity VR headset (HTC Vive) and haptic feedback enabled controller (Sensable PHANToM Omni) to simulate a real teleoperated task. An experimental study with 10 subjects was then conducted. We also performed simulated continuous operator authentication by using the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The performance of the model was evaluated based on the continuous (real-time) operator authentication accuracy as well as resistance to a simulated impersonation attack. The results suggest that the proposed method is able to achieve 70% (VR experiment) and 81% (JIGSAWS dataset) continuous classification accuracy with as short as a 1-second sample window. It is also capable of detecting an impersonation attack in real-time.

Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the human motion model as a triangulated graph. Previous approaches learned models based just on positions and velocities of the body parts while ignoring their appearance. Moreover, a heuristic approach was commonly used to obtain translation invariance. In this paper we suggest an improved approach for learning such models and using them for human motion recognition. The suggested approach combines multiple cues, i.e., positions, velocities and appearance into both the learning and detection phases. Furthermore, we introduce global variables in the model, which can represent global properties such as translation, scale or view-point. The model is learned in an unsupervised manner from unlabelled data. We show that the suggested hybrid probabilistic model (which combines global variables, like translation, with local variables, like relative positions and appearances of body parts), leads to: (i) faster convergence of learning phase, (it) robustness to occlusions, and, (Hi) higher recognition rate.

Social robot navigation is an evolving research field that aims to find efficient strategies to safely navigate dynamic environments populated by humans. A critical challenge in this domain is the accurate modeling of human motion, which directly impacts the design and evaluation of navigation algorithms. This article presents a comparative study of two popular categories of human motion models used in social robot navigation, namely velocity-based models and force-based models. A system-theoretic representation of both model types is presented, which highlights their common feedback structure, although with different state variables. Several navigation policies based on reinforcement learning are trained and tested in various simulated environments involving pedestrian crowds modeled with these approaches. A comparative study is conducted to assess performance across multiple factors, including human motion model, navigation policy, scenario complexity and crowd density. The results highlight advantages and challenges of different approaches to modeling human behavior, as well as their role during training and testing of learning-based navigation policies. The findings offer valuable insights and guidelines for selecting appropriate human motion models when designing socially-aware robot navigation systems.

Language is an indispensable for humanoid robot to be integrated into daily life. This paper proposes a novel approach to construct a space of motion labels from their mapping to human whole body motions. The motions are abstracted by Hidden Markov Models, which are referred to as motion symbols. The human motions are automatically partitioned into motion segments, and recognized as sequences of the motion symbols. Sequences of motion labels are also assigned to these motions. The referential relationship between the motion symbols and the motion labels is extracted by stochastic translation model, and distances among the labels are calculated from the association probability of the motion symbols being generated by the labels. The labels are located in a multidimensional space so that the distances are satisfied, and it results in a label space. The label space encapsulates relations among the motion labels such as their similarities. The label space also allows motion recognition. The validity of the constructed label space is demonstrated on a motion capture data-set.

Multiple Kernel Based Transfer Learning for the Few-Shot Recognition Task in Smart Home Scene

Body part motion recognition is an important research direction in computer vision and artificial intelligence, widely used in motion analysis, rehabilitation medicine, and game interaction.Traditional methods for recognizing body part movements are usually based on image processing, machine learning, or deep learning techniques.This article proposes a body part motion recognition method based on fuzzy theory and a double-layer hidden Markov model (HMM).This method aims to improve the accuracy and robustness of motion recognition, providing an effective solution for real-time motion capture and pose recognition applications.

It is difficult to give a robot all possible motions beforehand in a certain environment. Therefore, the robot needs to learn how to recognize other motions and to generate its own motions autonomously for working well. These learning algorithms need an efficient way to make recognition and generation of motions work together, because they take many computing resources. This paper focuses on a generation-based recognition. Our system consists of recognition and generation modules. The fanner and latter are constructed from left-to-right hidden Markov models (HMM) and reinforcement learning (RL), respectively. When a HMM in recognition module does not work enough, the model parameters of HMM are re-estimated by using a state-value function of RL in generation module. The proposed method enables us to improve the reliability of the HMM.

This paper presents a hidden Markov model (HMM) based motion recognition approach using abstract interpretation. Firstly, explains the interpretation algorithm in detail. Then uses the hidden Markov model toolkit (HTK) for HMM manipulation and its comprehensive recognition facilities to do the actual job.

Hybrid-feature-guided lung nodule type classification on CT images

Face recognition based on multi-class mapping of Fisher scores

In the industrial human-robot collaboration environment, workers and robots often need to collaborate to complete complex tasks in a shared space. Accurate and robust motion recognition is the key to improving the productivity and safety of human-robot collaboration. Accurately tracking and recognizing workers' motions can provide clues about tasks to be performed, thus ensuring the safety of collaborative workspaces. Aiming at the differences in postures and characteristics of workers' motions in the working environment, the human body's self-occlusion and partial occlusion, this paper uses a skeleton recognition algorithm to estimate the 3D skeleton information in monocular video. A novel skeleton descriptor that describes static and dynamic features of the 3D skeletons in the short time around the current frame is proposed. Besides, we train a deep neural network to recognize the human motion. Experimental results show that the proposed skeleton descriptor can improve the accuracy of motion recognition by about 4%, and the neural network we use outperforms the other three classical classification methods.

Human behaviour modelling for welfare technology using hidden Markov models