Publications by authors named "K Kutsuzawa"

Reliable proprioception and feedback from soft sensors are crucial for enabling soft robots to function intelligently in real-world environments. Nevertheless, soft sensors are fragile and are susceptible to various damage sources in such environments. Some researchers have utilized redundant configuration, where healthy sensors compensate instantaneously for lost ones to maintain proprioception accuracy.

View Article and Find Full Text PDF
Article Synopsis
  • The study focuses on how humans can sense object characteristics like stiffness and shape through their hands while also gauging their confidence in these estimations using proprioceptive signals.
  • Researchers created a learning framework using probabilistic inference and recurrent neural networks to estimate these physical and geometric properties in real time without needing complex hyperparameters.
  • Results showed that the neural networks can effectively quantify confidence levels in their estimations, accounting for uncertainty and task difficulty, which could enhance reliable object manipulation and decision-making in robotics.
View Article and Find Full Text PDF

How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance.

View Article and Find Full Text PDF

The steady increase in the aging population worldwide is expected to cause a shortage of doctors and therapists for older people. This demographic shift requires more efficient and automated systems for rehabilitation and physical ability evaluations. Rehabilitation using mixed reality (MR) technology has attracted much attention in recent years.

View Article and Find Full Text PDF

The lack of intuitive controllability remains a primary challenge in enabling transhumeral amputees to control a prosthesis for arm reaching with residual limb kinematics. Recent advancements in prosthetic arm control have focused on leveraging the predictive capabilities of artificial neural networks (ANNs) to automate elbow joint motion and wrist pronation-supination during target reaching tasks. However, large quantities of human motion data collected from different subjects for various activities of daily living (ADL) tasks are required to train these ANNs.

View Article and Find Full Text PDF