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Simulation is widely used in Medical Education as a teaching and learning method. The purpose of this paper is to discover the implication of the simulator into clinical trainee behaviour, medical education, and patient safety. At the other hand, a methodology of quantitative research design towards implementing Upper Limb Spasticity Simulator (ULSS) named BITA1.0 is discussed. The Descriptive Quantitative Research design is focused on formative clinical assessment with students of Master in Rehabilitation, Universiti Teknologi MARA as subjects with pre and post-response test. With the intention of implementing BITA1.0 into medical education, the result from The Descriptive Quantitative Research is essential. Keywords: simulation; spasticity; upper limb; medical education eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5iSI3.2552

Providing optimal rehabilitation services to the broad public is one of the greatest challenges in the healthcare sector due to the shortage of rehabilitation physicians and facilities. Recent advances in digitalization and sophisticated data analytics offers new innovative ways in delivering rehabilitation services to enhance the quality of life of people with disabilities. Currently, the development of data-driven solutions for rehabilitation in Malaysia is limited due to multiple factors: medical and rehabilitation data is not digitally stored; the knowledge for the interpretation of clinical data is distributed; in particular, there is a lack of expertise in the field of medical data science. Thus, a data science platform is proposed so that medical expertise can be made available through digital services and is not dependent on human resource, location, time or financial ability. This platform is applied for the smart diagnosis of upper limb spasticity in compliance with clinical practice, and extensible for the other smart rehabilitation applications. By collaborating with the prestigious Fraunhofer Society, their knowhow in industrial data science can be brought to Malaysia towards improved health care in the country. The smart diagnosis system provides advice in classifying the severity level of upper limb spasticity based on the Modified Ashworth Scale and the Modified Tardieu scale. The basis is a measurement system for muscle signal, muscle tone and elbow motion. Users of the smart diagnosis application include rehabilitation physicians, doctors from other specialities, nurses, psychologists, physiotherapists and occupational therapists. The tools provided by the data science platform is deployed to store and analyze the clinical data. Further, the expertise of a rehabilitation physician is emulated in the form of an expert system to determine the severity level of upper limb spasticity. The digital clinical database helps medical researchers in secondary analysis towards knowledge discovery for the betterment of intervention and treatment of spasticity.

Optimizing Resistance spot welding (RSW), often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg-Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of graphical user interface (GUI) using Qt designer and PyQt5. The standalone capability was created with customized icon by using Pyinstaller to enable a self-execution in windows platform. Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.

The Machine Learning Model (MLM) has garnered popularity in rehabilitation, ranging from developing algorithms in outcome prediction, prognostication, and training artificial intelligence. High-quality data plays a critical role in algorithm development. Limited studies have explored factors that may influence the MLM algorithm performance in predicting spasticity severity level. The objectives of this study were to train and validate a MLM algorithm for spasticity assessment and determine the algorithm's prediction performance in predicting ambiguous spasticity datasets. Forty-seven persons with central nervous system pathology that fulfilled the inclusion and exclusion criteria were recruited. Four biomechanical properties of spasticity were obtained using off-the-shelf wearable sensors. The data were analyzed individually, and ambiguous datasets were separated. The acceptable inertial data were used to train and validate MLM in predicting spasticity. The trained and validated MLM algorithm was later deployed to predict the ambiguous spasticity datasets. A series of MLM were applied, including Support Vector Machine, Decision Tree, and Random Forest. The MLM's performance accuracy of the validation data was 96%, 52%, and 72%, respectively. The validated MLM accuracy performance level predicting ambiguous datasets reduces to 20%, 23%, and 23%, respectively. This study elucidates data biases and variances of disease background, pathophysiological and anatomical factors that have to be considered in MLM training.

