Song Huat Yeo

A wearable, self-calibrating, wireless sensor network for body motion processing

A novel self-calibrating sensing technology using miniature linear encoders and inertial/magnetic measurement unit (IMU) provides the accuracy, fast response and robustness required by many body motion processing applications. Our sensor unit consists of an accelerometer, a 3-axis magnetic sensor, 2 gyroscopes and a miniature linear encoder. The fusion of data from the sensors is accomplished by extracting the gravity related term from the accelerometer and consistently calibrating the gyroscopes and linear encoder when the sensor unit is under static conditions. Using the fused sensors, we developed a complete motion processing system that consists of a gateway where the human kinematics modeling is embedded. A time divided multiple access wireless architecture is adopted to synchronize the sensor network at 100 Hz. Experimental results show that the combination of the IMU and linear encoder produces a low RMS error of 3.5deg and correlation coefficient of 99.01%. A video showing the capture a performers upper body motion is also realized.

Inertia sensor-based guidance system for upperlimb posture correction

Stroke rehabilitation is labor-intensive and time-consuming. To assist patients and therapists alike, we propose a wearable system that measures orientation and corrects arm posture using vibrotactile actuators. The system evaluates user posture with respect to a reference and gives feedback in the form of vibration patterns. Users correct their arm posture, one DOF at a time, by following a protocol starting from the shoulder up to the forearm. Five users evaluated the proposed system by replicating ten different postures. Experimental results demonstrated system robustness and showed that some postures were easier to mimic depending on their naturalness.

Building Hand Motion-Based Character Animation: The Case of Puppetry

Automatic motion generation for digital character under the real-time user control is a challenging problem for computer graphic research and virtual environment applications such as on-line games. The present study introduces a methodology to generate a glove puppet animation which is controlled by a new input device, called the Smart Glove, capturing the hand motion. An animation system is proposed to generate the puppet animation based on the procedural animation and motion capture data from Smart Glove. As the control of the puppet character in the animation takes into account the design of Smart Glove and the operation of the puppet in reality, the physical hand motion can either activate the designed procedural animation through motion recognition or tune the parameters of the procedural animation to build the new puppet motion, which allows the direct user control on the animation. The potential application and improvement of the current animation system are also discussed.

Intuitive vibro-tactile feedback for human body movement guidance

Humans rely on the feedbacks they received from a teacher in learning a new motor skill. Tactile feedback is effective in motor learning as it is direct and real-time. We proposed a 3D Orientation Guide (3DOG) which is made up of three coin-sized vibrating motors (tactors) to create meaningful tactile feedback to give users easily interpreted instruction on how to adjust their forearm postures and inform users of the correct forearm motion. The tactors are circling around the forearm and each tactor is assigned two vibration patterns corresponding to an axial movement in positive and negative directions respectively. The layout and vibration pattern design is evaluated through the usability test. 3DOG is proved to be able to create an intuitive tactile feedback which directs motion effectively. When it is integrated with a motion capture system, the device can track and correct body postures that has application in diverse areas, such as rehabilitation, assisting learning motor skills that have requirement for movements with high accuracy. 3DOG has a promising future because of its wearability, effectiveness, small size, and low cost.

A generic tension-closure analysis method for fully-constrained cable-driven parallel manipulators

Cable-driven parallel manipulators (CDPMs) are a special class of parallel manipulators that are driven by cables instead of rigid links. Due to the unilateral property of the cables, all the driving cables in a fully-constrained CDPM must always maintain positive tension. As a result, tension analysis is the most essential issue for these CDPMs. By drawing upon the mathematical theory from convex analysis, a sufficient and necessary tension-closure condition is proposed in this paper. The key point of this tension-closure condition is to construct a critical vector that must be positively expressed by the tension vectors associated with the driving cables. It has been verified that such a tension-closure condition is general enough to cater for CDPMs with different numbers of cables and DOFs. Using the tension-closure condition, a computationally efficient algorithm is developed for the tension-closure pose analysis of CDPMs, in which only a limited set of deterministic linear equation systems need to be resolved. This algorithm has been employed for the tension-closure workspace analysis of CDPMs and verified by a number of computational examples. The computational time required by the proposed algorithm is always shorter as compared to other existing algorithms.

