This paper presents a comparative analysis of six prominent registration techniques for solving CAD model alignment problems. Unlike the typical approach of assessing registration algorithms with synthetic datasets, our study utilizes point clouds generated from the Cranfield benchmark. Point clouds are sampled from existing CAD models and 3D scans of physical objects, introducing real-world complexities such as noise and outliers. The acquired point cloud scans, including ground-truth transformations, are made publicly available. This dataset includes several cleaned-up scans of nine 3D-printed objects. Our main contribution lies in assessing the performance of three classical (GO-ICP, RANSAC, FGR) and three learning-based (PointNetLK, RPMNet, ROPNet) methods on real-world scans, using a wide range of metrics. These include recall, accuracy and computation time. Our comparison shows a high accuracy for GO-ICP, as well as PointNetLK, RANSAC and RPMNet combined with ICP refinement. However, apart from GO-ICP, all methods show a significant number of failure cases when applied to scans containing more noise or requiring larger transformations. FGR and RANSAC are among the quickest methods, while GO-ICP takes several seconds to solve. Finally, while learning-based methods demonstrate good performance and low computation times, they have difficulties in training and generalizing. Our results can aid novice researchers in the field in selecting a suitable registration method for their application, based on quantitative metrics. Furthermore, our code can be used by others to evaluate novel methods.

This PRISMA systematic review covers the literature on predictive, musculoskeletal simulations. First, we define predictive movement for musculoskeletal systems, as the current literature suffers from inconsistent nomenclature. We distinguish two methods of prediction. The first uses neural models, like muscle-reflex-based and central pattern generator models. The second uses optimization, to make up for the lack of a neural model, like optimal control and deep reinforcement learning. For each method, we illustrate the main concepts and report accuracies, simulation times and limitations. Moreover, we identified key works over the past 50 years, which are fundamental for the current state-of-the-art. The majority of works employ optimization. We recognize six classes of cost function terms and note they are often combined using linear combinations. We describe musculoskeletal models, their muscle model, ground contact model and personalization. Similarly, we identify key software like OpenSim and SCONE. Additionally, we provide an overview of simulated movements, pathologies and assistive devices. We emphasize the difference in tracking simulations and prediction, while clarifying the benefits of using experimental data to predict movement. Finally, we call for quantitative validation to establish comprehensive comparisons between methods. To this end, we share a list of works open-sourcing their codes.

Accurate assessment of human movement and behavior is essential in fields such as ergonomics, rehabilitation, and human-robot interaction. This paper presents PhysioSense, an open-source framework for synchronized multi-modal data acquisition and management. Built on the Lab Streaming Layer (LSL), PhysioSense integrates heterogeneous data streams from kinematic, dynamic, and physiological sensors in real time, ensuring millisecond-level synchronization. Unlike general-purpose tools such as LabVIEW, OpenSignals, or ROS, PhysioSense is specifically tailored to human-centric research, offering a streamlined interface for sensor configuration, recording, visualization, and data export. The framework’s modular design supports extensibility and reproducibility, making it suitable for a range of experimental setups. Two case studies—an ergonomics analysis and a drilling task assessment—demonstrate the framework’s capabilities in real-world scenarios. PhysioSense addresses key challenges in multi-sensor integration and paves the way for more accessible and scalable movement analysis in both research and applied settings.

Assistive devices are a promising tool for enhancing or restoring human walking. Weight is an important consideration when designing these devices, yet datasets for weighted human walking are scarce. We measured 13 healthy, young participants walking at their own pace in two conditions: normal, flat ground walking, and walking with bilateral weights attached to the shank. The weights were applied symmetrically and ranged from 1 kg to 5 kg in steps of 1 kg. Each task was repeated ten times. We recorded 3D positions of 58 optical markers, ground reaction forces and 16 surface electromyography signals from 8 bilateral, lower-limb muscles. Additionally, the dataset includes static pose and maximum voluntary contraction trials. Moreover, we also provide processed, gait-averaged joint kinematics, kinetics and powers, and OpenSim input files. Codes to process the data and run the OpenSim pipelines are also shared.