Hand Pose Research Articles

Development of the Anthropomorphic Arm for Collaborative and Home Service Robot CHARMIE

Service robots are rapidly transitioning from concept to reality, making significant strides in development. Similarly, the field of prosthetics is evolving at an impressive pace, with both areas now being highly relevant in the industry. Advancements in these fields are continually pushing the boundaries of what is possible, leading to the increasing creation of individual arm and hand prosthetics, either as standalone units or combined packages. This trend is driven by the rise of advanced collaborative robots that seamlessly integrate with human counterparts in real-world applications. This paper presents an open-source, 3D-printed robotic arm that has been assembled and programmed using two distinct approaches. The first approach involves controlling the hand via teleoperation, utilizing a camera and machine learning-based hand pose estimation. This method details the programming techniques and processes required to capture data from the camera and convert it into hardware signals. The second approach employs kinematic control using the Denavit-Hartenbergmethod to define motion and determine the position of the end effector in 3D space. Additionally, this work discusses the assembly and modifications made to the arm and hand to create a cost-effective and practical solution. Typically, implementing teleoperation requires numerous sensors and cameras to ensure smooth and successful operation. This paper explores methods enabled by artificial intelligence (AI) that reduce the need for extensive sensor arrays and equipment. It investigates how AI-generated data can be translated into tangible hardware applications across various fields. The advancements in computer vision, combined with AI capable of accurately predicting poses, have the potential to revolutionize the way we control and interact with the world around us.

Accurate and Real-Time Variant Hand Pose Estimation Based on Gray Code Bounding Box Representation

Accurate and Real-Time Variant Hand Pose Estimation Based on Gray Code Bounding Box Representation

LGCANet: lightweight hand pose estimation network based on HRNet

LGCANet: lightweight hand pose estimation network based on HRNet

Sign Language Detection

Sign language serves as the primary mode of communication for millions of individuals worldwide with hearing impairments. Despite its importance, the availability of sign language interpretation services remains limited, contributing to significant communication barriers. To address this challenge, we propose the development of a web application leveraging state-of-the-art deep learning techniques for real-time sign language detection and translation. Key words : Sign language recognition, Gesture recognition, Machine learning algorithms, Human-computer interaction, Hand pose estimation, Image processing

Handpose Estimation Based Learning Academy for Improving Typing Efficiency by Using Mobile Technologies

This paper focuses on enhancing touch-typing skills through state-of-the-art deep learning technologies. It has been demonstrated that writing posture can be assessed using integrated keyboard detection and hand pose estimation in real-time browser environments. A unique dataset was created for training the object detection model to recognize individual keyboards. The object detection models were trained using various architectures and then assessed for both inference time and accuracy. In addition, a hand pose estimation was implemented to precisely recognize 21 knuckle points of the hand and compute their exact positions. In order to implement object detection and hand pose estimation, a stream transmission of the keyboard and hand scene is required. For this purpose, server-client communication via a QR code connection is implemented to transfer the stream between two mobile devices. Based on these deep learning technologies, a mobile web app was developed that offers 170 learning courses for touch-typing training. With the help of the Typing Learning Academy prototype, a study was conducted as part of this paper to evaluate the usability and utility of the learning app. This research demonstrates the potential for enhancing the development of writing skills through touch typing through the utilization of advanced deep learning technologies.

3D hand pose and mesh estimation via a generic Topology-aware Transformer model.

In Human-Robot Interaction (HRI), accurate 3D hand pose and mesh estimation hold critical importance. However, inferring reasonable and accurate poses in severe self-occlusion and high self-similarity remains an inherent challenge. In order to alleviate the ambiguity caused by invisible and similar joints during HRI, we propose a new Topology-aware Transformer network named HandGCNFormer with depth image as input, incorporating prior knowledge of hand kinematic topology into the network while modeling long-range contextual information. Specifically, we propose a novel Graphformer decoder with an additional Node-offset Graph Convolutional layer (NoffGConv). The Graphformer decoder optimizes the synergy between the Transformer and GCN, capturing long-range dependencies and local topological connections between joints. On top of that, we replace the standard MLP prediction head with a novel Topology-aware head to better exploit local topological constraints for more reasonable and accurate poses. Our method achieves state-of-the-art 3D hand pose estimation performance on four challenging datasets, including Hands2017, NYU, ICVL, and MSRA. To further demonstrate the effectiveness and scalability of our proposed Graphformer Decoder and Topology aware head, we extend our framework to HandGCNFormer-Mesh for the 3D hand mesh estimation task. The extended framework efficiently integrates a shape regressor with the original Graphformer Decoder and Topology aware head, producing Mano parameters. The results on the HO-3D dataset, which contains various and challenging occlusions, show that our HandGCNFormer-Mesh achieves competitive results compared to previous state-of-the-art 3D hand mesh estimation methods.

