Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set. We evaluate classification performance under three scenarios: utilizing grasp posture alone, utilizing grasp force alone, and combining both modalities. We propose Glove-Net, a hybrid CNN-BiLSTM architecture for classifying grasp patterns within our dataset, aiming to harness the unique advantages offered by both CNNs and BiLSTM networks. This model seamlessly integrates CNNs’ spatial feature extraction capabilities with the temporal sequence learning strengths inherent in BiLSTM networks, effectively addressing the intricate dependencies present within our grasping data. Our study includes findings from an extensive ablation study aimed at optimizing model configurations and hyperparameters. We quantify and compare the classification accuracy across these scenarios: CNN achieved 88.09%, 69.38%, and 93.51% testing accuracies for posture-only, force-only, and combined data, respectively. LSTM exhibited accuracies of 86.02%, 70.52%, and 92.19% for the same scenarios. Notably, the hybrid CNN-BiLSTM proposed model demonstrated superior performance with accuracies of 90.83%, 73.12%, and 98.75% across the respective scenarios. Through rigorous numerical experimentation, our results underscore the significance of multimodal grasp classification and highlight the efficacy of the proposed hybrid Glove-Net architectures in leveraging multisensory data for precise grasp recognition. These insights advance understanding of human–machine interaction and hold promise for diverse real-world applications.

This article proposes a radial-gap two-degree-of-freedom motor based on a magnetic screw structure. The proposed motor is composed of stator parts, rotor parts, and a mover part, and can generate torque and thrust force simultaneously and independently. Additionally, the motor can generate a large thrust force with the magnetic screw structure. In the proposed motor, the rotor parts and mover part, which act as the nut and screw of ball screws, are coupled with each other by means of magnetic force. Therefore, there is no friction between the rotor parts and mover part. The proposed motor can not only contribute to the minimization of multidegree-of-freedom systems, such as industrial robots but also improve the energy efficiency of robots. This article examines the effectiveness of the proposed motor through analysis and experimental results.

Force sensorless control with VCM is thought to be challenging because its thrust constant fluctuation characteristic and the flexible flat cable connected to the moving coil degrade the estimation accuracy. Modeling methods of thrust constant fluctuation have been proposed in previous studies. However, these methods were time-consuming, easily affected by friction, and did not consider cable disturbance. This paper presents an identification and compensation methodology for nonlinear internal disturbances in a voice coil motor (VCM) to accomplish precise force control with a linear VCM stage. In this study, a novel compensator was constructed on basis of these deterioration mechanisms. To realize the compensator, the deteriorations had to be obtained. To that end, a novel approach to identify the parameter fluctuation and cable restoring force was developed, and the compensator was applied to sensorless force control. The proposed approaches were verified through experiments using the prototype of a linear VCM stage.

Grasp synergies lead to identification of underlying patterns to develop control strategies for five-fingered prosthetic hands or exoskeletons. Data gloves play a crucial role in study of human grasping and could provide insights into grasp synergies. This paper presents the design and implementation of a data glove that has been fabricated using 3D printing technology and enhanced with instrumentation. The glove utilizes flexible sensors for the fingers and force sensors integrated into the glove at the fingertips to accurately capture grasp postures and forces. Understanding the kinematics and dynamics of human grasp including reach-to-grasp is undertaken. A comprehensive study involving 10 healthy subjects was conducted. Grasp synergy analysis is carried out to identify underlying patterns for grasping. Correlation analysis showed a strong synergy, especially between Index and Middle fingers with a 0.95 correlation coefficient. Principal Component Analysis (PCA) facilitated dimensionality reduction, revealing that three principal components capture over 97 percent of the variance in grasp postures, underscoring the complexity and synergy of hand movements.Grasp classification experiments validated the efficacy of PCA-based synergy, achieving high classification accuracies (95.84% to 92.34%) and demonstrating the method’s competitive performance in scenarios requiring reduced sensor complexity, as confirmed by confusion matrices and comparative analysis with existing methodologies. The t-SNE visualization showcased clusters of grasp postures and forces, unveiling similarities and patterns among different grasp types. These findings could serve as a comprehensive guide in design and control of five-fingered robotic hands and exoskeletons for rehabilitation applications, enabling the replication of natural hand movements.

Human grasps involved in activities of daily living rely on the correlation of multiple degrees of freedom in a hand. This correlation is called grasp synergies and holds promise for efficient control of robotic hands including prostheses and exoskeletons. A number of studies involving human grasping experiments have been reported. However, the approach, lift and hold phases which are intricately linked to the hand grasp for activities of daily living have hardly been part of such studies. We report a human grasping study which includes the approach, lift and hold phases. Grasp synergies in the force and posture space are identified. We have observed distinct force patterns while manipulating different objects. Additionally, our analysis of correlation coefficients between the pair of fingers highlights the cooperative behaviour and synergy between fingers while grasping. Our study contributes valuable insights into finger correlations, and synergies in terms of grasp postures and forces.