Recent years have seen the rise of many competent pose estimation models. Unfortunately, many of these still rely on manually labelled training data. With many arguing the importance of larger and more diverse pools of training data, this study proposes the use of virtually created hand images for pose estimation training. Synthetically generating virtual pose data provides a faster and more accurate method for creating labelled training data, due to the tedious and taxing nature of manual labelling. This study looks to employ 3D modelling software to simulate and record randomised, realistic, human hand postures. For this, virtual hands were recorded with ever-changing joint angles and randomised backgrounds. This created data was used to retrain an existing YOLOv11 pose estimation network. The model trained with solely synthetic data was able to detect hands in real-world images, though it scored a low accuracy for these images. Injecting an existing dataset with these synthetic data was able to provide a significantly reduced error in the estimations, showcasing a 54.0% increase in accuracy — compared to a model trained without the addition of these data. The results achieved show promise for the utilisation of synthetic data in the training of pose estimation models.

Reducing inter-subject variability between new users and the measured source subjects, and effectively using the information of classification models trained by source subject data, is very important for human–machine interfaces. In this study, we propose a style transfer mapping (STM) and fine-tuning (FT) subject transfer framework using convolutional neural networks (CNNs). To evaluate the performance, we used two types of public surface electromyogram datasets named MyoDatasets and NinaPro database 5. Our proposed framework, STM-FT-CNN, showed the best performances in all cases compared with conventional subject transfer frameworks. In the future, we will build an online processing system that includes this subject transfer framework and verify its performance in online experiments.

This study generates and analyzes unsafe human motions that cannot be measured experimentally in laboratories with dynamic consistency. Detailed whole-body motions are generated by a multilayered kinodynamics simulation (MLKD Sim) that uses a detailed digital whole-body human model and a simple motion-representation model that parametrically represents human motion mechanisms. First, we develop the simple motion-representation model that represents human motion and contact force data that are experimentally measured in a laboratory, and we identify this model's parameters based on these experimental data. Forward dynamics computation of this motion-representation model with changing model and/or environmental parameters simulates motion modification as well as a contact force with dynamic consistency. Finally, the mapping function from the motion-representation model's motion to the detailed motion identified from the experimental data is used to reconstruct the detailed whole-body motion. MLKD Sim reconstructs a vertical contact force with average error of 2.18E+02 N, center of mass trajectory with average error of 3.31E-02 m, ankle joint angle with average error of 1.11E-01 rad (2.95E+00%), and ankle joint torque with average error of 6.13E+01 Nm (1.93E+01%). Unsafe motion simulation results show that the physical load on the hip, knee, and ankle joints increases by 9.23E+01%, 5.42E+02%, and 1.45E+02% respectively with 0.5-m level difference in a running surface. These results imply that when sprinting in an unknown environment, we need to protect, in order, the knee ankle, and hip joints. This study conducts detailed dynamics and kinematics analysis of unsafe human motions that cannot be measured experimentally in laboratories to prevent injuries, falls, and fatigue, and these results should find applications in the fields of medicine and welfare.

Human motion measurement plays an important role in the field of motion analysis, virtual reality and ergonomics design. Recently, it has become possible to measure the human motion using a set of inertial measurement units (IMUs). However, the IMU-based motion-capture (MoCap) system only consider the ground contact constraints, so the constraints between the human and the product are not considered. Therefore, this study aims to develop a full-body motion estimation system using the IMUs, which can reflect the contact conditions between the product and the product users based on the user-specified contact constraints. In addition, the proposed system is validated through the measurement experiments of walking and chair-sitting motions. The walking experiment shows that the averaged errors of the joint angle estimation are ranged from 2.79±2.00 deg for the right ankle to 9.55±6.04 deg for the right hip. In addition, the chair-sitting experiment shows that the proposed system could estimate the sitting motion reflecting the contact condition between the subject and the chair surfaces. Our results indicate the potentials and challenges for measuring a variety of human motion using the proposed system.

Flexible and stretchable electronics are expected to contribute to electric conductors, sensors and actuators for various applications, such as robotic systems and power-assisted systems. In this study, a stretchable strain sensor is proposed as a length sensor for a pneumatic artificial muscle. To measure the length of the artificial muscle, the stretchable strain sensor is required to have quick response characteristics. A capacitance measurement circuit based on a band-pass filter is analyzed, and is tuned by the parameters of its electric components. The circuit measures the capacitance of the strain sensor with accuracy equivalent to a commercially available LCR meter. We confirm that the combination of the capacitance meter and the strain sensor measures the length of the artificial muscle with a high sampling rate of 10 kHz.

This paper presents a data-driven framework for estimating grasp posture and contact region between hand and object by using clustered lower dimensional grasp features. Our framework extracts features of the grasp posture in order to estimate the contact region. Redundant dimension of the hand posture is removed depending on task, which we regard typical for the object. For this purpose, we used mixture principal component analysis (MPCA). This method enables to estimate clusters that can approximate grasp postures by using lower dimensional features. Estimation results of the contact region was obtained by using clustered lower dimensional features of MPCA and object features. These results show that our clusters by MPCA have stronger correlation with the contact region than clustered lower dimensional features obtained by PCA and k-means. Finally, estimation results of the grasp posture and the contact region for three objects are demonstrated. One of the result was compared with grasp posture synthesized by another method presented in the previous research. This comparison revealed that our framework was able to synthesize human-like natural grasp posture than the previous one.

This paper presents a newly designed and developed MR-compatible three-axis force sensor: its principle, structure and performance. It employs a new MR-compatible optical micrometry based on differential measure of light intensity. This technology enables highly accurate and sensitive two degrees-of-freedom displacement sensing by using optoelectronic devices and pair of fiber optics. In order to realize three-axis force sensitivity, two micrometers are aligned in orthogonal directions. The accuracy of this force sensor is better than 3.0% and the maximum displacement of the detector is about 40 /spl mu/m under the applied force ranging from 0 to 15 N in vertical, and -8 to 8 N in horizontal directions. The loss of homogeneity of the magnetic field and signal-to-noise ratio of the MR image caused by this sensor are observed to be 0.49 ppm and 0.49 to 7.30% that show the sufficient MR compatibility of this sensor.