Conventional ankle rehabilitation exercises can be monotonous and repetitive. The use of robots and games can complement the existing practices, provide an engaging environment for the patient and alleviate the physiotherapist’s workload. This paper presents an ankle rehabilitation robot that uses two nitinol wire actuators and a Pong game to provide foot plantarflexion and dorsiflexion exercises. Nitinol is a type of smart material that has high volumetric mechanical energy density and can produce translational motion. A two-state discrete antagonistic control is proposed to manipulate the actuators. The system was tested on healthy participants and stroke patients. The results showed that the robot was safe and compliant. The robot did not forcefully plantarflex or dorsiflex the foot when the participant exerted opposing force. The actuators worked antagonistically to flex to the foot as intended, in sync with the up and down motions of the player’s bat in the game. These behaviors demonstrated the feasibility of a nitinol-based ankle rehabilitation robot and a simple and yet intuitive game in providing interactive rehabilitation exercise. The robot is expected to enhance the patient’s experience, participation and compliance to the rehabilitation routine and to quantitatively monitor the patient’s recovery progress.

Anomaly detection algorithms have vast applications, from fraud detection in business transactions to rare pattern detection in a production line to help prevent machinery failures. The availability of quantitative clinical data makes a compelling case for using anomaly detection algorithms in clinical settings, for instance, to help prevent diagnosis errors. This work evaluates the feasibility of using Isolation Forest algorithm for detection of spikes in surface electromyography (sEMG) of biceps and muscle resistive force in upper limb spasticity datasets. Results show that the anomaly detection in sEMG data is a good predictor for the occurrence of catch. It could be deployed in rehabilitation robotic systems for injury prevention by linking the anomaly detection to the actuation module exerting force in the system.

Upper limb spasticity (ULS) is a common pathophysiological changes manifest by a structural damage towards the central nervous system (CNS) that includes brain and spinal cord. The current clinical practice of spasticity assessment utilizes Modified Ashworth Scale (MAS) as a subjective tool to measure the severity of spasticity. Lack of objective value, poor sensitivity in detecting minimal changes, and dependency to the interpretation by the assessing clinicians are the several reasons of the inter and intra-rater variability of the measurement using MAS. These limit the use of MAS in diagnosing, treating, and monitoring spasticity especially in inexperienced clinicians, hence leading to inadequate spasticity management. To overcome this problem, a study is carried out to quantify and develop a data-driven model of ULS detection based on MAS. The characteristics that detect the existence of ULS according to MAS are identified and adopted to train the machine learning models for smart diagnosis purpose to assist the physicians to effectively manage spasticity.

Smart Health means innovation of the health sector through digitization. This leads to many possibilities regarding to an effective home-based, self-managed physical rehabilitation. With an innovative Product-Service-System (PSS), patients will have a better access, availability, and affordability of physical rehabilitation. In this contribution we present generic approaches of upgrading existent systems from a technological perspective. Furthermore, we introduce a framework for the agile development of innovative PSS, which could be adapted to other fields. The initial situation is an existing PSS for physical rehabilitation. The framework covers four phases. The planning phase consists of methods from the value based idea creation to Systems Engineering (SE) approaches, which focus on the conception of a Minimum Viable Product (MVP). The second phase describes a fast prototyping of the previously defined MVP, which addresses the PSS concept and the business model. Based on that, the next phase consists of the validation of the developed prototype and business model by real customers. The result is analyzed in the fourth stage. The learnings are interpreted regarding to the real market demand for the developed PSS. The developed PSS will be improved or completely changed through multiple iterations.

Industry 4.0 is changing design and manufacturing methodologies because future production lines will manufacture customized products for the mass market. As for that, Automated machines and systems in such a production line need to communicate with each other. In this context, data exchange takes place between different kind of systems or platforms. Therefore a system level approach clarifying the interactions between the manufacturing devices in heterogeneous environments addressing highly dynamic and often loosely coupled production processes is required. This paper describes a model-based systems engineering approach of which the system integration of a smart factory is systematically managed right from the conception design phase. During the conception phase, the communication flows between the system elements is specified using CONSENS (Conceptual Design Specification Technique for the Engineering of Complex Systems). Based on the system level design specifications, a working prototype of a mini production line deploying Supervisory Control and Data Acquisition (SCADA) technology has been developed. The SCADA software acts as a human-machine interface and provides a gateway to access data of the production line either locally or through the internet. Multi-role users could access the system locally or remotely for monitoring, controlling, analyzing and ordering using SCADA as the interface.