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the "move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation

The effectiveness of deep neural network models is intricately tied to the distribution of training data. However, in pose estimation, potential discrepancies in root joint positions and inherent variability in biomechanical features across datasets are often overlooked in current training strategies. To address these challenges, a novel Hand Pose Biomechanical Model (HPBM) is developed. In contrast to the traditional 3D coordinate-encoded pose, it provides a more intuitive depiction of the anatomical characteristics of the hand. Through this model, a data normalization approach is implemented to align the root joint and unify the biomechanical features of training samples. Furthermore, the HPBM facilitates a weakly supervised strategy for dataset expansion, significantly enhancing the data diversity. The proposed normalized method is evaluated on two widely used 3D hand pose estimation datasets, RHD and STB, demonstrating superior performance compared to the models trained without normalized datasets. Utilizing ground truth 2D keypoints as input, a reduction of 45.1% and 43.4% in error is achieved on the STB and RHD datasets, respectively. When leveraging 2D keypoints from MediaPipe, a reduction in error by 11.3% and 14.3% is observed on the STB and RHD datasets.

3D hand pose estimation and reconstruction based on multi-feature fusion

3D hand pose estimation and shape reconstruction is to recover the hand joint points and hand mesh vertices coordinates from the image. However, existing methods usually only use the high-level semantic features extracted by the backbone network to represent the hand mesh vertex features, which leads to a single representation of the hand vertices features and cannot fully utilize the feature information extracted by the network. In this paper, we propose a method for real-time 3D reconstruction of hands from a single RGB image, which enriches the 3D semantic information of the mesh vertices through multi-feature fusion. Firstly, we regress the 2D features of mesh vertices through Integral Pose Regression (IPR) and regard them as prior information to 3D features. Then we design a Multi-Scale Sampling(MSS) module to extract multi-scale information. Finally we fuse 2D prior features, multi-scale features, and high-level semantic features extracted by backbone to represent 3D initial feature. Additionally, we propose a Multi-Root(MR) loss function to address the imbalance problem caused by a single root joint. The experimental results indicate that our network achieves competitive performance on the FreiHAND and HO-3D public datasets, achieving fast inference speed with fewer parameters.

Clustering user preferences for personalized teleoperation control schemes via trajectory similarity analysis.

Successful operation of a teleoperated robot depends on a well-designed control scheme to translate human motion into robot motion; however, a single control scheme may not be suitable for all users. On the other hand, individual personalization of control schemes may be infeasible for designers to produce. In this paper, we present a method by which users may be classified into groups with mutually compatible control scheme preferences. Users are asked to demonstrate freehand motions to control a simulated robot in a virtual reality environment. Hand pose data is captured and compared with other users using SLAM trajectory similarity analysis techniques. The resulting pairwise trajectory error metrics are used to cluster participants based on their control motions, without foreknowledge of the number or types of control scheme preferences that may exist. The clusters identified for two different robots shows that a small number of clusters form stably for each case, each with its own control scheme paradigm. Survey data from participants validates that the clusters identified through this method correspond to the participants' control scheme rationales, and also identify nuances in participant control scheme descriptions that may not be obvious to designers relying only on participant explanations of their preferences.

