A Simple Upper Limb Rehabilitation Trainer

Stroke is the leading cause of disability. Reaching movement including shoulder and elbow movements is the most important movement for many daily activities routine. To maximize functional recovery, stroke survivors go through rehabilitation sessions, under the supervision of physiotherapists in hospitals. Unfortunately, physiotherapy is generally limited to only a few hours per week and labor-intensive. There are many robotic devices have been developed to overcome this problem. However, the cost of rehabilitation robots is still a common problem, limiting their cost-benefit profile and making evaluating and implementing them on a large scale difficult. This paper presents the simple, compact and low cost interactive rehabilitation module for upper limb rehabilitation purposes. This module is intended to be used for training of shoulder and elbow movements integrated with game like virtual reality system

Lesson learnt from an EEG-based experiment with ADHD children in Malaysia

There are growing interests among researchers worldwide pertaining to efficacy of electroencephalography (EEG) as diagnostic tools and noninvasive treatment for children with special needs. However, there are very limited studies discuss the efficacy of EEG-based experiment protocols among young children with ADHD particularly from the perspective of human-computer interaction methodologies. Thus, this paper provides some background on related studies in EEG for children with attention-deficit/hyperactive disorder (ADHD) and some insights on Malaysia experience with regards to ADHD detection and intervention programs. The lesson learnt presented in this paper highlights the factors that affect young children participation in EEG-based experiments that is relevant and beneficial for researchers who are working with children with special needs. © Springer International Publishing Switzerland 2016

Assessment of natural radionuclide in the soil in perimeter of UiTM Pahang Campus Jengka / Hisyam Abdul Rahman

Radionuclides such as Uranium-238 (238U), Thorium-232 e32Th) and Potassium-40 (40K) are unstable nucleus. It can occur naturally and known as second highest cause for lung cancer if being exposed for long period of time. UiTM Pahang campus Jengka is one of the branches of UiTM in Pahang. The study was done to know the concentration of naturally occurring radioactive material (NORM) in the soil aside from to find the radiological risk by calculate the external hazard index in UiTM Pahang. The sample was collected by using hand auger and then survey meter is used to obtain the absorbed dose rate of that place. Then the soil was dried up in oven at 700C for 24 hr. After being dried up, the soil is then sieved through 355 J.Ul1 sieve. By using EDXRF, the concentration of 238U, 232Th, 4~ and external hazard index was . obtained as 45.1 ± 2.9Bq/kg, 59.3 ± 2.9Bq/kg, 447.8 ± 26.7Bq/kg and 0.33 Hex. Then an isodose map was generated by using Surfer 14 softwar

A Nonlinear Model for Online Identifying a High-Speed Bidirectional DC Motor

The modeling system is a process to define the real physical system mathematically, and the input/output data are responsible for configuring the relation between them as a mathematical model. Most ofthe actual systems have nonlinear performance, and this nonlinear behavior is the inherent feature for thosesystems; Mechatronic systems are not an exception. Transforming the electrical energy to mechanical one orvice versa has not been done entirely. There are usually losses as heat, or due to reverse mechanical, electrical,or magnetic energy, takes irregular shapes, and they are concerned as the significant resource of that nonlinearbehavior. The article introduces a nonlinear online Identification of a high-speed bidirectional DC motor withdead zone and Coulomb friction effect, which represent a primary nonlinear source, as well as viscosity forces.The Wiener block-oriented nonlinear system with neural networks are implemented to identify the nonlin-ear dynamic, mechatronic system. Online identification is adopted using the recursive weighted least squares(RWLS) method, which depends on the current and (to some extent) previous data. The identification fitnessis found for various configurations with different polynomial orders, and the best model fitness is obtainedabout 98% according to normalized root mean square criterion for a third order polynomial

A simple upper limb rehabilitation trainer

Stroke is a leading cause of disability which can affect shoulder and elbow movements which are necessary for reaching activities in numerous daily routines. To maximize functional recovery of these movements, stroke survivors undergo rehabilitation sessions under the supervision of physiotherapists in healthcare settings. Unfortunately, these sessions may be limited due to staff constraints and are often labor-intensive. There are numerous robotic devices which have been developed to overcome this problem. However, the high cost of these robots is a major concern as it limits their cost-benefit profiles, thus impeding large scale implementation. This paper presents a simple and low cost interactive training module for the purpose of upper limb rehabilitation. The module, which uses a conventional mouse integrated with a small DC motor to generate vibration instead of any robotic actuator, is integrated with a game-like virtual reality system intended for training shoulder and elbow movements. Three games for the module were developed as training platforms, namely: Triangle, Square and Circle games. Results from five healthy study subjects showed that their performances improved with practice and time taken to complete the Triangle game was the fastest of the three