In Brain-Computer Interfaces (BCI), data sets need consistency factor to train good filters and classifiers. If there is lack of pattern in the data sets, it will affect the classification accuracy of the results. Consistency in extracted data-epoch requires precise markers to be embedded along the continuous Electroencephalography (EEG) data collection process. This study suggests placing the markers on actual movement through Electromyography (EMG) data classifications during isometric finger flexion and extension. An EMG-based marker system is applied to generate marker in EEG signal during data measurement in real time. EEGLAB software is used for pre-processing. The data loaded into the BCILAB interface for chosen feature extraction and classification. Spectrally weighted Common Spatial Pattern (Spec-CSP) used as feature extraction method and Linear Discriminant Analysis (LDA) is implemented for classification process. Results show that EMG-based marker approach shows higher potential success rate, at 73.6% compared to visual cue-based marker at 71.1%. It is suggested that EMG-based marker approach is applicable in finding pattern recognition of EEG data in isometric finger flexion and extension.

Prosthetic hand acts as a tool that enables the amputee to perform daily tasks. Instead of passive devices which are aesthetically pleasing, current devices come with improved functionality utilizing robotic technology. There are various ways to control a prosthetic hand. One of it includes Brain Computer Interface (BCI) which has advanced technologies and creates new possibilities in providing healthcare solutions for people with severe motor impairment. Generally, Electroencephalography (EEG) is dominated by BCI researchers as it provides non-invasive, inexpensive equipment, good resolution, ease of use, portability and no implantation approaches. EEG signals were recorded from healthy subjects through brain waves at specific locations on the scalp using Emotiv EEG Headset. This device can capture brain waves that include artifacts such as movement of a limb, respiration, speech, heartbeat and many more. Most artifacts will disturb the data, but artifacts can also be used for researchers to control the prosthetic hand. This paper shows that this technology is still undergoing a maturing process. In this work, the state-of-the-art is reviewed and a conceptual design for a non-invasive approach for controlling a prosthesis hand using Emotiv EEG Headset integrated with Graphical User Interface (GUI) is presented and designed using LabVIEW. This project is designed for applying cost-effective prosthetic hands controlled using signal artifacts. This device can be used and controlled by paralyzed individuals with limited communication ability using simple GUI.

The application of a high-fidelity part-task trainer for clinical education on upper limb disorders allows students to carry out recurring practices, gain skills and build up their confidence prior to engaging with real patients. The standardization of the symptoms assessment assists in the improvement of clinical findings which are not just limited to spasticity, but also leadpipe rigidity and cogwheel rigidity. The deployment of the Beckhoff PC-based control technology enables high precision in simulating spasticity characteristics thus increasing trainee proficiency. With the growing number of medical schools and therapy training centers, it solves the dilemma of patient unavailability and the difficulty of obtaining patient assistance for the preparation of apprenticeship training.

Multilayer nanofibrous scaffolds that mimic the microstructure gradient of connective tissues have shown promising results in tissue regeneration, but the effect of these gradients on the mechanical performance of fiber meshes is poorly understood. In this study, trilayer nanofibrous gelatin scaffolds with gradually increasing fiber diameters (227–679 nm) and pore sizes (1.14–4.93 μm2) were fabricated using a sequential electrospinning process, producing a fiber density gradient over the scaffold thickness. The mechanical properties of the fibrous scaffolds were evaluated using uniaxial tensile and fracture tests. Deformation of microscopic crack tip openings was simulated using finite element analysis. Results from uniaxial and fracture tensile tests showed that the mechanical properties of fibrous scaffolds were governed by network architectures. The microstructure gradient yielded corresponding changes of material properties over the scaffold thickness. Simulation results showed different stress distribution and energy dissipation at each layer of the graded scaffolds. Finite element analysis also revealed that a combination of network density and alignment gradients can improve fracture toughness of graded scaffolds. This study provides guidelines and methodologies for designing tailored gradient fibrous scaffolds to more closely mimic the structural and mechanical properties of native interfacial tissues. Keywords: Functionally graded materials, Multilayer fibrous scaffolds, Electrospinning, Tissue engineering, Finite element analysis, Mechanical properties