BaSICNet: Lightweight 3-D Hand Pose Estimation Network Based on Biomechanical Structure Information for Dexterous Manipulator Teleoperation

The use of hand pose for dexterous manipulator teleoperation is an attractive method to the control of the multi-fingered manipulators. Furthermore, the advancement of the deep learning and depth sensors has encouraged the development of the 3D hand pose estimation. However, developing a 3D hand pose estimation method with an accurate and real-time performance is still a difficult task in computer vision. In this paper, a lightweight depth-based network named biomechanical structure information cascade network (BaSICNet) is proposed by considering the global and local structure of human hands to improve the performance through a cascade network and a bone-constraint loss function. In addition, the BaSICNet is applied to a five-fingered dexterous manipulator platform to achieve visual hand-based teleoperation. Extensive evaluations on two public datasets show that the BaSICNet can produce accurate and fast 3D hand poses (9.15mm and 7.59mm mean errors on NYU and MSRA datasets with 114.7 fps), and can achieve superior 3D hand pose estimation balance of accuracy and speed when compared with state-of-the-art (SOAT) methods. Experiment results on the dexterous manipulator platform also show that the BaSICNet can be applied well for teleoperation.

Hand LightWeightNet: an optimized hand pose estimation for interactive mobile interfaces

In this paper, a hand pose estimation method is introduced that combines MobileNetV3 and CrossInfoNet into a single pipeline. The proposed approach is tailored for mobile phone processors through optimizations, modifications, and enhancements made to both architectures, resulting in a lightweight solution. MobileNetV3 provides the bottleneck for feature extraction and refinements while CrossInfoNet benefits the proposed system through a multitask information sharing mechanism. In the feature extraction stage, we utilized an inverted residual block that achieves a balance between accuracy and efficiency in limited parameters. Additionally, in the feature refinement stage, we incorporated a new best-performing activation function called “activate or not” ACON, which demonstrated stability and superior performance in learning linearly and non-linearly gates of the whole activation area of the network by setting hyperparameters to switch between active and inactive states. As a result, our network operated with 65% reduced parameters, but improved speed by 39% which is suitable for running in a mobile device processor. During experiment, we conducted test evaluation on three hand pose datasets to assess the generalization capacity of our system. On all the tested datasets, the proposed approach demonstrates consistently higher performance while using significantly fewer parameters than existing methods. This indicates that the proposed system has the potential to enable new hand pose estimation applications such as virtual reality, augmented reality and sign language recognition on mobile devices.

Ultimate pose estimation: A comparative study

AbstractPose estimation is a computer vision task used to detect and estimate the pose of a person or an object in images or videos. It has some challenges that can leverage advances in computer vision research and others that require efficient solutions. In this paper, we provide a preliminary review of the state‐of‐the‐art in pose estimation, including both traditional and deep learning approaches. Also, we implement and compare the performance of Hand Pose Estimation (HandPE), which uses PoseNet architecture for hand sign problems, for an ASL dataset by using different optimizers based on 10 common evaluation metrics on different datasets. Also, we discuss some related future research directions in the field of pose estimation and explore new architectures for pose estimation types. After applying the PoseNet model, the experiment results showed that the accuracy achieved was 99.9%, 89%, 97%, 79%, and 99% for the ASL alphabet, HARPET, Yoga, Animal, and Head datasets, comparing those with common optimizers and evaluation metrics on different dataset.

Monocular 3D Hand Mesh Recovery via Dual Noise Estimation

Current parametric models have made notable progress in 3D hand pose and shape estimation. However, due to the fixed hand topology and complex hand poses, current models are hard to generate meshes that are aligned with the image well. To tackle this issue, we introduce a dual noise estimation method in this paper. Given a single-view image as input, we first adopt a baseline parametric regressor to obtain the coarse hand meshes. We assume the mesh vertices and their image-plane projections are noisy, and can be associated in a unified probabilistic model. We then learn the distributions of noise to refine mesh vertices and their projections. The refined vertices are further utilized to refine camera parameters in a closed-form manner. Consequently, our method obtains well-aligned and high-quality 3D hand meshes. Extensive experiments on the large-scale Interhand2.6M dataset demonstrate that the proposed method not only improves the performance of its baseline by more than 10% but also achieves state-of-the-art performance. Project page: https://github.com/hanhuili/DNE4Hand.