The impact of social interaction anxiety in the use of learning management system: a tentative model

Learning Management System is a relatively new phenomenon and until very recently their use in University was limited, now the lecturers and the students have been push to use this system so that they have a better and improvement in learning situation. The Learning Management System is already changing the organization and delivery of higher education. The pedagogical forces that have driven the higher learning institutions to adopt and incorporate ICTs in teaching and learning include greater information access and greater communication. This concept paper is observes the impact of Learning Management System usage among university students. It is also looks at the moderating effect of Learning Management System usage between Offline Social Interaction and Social Interaction Anxiety. This research becomes a concern because a significant relationship social interaction anxiety was found between the level of problematic Internet use and social interaction anxiety, (Cem Cuhadar, 2011). The paper observes the Anxiety/Uncertainty Management (AUM) Theory, developed mainly by William Gudykunst (1995)

A Non-Destructive Oil Palm Fruit Freshness Prediction System with Artificial Neural Network

The economical, rapid, and non-destructive method using reflectance Near-Infrared Spectroscopy (NIRS) technique were designed and developed for oil palm fruit (Elaeis Guineensis) freshness prediction. The ripe maturity oil palm freshness of Tenera variety was used for this study by two consecutive days. The measurement of spectral value was obtained with a linear array sensor. The Artificial Neural Network (ANN) was trained with Levenberg-Marquardt algorithm by using half of the data sets, quarter for validation and the rest quarter for test purpose. The performance of the oil palm fruit freshness prediction system was evaluated. Results indicate that the ANN with 6 hidden neurons achieved the best prediction accuracy with root mean squared error (RMSE) and the correlation coefficient (R) were 6.8449 hours and 0.8418 respectively. This suggests that the proposed method is promising to be further developed to automate oil palm freshness inspection

A Simple Position Sensing Device for Upper Limb Rehabilitation

Stroke is a leading cause of disability which can affect shoulder and elbow movements which are necessary for reaching activities in numerous daily routines. Rehabilitation under the supervision of physiotherapists in healthcare settings is to encourage the recovery process. Unfortunately, these sessions are often labor-intensive and limited intervention time between physiotherapist and the stroke patient due to staff constraints. Dedicated robotic devices have been developed to overcome this issues. However, the high cost of these robots is a major concern as it limits their cost-benefit profiles, thus impeding large scale implementation. This paper presents a simple and portable unactuated interactive rehabilitation device for upper limb rehabilitation purposes. This device has been developed by using a conventional mouse integrated with three interactive training modules, namely the Triangle, Square, and Circle modules intended for training shoulder and elbow movements. Results from five healthy subjects showed the more deviation from the path will be happened when the subject move their hand to the other side of their dominant hand. Besides, the shape of the module that includes combination of X and Y axis direction is more difficult compare to either X or Y axis

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Various predictive models, both linear and non-linear, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Artificial Neural Network (ANN), were frequently employed for predicting the clinical scores of stroke patients. Nonetheless, the effectiveness of these predictive models is somewhat impacted by how features are selected from the data to serve as inputs for the model. Hence, it's crucial to explore an ideal feature selection method to attain the most accurate prediction performance. This study primarily aims to evaluate the performance of two non-motorized three-degree-of-freedom devices, namely iRest and ReHAD using MLR, PLS and ANN predictive models and to examine the usefulness of including a hand grip function with the assessment device. The results reveal that ReHAD coupled with non-linear model (i.e. ANN) has a better prediction performance compared to iRest and at once proving that by including the hand grip function into the assessment device may increase the prediction accuracy in predicting Motor Assessment Scale (MAS) score of stroke subjects. Furthermore, these findings imply that there is a substantial association between kinematic variables and MAS scores, and as such the ANN model with a feature selection of twelve kinematic variables can predict stroke patients' MAS scores

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Various predictive models, both linear and non-linear, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Artificial Neural Network (ANN), were frequently employed for predicting the clinical scores of stroke patients. Nonetheless, the effectiveness of these predictive models is somewhat impacted by how features are selected from the data to serve as inputs for the model. Hence, it's crucial to explore an ideal feature selection method to attain the most accurate prediction performance. This study primarily aims to evaluate the performance of two non-motorized three-degree-of-freedom devices, namely iRest and ReHAD using MLR, PLS and ANN predictive models and to examine the usefulness of including a hand grip function with the assessment device. The results reveal that ReHAD coupled with non-linear model (i.e. ANN) has a better prediction performance compared to iRest and at once proving that by including the hand grip function into the assessment device may increase the prediction accuracy in predicting Motor Assessment Scale (MAS) score of stroke subjects. Furthermore, these findings imply that there is a substantial association between kinematic variables and MAS scores, and as such the ANN model with a feature selection of twelve kinematic variables can predict stroke patients' MAS scores