6 accepted papers
Accurate prediction of human joint moments is essential for enhancing the stability, responsiveness, and control of wearable exoskeletons. Although machine learning approaches from inertial measurement unit (IMU) signals have advanced joint moment estimation, existing models still suffer from limite
Modeling and controlling the musculoskeletal system are crucial for understanding human motor functions, optimizing human-robot interaction, and developing embodied intelligence. However, existing musculoskeletal models are mainly limited to specific body parts and muscle groups, and still face chal
This paper presents a comparative analysis of model-based control strategies for a Bionic Ankle Tensegrity Exoskeleton (BATE), designed to emulate the self-stress equilibrium and self-supporting characteristics of the human ankle biotensegrity structure. Model-based control strategies are convention
This paper proposes a novel Tensegrity-Based Fracture Reduction Robot (TFRR) designed to enhance the safety and efficacy of orthopedic procedures through integrated force-sensing and control capabilities. Inspired by the biomechanics of skeletal muscles, the robot adopts a tensegrity architecture th
Twisted String Actuators (TSAs) exhibit several advantages, including lightweight, compact, and having a high power-to-weight ratio. However, current research on kinematic models of TSAs is limited to deriving the relationship between motor input and output through idealized geometric calculations.…
The differential mechanism can reduce the number of actuators and efficiently distribute force or power. We proposed a novel multiport output twisted string actuator (MO-TSA) with self-differential mechanism that employs a single actuator to achieve multiport outputs. The differential MO-TSA is adap…