Cross-Constrained Progressive Inference for 3D Hand Pose Estimation with Dynamic Observer-Decision-Adjuster Networks

Generalization is very important for pose estimation, especially for 3D pose estimation where small changes in the 2D images could trigger structural changes in the 3D space. To achieve generalization, the system needs to have the capability of detecting estimation errors by double-checking the projection coherence between the 3D and 2D spaces and adapting its network inference process based on this feedback. Current pose estimation is one-time feed-forward and lacks the capability to gather feedback and adapt the inference outcome. To address this problem, we propose to explore the concept of progressive inference where the network learns an observer to continuously detect the prediction error based on constraints matching, as well as an adjuster to refine its inference outcome based on these constraints errors. Within the context of 3D hand pose estimation, we find that this observer-adjuster design is relatively unstable since the observer is operating in the 2D image domain while the adjuster is operating in the 3D domain. To address this issue, we propose to construct two sets of observers-adjusters with complementary constraints from different perspectives. They operate in a dynamic sequential manner controlled by a decision network to progressively improve the 3D pose estimation. We refer to this method as Cross-Constrained Progressive Inference (CCPI). Our extensive experimental results on FreiHAND and HO-3D benchmark datasets demonstrate that the proposed CCPI method is able to significantly improve the generalization capability and performance of 3D hand pose estimation.

Keypoint Fusion for RGB-D Based 3D Hand Pose Estimation

Previous 3D hand pose estimation methods primarily rely on a single modality, either RGB or depth, and the comprehensive utilization of the dual modalities has not been extensively explored. RGB and depth data provide complementary information and thus can be fused to enhance the robustness of 3D hand pose estimation. However, there exist two problems for applying existing fusion methods in 3D hand pose estimation: redundancy of dense feature fusion and ambiguity of visual features. First, pixel-wise feature interactions introduce high computational costs and ineffective calculations of invalid pixels. Second, visual features suffer from ambiguity due to color and texture similarities, as well as depth holes and noise caused by frequent hand movements, which interferes with modeling cross-modal correlations. In this paper, we propose Keypoint-Fusion for RGB-D based 3D hand pose estimation, which leverages the unique advantages of dual modalities to mutually eliminate the feature ambiguity, and performs cross-modal feature fusion in a more efficient way. Specifically, we focus cross-modal fusion on sparse yet informative spatial regions (i.e. keypoints). Meanwhile, by explicitly extracting relatively more reliable information as disambiguation evidence, depth modality provides 3D geometric information for RGB feature pixels, and RGB modality complements the precise edge information lost due to the depth noise. Keypoint-Fusion achieves state-of-the-art performance on two challenging hand datasets, significantly decreasing the error compared with previous single-modal methods.

Attentional bias for hands: Cascade dual‐decoder transformer for sign language production

AbstractSign Language Production (SLP) refers to the task of translating textural forms of spoken language into corresponding sign language expressions. Sign languages convey meaning by means of multiple asynchronous articulators, including manual and non‐manual information channels. Recent deep learning‐based SLP models directly generate the full‐articulatory sign sequence from the text input in an end‐to‐end manner. However, these models largely down weight the importance of subtle differences in the manual articulation due to the effect of regression to the mean. To explore these neglected aspects, an efficient cascade dual‐decoder Transformer (CasDual‐Transformer) for SLP is proposed to learn, successively, two mappings SLPhand: Text → Hand pose and SLPsign: Text → Sign pose, utilising an attention‐based alignment module that fuses the hand and sign features from previous time steps to predict more expressive sign pose at the current time step. In addition, to provide more efficacious guidance, a novel spatio‐temporal loss to penalise shape dissimilarity and temporal distortions of produced sequences is introduced. Experimental studies are performed on two benchmark sign language datasets from distinct cultures to verify the performance of the proposed model. Both quantitative and qualitative results show that the authors’ model demonstrates competitive performance compared to state‐of‐the‐art models, and in some cases, achieves considerable improvements over them.

A Dexterous Hand-Arm Teleoperation System Based on Hand Pose Estimation and Active Vision.

