This paper introduces a novel approach for prosthetic wrist control, addressing limitations of traditional electromyography-based methods. While previous research has primarily focused on hand and gripper development, our study emphasizes the importance of wrist mobility for enhancing dexterity and manipulation skills. Leveraging a combination of visual data and inertial sensors, we proposed a system capable of estimating object orientation in real-time, enabling automatic and natural control of a prosthetic wrist. Our deep learning-based model can accurately interpret object posture from the user’s perspective, facilitating seamless wrist movement based on object inclination. In addition, Gaussian filtering was employed to mitigate noise in image-based posture estimation while preserving essential trends. Through this approach, users can achieve natural positioning without needing additional muscle movements, thus significantly improving prosthetic usability and user experience.

Hands perform various functions. There are many inconveniences in life without the use of hands. People without the use of hands wear prostheses. Recently, there have been many developments and studies about robotic prosthetic hands performing hand functions. Grasping motions of robotic prosthetic hands are integral in performing various functions. Grasping motions of robotic prosthetic hands are required recognition of grasping targets. A path toward using images to recognize grasping targets exists. In this study, object recognition in images for grasping motions are performed by using object detection based on deep-learning. A suitable model for the grasping motion was examined through three object detection models. Also, we present a method for selecting a grasping target when several objects are recognized. Additionally, it will be used for grasping control of robotic prosthetic hands in the future and possibly enable automatic control robotic prosthetic hands.

Conventional prosthetic hands require users to activate designated muscles or press buttons to select among predefined grasping patterns. These methods are time-consuming and increase muscle fatigue. This study proposes a regression model that differentiates multiple muscle activation patterns allowing the user to select a desired grasping pattern. We classified four hand primitives and three force intensities, which can reflect the intention of prosthetic hand users. An 8-channel band-type sEMG sensor was used to measure myoelectric signals from an amputated upper-arm. To acquire the sEMG data, the amputee was instructed to imagine four hand primitives (fist, open hand, flexion, and extension) with three levels of force intensity (low, medium, and high). Time-domain features (mean average value, variance, waveform length, and root mean square) were extracted from the sEMG signal and classified using a Support Vector Machine. The hand primitives and force intensities had accuracies of 95% and 90%, respectively. Results indicate the regression model reflected the user’s intention to select different grasping patterns, and is thus expected to improve the quality of life of amputees.