Editorial: Assessment of biomechanical mechanism in the context of sports injury prevention or rehabilitation. [PDF]
Innocenti B.
europepmc +1 more source
Cteno‐Bot: An Untethered Metachronally Swimming Robot With Magnetoactive Propulsors
We present Cteno‐bot, an untethered ctenophore‐inspired robot which swims using metachronally coordinated appendages. A single mechanism controls up to 216 magnetoactive propulsors via a dynamically varying magnetic field. We show that the swimming speed of the robot can be increased without a corresponding increase in power requirement, simply by ...
David J. Peterman, Margaret L. Byron
wiley +1 more source
Lower peak knee joint kinetics during walking in patients with knee reconstruction for bone sarcoma compared to healthy controls. [PDF]
Rice HM +6 more
europepmc +1 more source
A multimodal quad‐finger soft robotic hand (QDO hand) uses dual‐chamber straight–curved origami prismatic (SCOP) origami actuators. By coordinating positive and negative pressurization in the two chambers, each finger produces axial extension, contraction and bidirectional bending.
Qinlin Tan +6 more
wiley +1 more source
Validity, Reliability and Interpretability of an IMU-Based System to Measure 3D Lower Limb Kinematics of Patients with Heterogeneous Gait Disorders. [PDF]
Carcreff L +5 more
europepmc +1 more source
A unified, reusable modeling pipeline enables task‐driven design of soft robots across actuator families and task scenarios. High‐fidelity simulations are compressed into compact pseudo‐rigid‐body joint surrogates, while a design‐conditioned meta‐model generates new surrogates from geometry parameters without rerunning finite element method.
Yao Yao, David Howard, Perla Maiolino
wiley +1 more source
Description and validation of a portable system for biomechanical ex vivo knee kinematics and laxity assessment in simulated intra-operative scenarios. [PDF]
Sisella M +3 more
europepmc +1 more source
Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang +6 more
wiley +1 more source
Flexion-dependent differences in tibial rotation measured by navigation and robotic systems during bicruciate-stabilised total knee arthroplasty. [PDF]
Wada K +5 more
europepmc +1 more source