Markerless vision-based teleoperation that leverages innovations in computer vision offers the advantages of allowing natural and noninvasive finger motions for multifingered robot hands. However, current pose estimation methods still face inaccuracy issues due to the self-occlusion of the fingers. Herein, we develop a novel vision-based hand-arm teleoperation system that captures the human hands from the best viewpoint and at a suitable distance. This teleoperation system consists of an end-to-end hand pose regression network and a controlled active vision system. The end-to-end pose regression network (Transteleop), combined with an auxiliary reconstruction loss function, captures the human hand through a low-cost depth camera and predicts joint commands of the robot based on the image-to-image translation method. To obtain the optimal observation of the human hand, an active vision system is implemented by a robot arm at the local site that ensures the high accuracy of the proposed neural network. Human arm motions are simultaneously mapped to the slave robot arm under relative control. Quantitative network evaluation and a variety of complex manipulation tasks, for example, tower building, pouring, and multitable cup stacking, demonstrate the practicality and stability of the proposed teleoperation system.

An artificial intelligence-based bone age assessment model for Han and Tibetan children.

Background: Manual bone age assessment (BAA) is associated with longer interpretation time and higher cost and variability, thus posing challenges in areas with restricted medical facilities, such as the high-altitude Tibetan Plateau. The application of artificial intelligence (AI) for automating BAA could facilitate resolving this issue. This study aimed to develop an AI-based BAA model for Han and Tibetan children. Methods: A model named "EVG-BANet" was trained using three datasets, including the Radiology Society of North America (RSNA) dataset (training set n = 12611, validation set n = 1425, and test set n = 200), the Radiological Hand Pose Estimation (RHPE) dataset (training set n = 5491, validation set n = 713, and test set n = 79), and a self-established local dataset [training set n = 825 and test set n = 351 (Han n = 216 and Tibetan n = 135)]. An open-access state-of-the-art model BoNet was used for comparison. The accuracy and generalizability of the two models were evaluated using the abovementioned three test sets and an external test set (n = 256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1year were used as indicators. Bias was evaluated by comparing the MAD between the demographic groups. Results: EVG-BANet outperformed BoNet in the MAD on the RHPE test set (0.52 vs. 0.63years, p < 0.001), the local test set (0.47 vs. 0.62years, p < 0.001), and the external test set (0.53 vs. 0.66years, p < 0.001) and exhibited a comparable MAD on the RSNA test set (0.34 vs. 0.35years, p = 0.934). EVG-BANet achieved accuracy within 1year of 97.7% on the local test set (BoNet 90%, p < 0.001) and 89.5% on the external test set (BoNet 85.5%, p = 0.066). EVG-BANet showed no bias in the local test set but exhibited a bias related to chronological age in the external test set. Conclusion: EVG-BANet can accurately predict the bone age (BA) for both Han children and Tibetan children living in the Tibetan Plateau with limited healthcare facilities.

Kinematic-driven human-robot interaction system with deep learning for flexible acupuncture needling manipulations

Acupuncture is an important therapeutic method in traditional Chinese medicine and essentially characterized by flexible needling manipulations that are complicated for repetitions. In this work, we designed a bio-inspired acupuncture robot combined with human–machine interaction system that can implement flexible and precise needle manipulations as manual acupuncture. In the proposed framework, the manual needling process was recorded with online imaging techniques. With a deep learning-based acupuncture estimator, kinetic parameters of lift insertion (LI) and twist rotation (TR) manipulations were extracted in three-dimensional space and the precent of correct key points for operator’s hand pose estimation achieved 94.03%. It is founded that the variation of operator’s finger vector angle for LI manipulation was much weaker than TR manipulation. Furthermore, inspired by kinematic characteristics of manual acupuncture, a six-degree-of-freedom robot was used to simulate the coordinated motion of hand joints in needling manipulations through robot kinematics analysis and trajectory planning with polynomial interpolation method. Numerical and experiential results showed that the acupuncture robot can mimic various needling manipulations as manual acupuncture. The flexibility and stability of the robotic acupuncture framework were evaluated for a wide range of needling parameters. It was demonstrated that the robot can follow the periodic dynamics of LI and TR manipulations with various frequencies and amplitudes, and perform accurate acupuncture operations with smooth needling trajectories as joint jitters during needle manipulations were completely eliminated. The presented results provide a new strategy for modern applications of acupuncture treatment with robotic and flexible needling manipulations to improve the therapeutic efficacy.