Data in this article are supplementary to the corresponding research article [1]. Morphological features of homogeneous and graded nanofibrous electrospun gelatin scaffolds were observed using scanning electron microscopy. Microstructural properties including fiber diameter and pore size were determined via image analysis, using ImageJ. Uniaxial tensile and fracture tests were performed on both homogeneous and graded scaffolds using a universal testing machine. Stress-strain curves of all scaffolds are presented. Computing software, MATLAB, was used to design fibrous networks with thickness-dependent density and alignment gradients (DAG). Finite element analysis software, Abaqus, was used to determine the effect of the number of layers on the fracture properties of DAG multilayer scaffolds. Keywords: Electrospinning, Gelatin scaffold, Thickness, Tensile properties, Fracture properties, Finite element analysis, Functionally graded materials, Fibrous materials

One of the most frequent cause of limb disabilities worldwide is stroke. In order to achieve high recoverability, stroke patients require repetitive and consistent rehabilitation treatment. However, the increasing number of stroke patients with limited number of therapists and transportation problem among patients from rural places limit the accessibility to have better treatment. Hence, it is important to have a Rehabilitation Robot Devices (RRDs) to minimize the problems. This research is aimed to design an upper limb rehabilitation robot device that is portable and adjustable. In order to ensure that the robot arm design achieve the desired movement, CATIA V5R22 was used to simulate the design with a human builder. Motion analysis in SolidWork 2018 was used to investigate the angular displacement of the robot arm link. The simulation showed that, the robot arm design corresponds with the Malaysian anthropometry dimensions. The motion analysis showed that, the movement pattern of the robot arm achieved the desired angle of movement during rehabilitation process with the wrist extension recorded the highest time cycle which is 16 seconds for 45 degrees of extension.

A sense of touch is essential for amputees to reintegrate into their social and work life. The design of the next generation of the prostheses will have the ability to effectively convey the tactile information between the amputee and the artificial limbs. This work reviews non-invasive haptic feedback stimulation techniques to convey the tactile information from the prosthetic hand to the amputee’s brain. Various types of actuators that been used to stimulate the patient’s residual limb for different types of artificial prostheses in previous studies have been reviewed in terms of functionality, effectiveness, wearability and comfort. The non-invasive hybrid feedback stimulation system was found to be better in terms of the stimulus identification rate of the haptic prostheses’ users. It can be conclude that integrating hybrid haptic feedback stimulation system with the upper limb prostheses leads to improving its acceptance among users.

The use of body-powered prostheses can be tiring and lead to further problems with compliance and prosthetic restoration. Brain-Computer-Interfaces (BCI) offers a mean of controlling prostheses for patients that otherwise are unable to operate such devices due to physical limitations. An issue with BCIs is that they tend to either require invasive recording methods, posing a surgical risk, or work by generating control signals from not task related brain activity patterns such as right vs. left hand, hand vs. leg or visual stimulation and therefore are not intuitive in their control. This research aims to test the possibility of controlling the grasp and release of an upper limb prosthetic terminal device by classifying Electroencephalogram (EEG) data from real hand grasping and releasing movement. Data from five healthy subjects were recorded using a consumer grade non-invasive Emotiv EPOC headset. During the measurement the subjects were asked to perform isometric finger extension and flexion of their right hand. In order to bring the EEG data into correlation with the executed movement a simultaneous electromyogram (EMG) recording is proposed as an alternative method to recordings of visual cued movement. Classified EMG data was used to generate markers in the EEG data and to epoch the data. In order to increase the signal to noise ratio and allow better classification, the EEG data was filtered and spectrally weighted common spatial patterns (spec-CSP) were used for feature extraction. Using linear discriminant analysis a classification rate of up to 73.2% between grasp and release was achieved. In this work, a novel EMG-assisted approach has been developed for classification of EEG data from hand grasp and release movements. It shows feasibility for a more intuitive control of upper limb prosthetic terminal device using low-cost BCI without the risk associated with invasive measurement.

