Abstract Remote monitoring and evaluation of human motion during daily life require accurate extraction of kinematic quantities of body segments. Current approaches use inertial sensors that require numerical time differentiation to access the angular acceleration vector, a mathematical operation that greatly increases noise in the acceleration value. Here we introduce a wearable sensor that utilises a spatially defined cluster of inertial measurement units on a rigid base for directly measuring the angular acceleration vector. For this reason, we used computational modelling and experimental data to demonstrate that our new sensor configuration improves the accuracy of tracking angular acceleration vectors. We confirmed the feasibility of tracking human movement by automatic assessment of experimental fall initiation and balance recovery responses. The sensor therefore presents an opportunity to pioneer reliable assessment of human movement and balance in daily life. 

A wearable sensor and framework for accurate remote monitoring of human motion

Abstract To improve locomotion and operation integration, this paper presents an integrated leg-arm quadruped robot (ILQR) that has a reconfigurable joint. First, the reconfigurable joint is designed and assembled at the end of the leg-arm chain. When the robot performs a task, reconfigurable configuration and mode switching can be achieved using this joint. In contrast from traditional quadruped robots, this robot can stack in a designated area to optimize the occupied volume in a nonworking state. Kinematics modeling and dynamics modeling are established to evaluate the mechanical properties for multiple modes. All working modes of the robot are classified, which can be defined as deployable mode, locomotion mode and operation mode. Based on the stability margin and mechanical modeling, switching analysis and evaluation between each mode is carried out. Finally, the prototype experimental results verify the function realization and switching stability of multimode and provide a design method to integrate and perform multimode for quadruped robots with deployable characteristics. 

Multimode Design and Analysis of an Integrated Leg-Arm Quadruped Robot with Deployable Characteristics

Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates. 

Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care

The escalating proliferation of space debris poses an increasing risk to spinning satellites, elevating the probability of hazardous collisions that can result in severe damage or total loss of functionality. To address this concern, a pioneering inflatable protective structure is employed to ensure the optimal functionality of spinning satellites. Additionally, a multi-body dynamic modeling method based on spring hinge unfolding/spring expansion is proposed to tackle the complex dynamics of spinning satellites with inflatable protective structures during flight. This method enables analysis of the motion parameters of spinning satellites. First, the structural composition of a spinning satellite with inflatable protective structures is introduced and its flight process is analyzed. Then, an articulated spring hinge unfolding model or a spring expansion model using the Newton–Euler method is established to describe the unfolding or expansion of the spinning satellite with inflatable protective structures during flight. Finally, the effects on the motion parameters of a spinning satellite are analyzed through simulation under various working conditions.

Modeling and Disturbance Analysis of Spinning Satellites with Inflatable Protective Structures

Robots are increasingly being used across various sectors, from industry and healthcare to household applications. In practice, a pivotal challenge is the reaction to unexpected external disturbances, whose real-time feedback often relies on (noisy) sensor measurements. Subsequent inverse-dynamics calculations demand noise-amplifying numerical differentiation, leading to impracticable results. Although much effort has been spent on establishing direct measurement approaches, their measurement uncertainty quantification has not or yet insufficiently been tackled in the literature. Here, we propose a multi-method framework to develop an angular acceleration reference and provide evidence that it can serve as a measurement standard to calibrate various kinematic sensors. Within the framework, we use Monte-Carlo simulations to quantify the uncertainty of a direct measurement sensor recently developed by our team; the inertial measurement cluster (IMC). For angular accelerations up to 21 rad/s2, the standard deviation of the IMC was on average only 0.3 rad/s2 (95% CI: [0.28,0.31]  rad/s2), which constitutes a reliable data-sheet record. Further, using least-squares optimization, we show that the deviation of IMC with respect to the reference was not only less on the level of angular acceleration but also on the level of angular velocity and angle, when compared to other direct and indirect measurement methods. Maximilian Gießler and colleagues present a multi-method framework to provide an angular acceleration reference, which significantly improves the accuracy compared to other approaches. The framework could serve as a measurement standard to calibrate various sensors.

A multi-method framework for establishing an angular acceleration reference in sensor calibration and uncertainty quantification

Abstract Maintaining stability while walking on arbitrary surfaces or dealing with external perturbations is of great interest in humanoid robotics research. Increasing the system’s autonomous robustness to a variety of postural threats during locomotion is the key despite the need to evaluate noisy sensor signals. The equations of motion are the foundation of all published approaches. In contrast, we propose a more adequate evaluation of the equations of motion with respect to an arbitrary moving reference point in a non-inertial reference frame. Conceptual advantages are, e.g., getting independent of global position and velocity vectors estimated by sensor fusions or calculating the imaginary zero-moment point walking on different inclined ground surfaces. Further, we improve the calculation results by reducing noise-amplifying methods in our algorithm and using specific characteristics of physical robots. We use simulation results to compare our algorithm with established approaches and test it with experimental robot data. 

/pdf/robust-inverse-dynamics-by-evaluating-newton-euler-equations-3rn4pc2a.pdf

Robust inverse dynamics by evaluating Newton–Euler equations with respect to a moving reference and measuring angular acceleration

A manipulator with a human-like range of motion combined with adequate control strategies can increase the autonomy of humanoid robots. If we look at the motion patterns of human beings during manipulation, we see that they often combine trunk and manipulator motion to grasp objects lying outside the workspace of the manipulator itself. In this work, we introduce an approach for a hybrid inverse kinematics algorithm with a focus on avoiding joint and workspace limitations. We combine a fully analytical inverse kinematics algorithm for a redundant 7-DOF manipulator with the inverse kinematics of the robot's trunk. To handle the workspace limitations, if a desired pose lies outside the workspace, we combine the robot's trunk motion with the manipulator motion. We demonstrate the performance of our approach with the humanoid robot Sweaty, playing fully autonomously a chess game against a human while picking up chess pieces lying outside the workspace of its manipulator by a combined trunk and manipulator motion. 

Hybrid Inverse Kinematics for a 7-DOF Manipulator Handling Joint Limits and Workspace Constraints

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Maximilian Gießler

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Papers

A wearable sensor and framework for accurate remote monitoring of human motion

Robust inverse dynamics by evaluating Newton–Euler equations with respect to a moving reference and measuring angular acceleration

A multi-method framework for establishing an angular acceleration reference in sensor calibration and uncertainty quantification

Hybrid Inverse Kinematics for a 7-DOF Manipulator Handling Joint Limits and Workspace Constraints