Manual testing is one of the most common diagnostic methods for anterior cruciate ligament (ACL) injuries in the knee joint. In particular, the Lachman test is superior to others. In this study, a 9-axis inertial sensor and a force sensor are used to measure the examiner's manual technique. In this study, we experimented on the accuracy of the inertial sensor and an experiment to measure the Lachman test. The results of Experiment 1 were within ± 1.7 degrees of the theoretical values for each axis throughout the experiment. The results of Experiment 2 showed that Endpoint could be estimated from the acceleration component of the subject's tibia, the acceleration component of the examinee's hand, and the force of each finger and that the motion of the knee joint and the examinee's hand technique could be measured. The development of this system is expected to have training effects for unskilled examinees.

We consider the problem of learning a sparse rule model, a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over a hyper-rectangle in the input space. Since the number of all possible such rules is extremely large, it has been computationally intractable to select the optimal set of active rules. In this paper, to solve this difficulty for learning the optimal sparse rule model, we propose Safe RuleFit (SRF). Our basic idea is to develop meta safe screening (mSS), which is a non-trivial extension of well-known safe screening (SS) techniques. While SS is used for screening out one feature, mSS can be used for screening out multiple features by exploiting the inclusion-relations of hyper-rectangles in the input space. SRF provides a general framework for fitting sparse rule models for regression and classification, and it can be extended to handle more general sparse regularizations such as group regularization. We demonstrate the advantages of SRF through intensive numerical experiments.

This paper presents a novel approach to sampling subsurface asteroidal regolith under severe time constraints. Sampling operations that must be completed within a few hours require techniques that can manage subsurface obstructions that may be encountered. The large uncertainties due to our lack of knowledge of regolith properties also make sampling difficult. To aid in managing these challenges, machine learning-based detection methods using tactile feedback can detect the presence of rocks deeper than the length of the probe, ensuring reliable sampling in unobstructed areas. In addition, given the variability of soil hardness and the short time available, a corer shooting mechanism has been developed that uses a special shape-memory alloy to collect regolith in about a minute. Experiments on subsurface obstacle detection and shooting-corer ejection tests were conducted to demonstrate the functionality of this approach.

This paper presents a contact-event-triggered filter using only a force-torque sensor with impedance control for non-cooperative, rotating, heavy object capture. Contact events are modeled for prediction, and detected to trigger the particle filter's updating process. By combining these features, a computationally efficient, contact-event-triggered filter is proposed. For our purpose of capture using impedance control, expected contact events, collisions and sliding are defined for prediction and detection. This novel method is implemented in an air bearing robotic system, and has demonstrated its superiority with the highest success rate (100%) for sliding contact mode cases, whereas the previous method could only yield a success rate of 87.9%. The computation resource is demonstrated to be limited, with a computation time of 4.2 milliseconds on average and 8.3 milliseconds at worst.

This paper proposes real-time collision-based contact mode estimation with only a force-torque sensor for capturing a moving rigid body. The contact modes are defined for determining when to generate the signal to close the robotic hand for establishing object closure. In our particle filter approach, collision-triggered filter is used to determine the contact mode with the least amount of computation. Brach's collision model is used for our collision model-based approach for a rigid body because it is computationally light-weighted and enables the sampling of three collision properties for the particle filter. The validity of our method is experimentally demonstrated by achieving the highest success rate using the reasonable computation resources required (average of 3.9 milliseconds and worst of 6.1 milliseconds with our setup), and verifying each computation resource (or number of particles) based on the size of motion estimation error in the pre-capture phase.

This paper presents the path planning and coordinated control of a space robot with a manipulator for capturing a rotating large object. As the grasping point on a rotating large object is translationally moving fast, an appropriate strategy and coordinated motion control of the spacecraft base and robotic arm must be employed for approaching and tracking such a grasping point. In this paper, we propose a robust control scheme including the online path planning and compliance control for grasping such a target. The path planning is derived in a simple form that allows the desired end-effector trajectory to be easily modified in real-time using the newly updated states without complex numerical calculation. In addition, the compliance control allows the end-effector to track the planned trajectory or the moving grasping point, while using contact force feedback to reduce the end-effector position error from the grasping point when capturing the target. This end-effector motion is implemented by coordinated control on the spacecraft base and robotic arm, which can suitably alter their distribution of motion according to scenes using a weighted pseudoinverse matrix. Experiments are conducted to demonstrate the validity of the proposed path planning and compliance control.

This paper discusses robust capture of a free-floating target using a robotic arm. The position error resulting from sensor errors and time delay can cause undesired contact and unstable control. In this paper, we propose a caging-based rigid gripper and impedance control, which enables the robot to capture the target robustly without precise motion tracking and large force interaction. The performance of the proposed method is verified experimentally using an air-floating system that emulates planar microgravity motion.

A rocket-propelled miniature robot is capable to explore sites that planetary rovers cannot reach with efficiency in locomotion distance per mass. The technical difficulty is the significant variance in its flight distance because of two factors: sensitivity of error in the center of gravity with respect to the thrust axis, and solid rocket engines deviance in thrusting force. To overcome the issue, our flight distance control strategy includes flight trajectory forming, and the flight trajectory prediction including the opposing shot. With our method, we experimentally showed the flight distance to the forward direction improved by a factor of four in variance reduction.

We present a new system for interactively modeling and rendering nonphotorealistic hairstyles. The system is featured with a user-friendly sketch interface allowing a user generate his/her desired hairstyle simply by drawing a few free-form strokes. Hairstyles are modeled with a new polygon based technique called cluster polygon, and can be rendered expressively to obtain hair images similar to those found in cell animation and cartoons.