Healthcare providers in the field of physical and rehabilitation medicine play a vital role to help patients suffering spasticity readapting themselves to their normal daily activities. Mathematical modeling of spasticity has the potential to avoid the issue of variability in the assessment of spasticity using the Modified Ashworth scale (MAS). In this work, an existing mathematical model for upper limb spasticity is verified using clinical data sets of upper limb spasticity collected in Malaysia at the level of MAS 1+. The data set consists of torque values measured at each elbow angle as the elbow extends from a full flexion position to a full extension position during slow and fast stretch of the forearm. The aim is to find out the capability of the mathematical model and lay a foundation for the future work on data-driven modeling of upper limb spasticity based on the Modified Ashworth Scale.

Providing sensory feedback for prostheses users increases the manipulation ability, as well as introduces a feeling of the embodiment. A novel hybrid haptic feedback stimulation system of the amputees of upper limb mutilation was designed, developed, and evaluated in this study. The haptic wearable device was built by mean of single servomotor as a pressure feedback display and a pair of vibration motors as a vibration feedback display. Accordingly, the pressure display is responsible for conveying the missing sensing of the contact pressure and its force level. While the vibration display has utilized to provide an indication about the continued contact pressure. Able-body participants are engaged to evaluate the performance of the proposed feedback stimulation system. The results show that the participants are able to distinguish the touch, the start of touch, the end of touch, and the range of force with 100 % accuracy. While 96 % and 88 % of the able-body participants are capable to identify the grasp and the slipping objects, respectively. Furthermore, the ability of the proposed haptic wearable device to convey the tactile sensory information to the user’s brain without confusing or pre-training was improved.

The development of a robotic exoskeleton to restore and rehab, hand and finger function is highly competitive nowadays. The robotic exoskeleton is an active actuated mechanism implemented in rehabilitation system, in which each finger attached to an instrumented lead screw mechanism allowing force and position control, according to the normal human setting. The robotic device is a direct driven actuated based on ergonomics measurements, capable to assist in flexion and extension motion. As an adaptation mechanism, it's also compatible with various sizes and shapes of anthropometric human‘s finger. The integration of DC servo motor and lead screw mechanism were the main features of the interface, which allows independent motion of the five fingers with small and lightweight actuators. The device is easily transportable, efficient safety performance, user friendly and offer multiple modes of training potentials. This paper presents the measurements implemented in the system to determine the requirements for finger and hand rehabilitation device, the design and characteristic of the whole system.

Functional recovery of upper limb after stroke is crucial to restore the ability to perform activities of daily living (ADL). This paper presents a robotic rehabilitation approach based on repetitive exercise aimed to help stroke survivors relearn the skills of finger flexion and extension at the comfort of their home. The finger rehabilitation device deploysShape Memory Alloy(SMA) wires as an actuation approach to deliver three degrees of freedom per finger module. The advantages and challenges of using SMA wires rather than conventional actuators are discussed. A prototype of the finger rehabilitation device was built using PLA material and experiments have been conducted for the purposes of feasibility study. Tests conducted on the wires suggest that it has to have sufficient weight and also has to be stretching at high temperature rather than room temperature in order to have an optimum range of recovery.

Friction stir welding (FSW) is a relatively new solid-state joining process which is considered energy efficient, eco-friendly and versatile. High stress and strain occur at the rotating tool, consisting of a pin (probe) and a shoulder, during the friction stir process. The geometrical design of the tool has some impact in terms of stress and strain once static load is applied against the tool. In this work, specific stress can be found on the tool due to the plunging and travel process that is analysed using finite element method. In the present work, a steady state finite element stress analysis of friction stir welding was carried out using CATIA V5 software. The critical points of the FSW tool are located mainly on the edge between the shoulder and the pin, where a large amount of stress is found and further leads to failure or tool defects. This critical stress and strain can be reduced by enlarging the diameter size of the pin and increasing tool life.

The applicability of shape memory alloy as an actuator for robotic hands is tested. A parallel arrangement of SMA wires along the forearm is implemented into a hand model after consideration of different designs. The activation of the SMA is achieved using a joule heating approach. The arrangement is tested for its controllability using open and closed loop control. An adaptive PID controller shows best results for the full range of motion.