A body sensor network for tracking and monitoring of functional arm motion

In this paper, we present a novel sensing technique, Optical Linear Encoder (OLE), in which the motion of an optical encoder on a reflective strip is converted to limb joints goniometric data. A body sensing module is designed to integrate the OLE and an accelerometer. A sensor network of three sensing modules is established via Controller Areas Network (CAN) bus to capture full motion of human arm with a 7-DOF kinematic model. In addition, a statistical study was conducted to confirm the repeatability and reliability of our sensor network. Results demonstrate that the sensor system has strong potential to be used as a low-cost tool for motion capture, and objective arm function evaluation for both short-term and long-term monitoring.

Gait generation for inchworm-like robot locomotion using finite state model

The gait of a multisegment inchworm robot is a series of actuator actions that will change the shape of the robot to generate a net motion. In this article, we model the multisegment inchworm robot as a finite state automaton. Gait generation is posed as a search problem on the graph described by the automaton with prescribed state transitions. The state transitions are defined based on the kinematics of robot locomotion. The auxiliary actuator concept is introduced. Single-stride and multistride gait generations are discussed. Single-stride gaits exhibit fault-tolerant and real-time computation features that are neccessary in actual applications. Both computer simulation and experimental hardware platform are developed for various aspects of the gait generation and planning.

Integration of Sensing and Feedback Components for Human Motion Replication

Replication of human body motion is a very important means to maintain a subject’s emotion, knowledge and experience. The replication process requires accurate motion capturing system with sensor technologies to measure postures of human bodies and posture transmission, as well as feedback systems to adjust postures to fit into targeted ones. The sensing and feedback technologies are fundamental building blocks of motion capturing systems, work training system and rehabilitation systems. In particular, the construction of sensing and feedback systems for dynamic postures is much more complicated than that for static postures in terms of the time evolution and non-ridge body. We believe that dynamic postures can be represented by a set of blueprint or code like trajectories of particle of human body movement. Furthermore, we derive that the sensing and feedback systems should be able to establish to directly measure those critical particles without relying on external infrastructures. In the paper, some of those kinds of sensing and feedback devices are presented and some evidences such as feature contours are obtained through analysis of captured data by those devices in order to prove our estimation. We confess that our work is preliminary for this new field, but we hope that the work presented here can lead more efforts to bring out systematic approaches of feature detection and extraction of human postures whose characteristics are different from those of video and audio.

Locomotion and navigation of a Planar Walker based on binary actuation

Locomotion and navigation of a surface walking/climbing robot - Planar Walker, based on a novel planar 8-bar mechanism are studied. The robot moves on a surface through decoupled transverse gaits and turning gaits with finite lengths and finite rotation angles. Motions of the gaits are modeled using planar rigid motion group. Three point-to-point navigation methods are developed for various situations: simple line of sight (SLS), simulated annealing accurate planning (SAAP), and localized hybrid accurate planning (LHAP) algorithms. Computer simulation shows that SAAP produces accurate gait sequences and LHAP saves computation time and resources for long-range targets. However, experiment shows that SLS outperforms SAAP and LHAP as the number of gaits becomes the major criteria in evaluating the gait performance due to imprecision of individual gait movements.

Puppet playing: an interactive character animation system with hand motion control

Puppetry is a popular art form involving the process of animating the inanimate performing puppets. Puppet playing not only is controlled by artists hand motions but also follows physical laws and engineering principles. To implement a puppet playing scenario in virtual environments, an interactive animation system is designed by taking into account both the users hand motion and the constraints of puppet and environment. Here the hand motion is real-time captured and recognized through a new input device, namely SmartGlove. The animation system adapts IMHAP testbed for procedural animation and generates puppet animation based on both the procedural animation technique and motion capture data from SmartGlove. Thus the physical hand motion can either activate the designed procedural animation through motion recognition or tune the parameters of the existing procedural animation to generate new puppet motions. This system allows a user to directly control puppet animation while preserving the high accuracy of motion control. The animation results show that the association of hand motion can smoothly plan and generate each procedure animation and seamlessly blend the gap between key frames. The potential application and improvement of the current animation system are discussed.