Abu Ubaidah Shamsudin

Neuro-Based Thumb-Tip Force and Joint Angle Modelling for Development of Prosthetic Thumb Control

Human fingers have a specific role that contributes to different hand functions. Among these fingers, the thumb plays the most special function as an anchor to many hand activities. As a result, the loss of the thumb due to traumatic accidents can be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. The movement of the prosthetic device can be naturally controlled by using electromyogram (EMG) signals. In this work, the EMG signals from the human muscles were measured in different thumb configurations and thumb-tip forces in flexion movement. The muscles involved are the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). The classification of the EMG signals based on different force and thumb configurations is performed using an Artificial Neural Network (ANN). From a series of experiments, the results show that the neural network efficiently classified the signals and a unique set of EMG signals was generated for each thumb movement and force. Therefore, EMG signals were used to control the prosthetic movement with aid from the developed neural network.

Fog removal using laser beam penetration, laser intensity, and geometrical features for 3D measurements in fog-filled room

Abstract Three dimension (3D) point cloud data in fog-filled environments were measured using light detection and ranging (LIDAR). Disaster response robots cannot easily navigate through such environments because this data contain false data and distance errors caused by fog. We propose a method for recognizing and removing fog based on 3D point cloud features and a distance correction method for reducing measurement errors. Laser intensity and geometrical features are used to recognize false data. However, these features are not sufficient to measure a 3D point cloud in fog-filled environments with 6 and 2 m visibility, as misjudgments occur. To reduce misjudgment, laser beam penetration features were added. Support vector machine (SVM) and K-nearest neighbor (KNN) are used to classify point cloud data into ‘fog’ and ‘objects.’ We evaluated our method in heavy fog (6 and 2 m visibility). SVM has a better F-measure than KNN; it is higher than 90% in heavy fog (6 and 2 m visibility). The distance error correction method reduces distance errors in 3D point cloud data by a maximum of 4.6%. A 3D point cloud was successfully measured using LIDAR in a fog-filled environment. Our method’s recall (90.1%) and F-measure (79.4%) confirmed its robustness.

Preliminary approach of FES-cycling framework model development for spinal cord injury application: Part 1

Since 1960s, functional electrical stimulation (FES) applications has been used to improves, recover and restore several functions of paralyzed muscles due to spinal cord injury (SCI), stroke and any level of injury related to spinal. FES induced movement control is a significantly challenging area due to complexity and nonlinearity of musculoskeletal system which is the complexity control of muscle motor function model by the artificial activation of paralyzed muscles. Lots of research interest in lower-limb model especially for paraplegia either by exo-skeleton, gaiting exercise, robotics assistive device, wheel-chair, cycling, rowing and etc. Thus, in part one (1) of this paper discussed surface issues of several relations between software and hardware development via FES-Cycling framework model. Simple and efficient approach by using model-based design for experimental process and data gathering. The computer-based closed-loop FES-ScienceMode Hasomed-GmbH system uses MATLAB/Simulink, Software Development Kit 7.1 and RealTime Windows Target under Windows 7 for online data acquisition via microcontroller based, controlling and processing. Proposed optimization fuzzy control-strategy about FES-Cycling induced performance simulation studies against muscle fatigue and external disturbances with customized musculoskeletal mechanism lower limb model for cycling applied and majorly about result and discussions will be highlight in part two (2) for upcoming publications.

A high-level controller for robot-assisted rehabilitation based on Chedoke-McMaster stroke assessment

Amongst the major challenges in post-stroke rehabilitation are the repetitiveness nature of rehabilitation procedure, and the accessibility of therapists for long-term treatment. In manual rehabilitation procedure, the patient is subjected to repetitive mechanical movement of the affected limb by the therapist. In one of the techniques called active-assist exercise, the subject moves his affected limb along a specified trajectory with the therapist guiding the motion. The therapist gives some assistance to the subject to complete the course if deemed necessary and the procedure repeats.. The significant advantages of using robots in assisting rehabilitation are its efficiency and it is fatigue free. The robots however need to be developed to have the capability of human therapist in providing the rehabilitation more naturally. In this paper, the research work focuses on developing a new framework for the robot controller system. In particular, a high-level controller, which is in the form of supervisory controller based on discrete event system theory, is discussed. The controller is capable of giving complex, autonomous guidance during the therapeutic procedure naturally based on the Chedoke-McMaster stroke assessment method.

Two-stage Hybrid A* path-planning in large petrochemical complexes

In this study, we aim to achieve path-planning for firefighter robots in large petrochemical complexes. In large environments, path-planning (e.g., Hybrid A*) requires a large computation memory and a long execution time. These constrains are not feasible for firefighter robots. In order to overcome these two challenges, we propose a two-stage hybrid A* path-planning. For the first stage we use a global path-planner that makes a path using a low-resolution grid map of 2 m. The global path-planner generates a path for an area of approx. 500 m ×1000 m in 10 seconds. In the second stage, we refine the path by using a local-planner that uses a local-map of 100 m ×100 m size around the robot with a high resolution grid of 1 m. The local planner receives its sub-goal from the global planner and recalculates a local path at a high speed of a few hundred milliseconds. Therefore, the local-planner can react to changes of the map due to obstacles in real-time. We evaluated our proposed method by comparing with conventional hybrid A* in simulated as well as real experimental data of petrochemical complexes. By employing the local-planner our method could drastically reduce the used memory and execution time for the re-planning. For a trajectory of 600 m, our method reduces the execution time by 99.2% for real data and by 94.34% for simulated data. The memory usage was likewise drastically reduced by 97.45% for real data and by 97.91% for simulated data.

Evaluation of LIDAR and GPS based SLAM on fire disaster in petrochemical complexes

Firefighter robot autonomy is important for fire disaster response robotics. SLAM is a key technology for the autonomy. We want to know if SLAM can be used in fire disasters. However, evaluating SLAM in an actual fire disaster is not possible because we cannot generate large fires in actual petrochemical complexes. In this study, we simulated a fire disaster, collected sensor data for different conditions in the fire disaster, and evaluated the accuracy of the SLAM. The fire effect for LIDAR was analyzed and the effect embedded in the LIDAR measurement simulator. Several sensor interval parameters used by a heat protection cover was also analyzed for protecting sensor from heat. The evaluation result show the best parameter is 1 s measurement and 9 s sensor cooling which the average accuracy of GPS and LIDAR based SLAM was in the range 0.25 — 0.36 m in the most difficult scenario in the petrochemical complex, has dimensions 1000 m × 600 m. Using the simulator enables us to evaluate the best interval parameter of GPS and LIDAR based SLAM at the fire disaster. The knowledge from the fire effect of the LIDAR could be used to improve LIDAR measurement in actual fire disasters.

Electromyography (EMG)-based thump-tip force estimation for prosthetic thumb

Every normal-born human have five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gripping a ball and holding a spoon for eating. As a result, the lost of thumb due to traumatic accidents could be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb can be worn to complement the function of the rest of the fingers. In this work the relationship between the electromyography (EMG) and thumb tip force is investigated in order to develop a more natural controlled prosthetic thumb. The signals are measured from the thumb intrinsic muscles namely the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). Meanwhile the thumb tip force is recorded by using the force sensor (FSR). The relationship between the EMG signals to the thumb-tip force is established by using Artificial Neural Network (ANN). A series of experiments have been conducted and preliminary results show the efficacy of ANN to capture the relationship model.