Tendon-based modelling, estimation and control for a simulated high-DoF anthropomorphic hand model

Tendon-driven anthropomorphic robotic hands often lack direct joint angle sensing, as the integration of joint encoders can compromise mechanical compactness and dexterity. This paper presents a computational method for estimating joint positions from measured tendon displacements and tensions. An efficient kinematic modeling framework for anthropomorphic hands is first introduced based on the Denavit-Hartenberg convention. Using a simplified tendon model, a system of nonlinear equations relating tendon states to joint positions is derived and solved via a nonlinear optimization approach. The estimated joint angles are then employed for closed-loop control through a Jacobian-based proportional-integral (PI) controller augmented with a feedforward term, enabling gesture tracking without direct joint sensing. The effectiveness and limitations of the proposed estimation and control framework are demonstrated in the MuJoCo simulation environment using the Anatomically Correct Biomechatronic Hand, featuring five degrees of freedom for each long finger and six degrees of freedom for the thumb.

The impact of tactile sensor configurations on grasp learning efficiency -- a comparative evaluation in simulation

Tactile sensors are breaking into the field of robotics to provide direct information related to contact surfaces, including contact events, slip events and even texture identification. These events are especially important for robotic hand designs, including prosthetics, as they can greatly improve grasp stability. Most presently published robotic hand designs, however, implement them in vastly different densities and layouts on the hand surface, often reserving the majority of the available space. We used simulations to evaluate 6 different tactile sensor configurations with different densities and layouts, based on their impact on reinforcement learning. Our two-setup system allows for robust results that are not dependent on the use of a given physics simulator, robotic hand model or machine learning algorithm. Our results show setup-specific, as well as generalized effects across the 6 sensorized simulations, and we identify one configuration as consistently yielding the best performance across both setups. These results could help future research aimed at robotic hand designs, including prostheses.

pyPCG: A Python Toolbox Specialized for Phonocardiography Analysis

Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. Development for methods which analyse phonocardiographical data try to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. Although there is little to no standardization in these published algorithms and multiple parts of these models have to be reimplemented on a case-by-case basis. Datasets containing heart sound recordings also lack standardization in both data storage and labeling, especially in fetal phonocardiography. We are presenting a toolbox that can serve as a basis for a future standard framework for heart sound analysis. This toolbox contains some of the most widely used processing steps, and with these, complex analysis processes can be created. These functions can be individually tested. Due to the interdependence of the steps, we validated the current segmentation stage using a manually labeled fetal phonocardiogram dataset comprising 50 one-minute abdominal PCG recordings, which include 6,758 S1 and 6,729 S2 labels. Our results were compared to other common and available segmentation methods, peak detection with the Neurokit2 library, and the Hidden Semi-Markov Model by Springer et al. With a 30 ms tolerance our best model achieved a 97.1% F1 score and 10.8 +/- 7.9 ms mean absolute error for S1 detection. This detection accuracy outperformed all tested methods. With this a more accurate S2 detection method can be created as a multi-step process. After an accurate segmentation the extracted features should be representative of the selected segments, which allows for more accurate statistics or classification models. The toolbox contains functions for both feature extraction and statistics creation which are compatible with the previous steps.