Teo Chee Leong

HandCARE : A Cable-Actuated Rehabilitation System to Train Hand Function After Stroke

We have developed a robotic interface to train hand and finger function. HandCARE is a Cable-actuated rehabilitation system, in which each finger is attached to an instrumented cable loop allowing force control and a predominantly linear displacement. The device, whose designed is based on biomechanical measurements, can assist the subject in opening and closing movements and can be adapted to accommodate various hand shapes and finger sizes. Main features of the interface include a differential sensing system, and a clutch system which allows independent movement of the five fingers with only one actuator. The device is safe, easily transportable, and offers multiple training possibilities. This paper presents the biomechanical measurements carried out to determine the requirements for a finger rehabilitation device, and the design and characterization of the complete system.

Development of a Robot-Assisted Rehabilitation Therapy to train Hand Function for Activities of Daily Living

This paper presents the evaluation of a new two degree-of-freedom robotic interface, and the development of exercises to train movements and force control of wrist and hand. The Haptic Knob has two actuated parallelogram structures with a knob at the output, to interact with the fingers in a way to simulate grasping/releasing, in combination with pronation/supination movements of the forearm. Motivating game-like exercises have been designed according to a functional approach, where fundamental hand function required in activities of daily living (ADL) can be trained, while the device provides assistive or resistive forces. Preliminary testing has been performed with healthy subjects and three chronic stroke patients. Subjects found the exercises to be comfortable, and the robotic interface offers adequate range of motion and forces. A study with a group of chronic stroke patients will be conducted during the next months to determine the potential benefit of a therapy using our robotic equipment.

A Cable Driven Robotic System to Train Finger Function After Stroke

This paper presents a novel robotic interface to train intrinsic finger movements. The mechanical design, base on a cable system interacting with the fingers, offers the possibility of adapting the interface to accommodate various hand sizes and finger orientation. A main feature of the device is a clutch system, which consists of five clutches, one for each finger, that can be switched to three different modes: ( rest mode: the fingers are mechanically maintained at a fixed position, (ii) passive (from the view of the interface) mode: the finger is free to move along the path defined by the cable, and (iii) active mode: the force generated by the motor is applied to the finger.) With this mechanism, it is possible to train hand muscle function using only one actuator. The interaction wit the subject is measured by means of a position encoder an five force sensors located close to the output. We describe the human-oriented design of our underactuated robotic interface based on measured biomechanics. We detail the redundant safety mechanisms, the actuation, sensing and control system and report the performance and preliminary results obtained with this interface.

A technique to train finger coordination and independence after stroke.

Purpose. Finger coordination and independence are often impaired in stroke survivors, preventing them from performing activities of daily living. We have developed a technique using a robotic interface, the HandCARE, to train these functions. Method. The Hand Cable-Actuated REhabilitation (CARE) system can assist the subject in opening and closing movements of the hand, and can be adapted to accommodate various hand sizes and finger shapes. Results. Two game-like exercises have been developed, which use a motivating approach to promote recovery of finger coordination and independence. Mathematical measures have been implemented to evaluate these functions. This technique is validated with two post-stroke subjects who practiced for 20 minutes twice a week during eight weeks. Conclusions. The results show significant improvements in finger coordination, force modulation as well as finger independence.

HandCARE2: A novel cable interface for hand rehabilitation

We have developed a novel robotic interface to train hand and finger function. HandCARE2 is a Cable-Actuated REhabilitation (CARE) system, in which each finger is free to move in space with a large range of movement, from a closed hand to full extension. The device, designed based on biomechanical measurements, can assist the subject in opening and closing movements of the hand and can be adapted to accommodate various hand shapes and finger sizes. The system uses a cable-spring transmission and a clutch system, which allow movement combinations of the five fingers with only two actuators. The device is safe, cost effective and offers multiple possibilities to train finger independence and coordination, with various grasps.

Exercises for rehabilitation and assessment of hand motor function with the Haptic Knob

This paper investigates robot-assisted rehabilitation and assessment of hand function after stroke using the Haptic Knob, a 2 degrees-of-freedom end-effector based robotic device to train grasping and wrist pronation/supination. Nine chronic stroke subjects trained over a period of 6 weeks, with 3 one-hour sessions of robot-assisted therapy per week, consisting in two exercises requiring active participation promoted by therapeutic games. Behavioral data collected by the Haptic Knob were analyzed to evaluate motion control, smoothness and precision over the therapy. Subjects progressively improved their performances in the proposed functional exercises, suggesting improvement in hand motor function. This was confirmed by results of standard clinical assessment as subjects improved a mean of 4.3 points in the Fugl-Meyer assessment scale, accompanied by a decrease in spasticity. These results illustrate the positive effect of therapy with the Haptic Knob and the possibility to use it as an assessment tool to evaluate and monitor hand motor function during rehabilitation therapy.

reachMAN2: A compact rehabilitation robot to train reaching and manipulation

This paper describes the reachMAN2, a rehabilitation robot with minimum degrees-of-freedom to train arm reaching and manipulation for typical ADLs, providing assistance in arm flexion/extension, forearm supination/pronation and hand opening/closing. The design, safety, control and performance evaluation of the system are presented. A handle using an innovative cam mechanism enables natural hand opening/closing movements. The interaction force between the device and the subject is measured via four force sensors located on the handle of the device. A preliminary study with healthy subjects was performed to assess the performances of the device.

A Digital Stereo Microscope Platform for Microsurgery Training

We describe a software defined surgical microscope platform for developing AI applications in surgical training. The microscope has facility to merge and render multiple streams of live and/or stored video, and has the ability to enhance, annotate, and measure using a 3D position and orientation tracking forceps. A configuration mechanism controls the zoom, focus and disparity of the stereo view and stores surgeon and procedure specific configuration. The system tracks the surgical motion and analyses its quality in realtime. Several measures of quality of motion are described and can be used as a platform to develop AI applications in surgical training.

A system for analysing surgical motion under a surgical visual aid

A system for evaluating the hand motion under surgical visual aids is discussed. The system consists of a set of visually guided motor tasks, a method of tracking the hand motion and a set of techniques for analyzing the movement data. Visually guided motor tasks abstract the surgical motion to enable performance by non-surgeons. The movement data is analyzed to identify the motion elements such as motion induced by measurement system noise, motion due to physiological tremor, motion due to myoclonic jerk, drift and voluntary dexterous motion. Empirical mode decomposition is used as the method to analyze the motion data along with its frequency plot to verify that the decomposed signals indeed correspond to the respective processes. Early results of the complex multicomponent data is reported with the extraction of the voluntary movement and the involuntary movement data.