7.1 Introduction

Stroke and cardio vascular diseases have a high incidence in countries such as: Kingdom of Saudi Arabia, Egypt and Germany, Romania, China and USA. Beside the early detection of high-risk persons, their monitoring and the detection critical, deathtrap events, their effective emergency management the rehabilitation process is difficult and cost intensive.

Stroke is an urgent case that may cause problems like weakness, numbness, vision problems, confusion, trouble walking or talking, dizziness and slurred speech. It may also cause sudden death. It is a leading cause of death in the United States. For these reasons, brain stroke is considered an emergency case as same as heart attack and needs to be treated immediately before causing more problems.

Although stroke is a disease of the brain, it can affect the entire body. A common disability that results from stroke is complete paralysis on one side of the body, called hemiplegic. A related disability that is not as debilitating as paralysis is one-sided weakness or hemi paresis. Many stroke patients experience pain in legs and hands. Therefore, patients’ case emergency for pre-stroke detection as well as post-stroke rehabilitation treatment is very important for long time recovery and overall patient health management. Therefore, in this project we three main targets, first is patient emergency and stroke early detection through mobile health technology and then second phase we aim to address patient post-stroke rehabilitation through our new innovative design of rehabilitation robotics controller.

In first phase, we want to implement and develop a complete product through research and development of Mobile health system, Mobile Health in remote medical systems has opened up new opportunities in healthcare systems. Mobile Health is a steadily growing field in telemedicine and it combines recent developments in artificial intelligence and cloud computing with telemedicine applications. For these reasons, brain stroke is considered an emergency case as same as heart attack and needs to be treated immediately before causing more problems. In the recent research, what we witness is a high competition and new revolution towards mobile health in general, especially in field of chronic illnesses and emergency cases like heart attack and diabetics. However, today’s Mobile Health research is still missing an intelligent remote diagnosis engine for patient emergency cases such as Brain Stroke. Moreover, Remote patient monitoring and emergency cases need intelligent algorithms to alert with better diagnostic decisions and fast response to patient care. This research work proposes a Hybrid Intelligent remote diagnosis technique for Mobile Health Application for Brain Stroke diagnosis.

Mobile Health in remote medical systems has opened up new opportunities in healthcare systems. It combines recent developments in artificial intelligence and cloud computing with telemedicine applications. This technology help patients manage their treatments when attention from health workers is costly, unavailable, or difficult to obtain regularly.

In fact remote monitoring - which is seen as the technology with the highest financial and social return on investment, given current healthcare challenges - is a focus for many of the pilot projects.

Mobile Health for patient tracking supports the coordination and quality of care for the benefits of rural communities including the urban poor, women, the elderly, and the disabled. This would promote public health and prevent disease at the aggregate level.

Some stroke disorders affect the nerves (e.g. Stroke) and cause problems with thinking, awareness, attention and lead to emotional problems. Stroke patients may have difficulty controlling their emotions or may express inappropriate emotions. So that brain stroke is considered an emergency case that needs to be treated immediately before causing more problems.

In the first phase of the proposed research proposal aims to develop a new intelligent mobile health applications based on new artificial intelligent technologies in the field of brain stroke by proposing an intelligent mobile health application based on EMG sensor which provides a significant source of information for identification of neuromuscular disorders.

In final (second) phase of the research, we want to develop a new innovative robotics controller for patient’s rehabilitation. The rehabilitation points towards the intense and repetitive movement assisted therapy that has shown significant beneficial impact on a large segment of the patients. The availability of such training techniques, however, are limited by:

1. The amount of costly therapist’s time they involve,
2. The ability of the therapist to provide controlled,
3. Quantifiable and repeatable assistance.

These limitations are quite important in Saudi Arabia. Rehabilitation robotics systems are a very important problem, especially in the therapeutic domain of stroke patients. This is due to:

1. The complexities of patients’ treatments procedures such as physiotherapy
2. Since Electromyography (EMG) detects muscle response during different actions, it gives useful identification of the symptoms’ causes. Such disorders that can be identified by EMG are neuromuscular diseases, Nerve injury, and Muscle degeneration. The dealing with Electromyography (EMG) signals provides significant source of information for identification of neuromuscular disorders.
3. A robot-assisted rehabilitation can provide quantifiable and repeatable assistance that ensure consistency during the rehabilitation and
4. A robot-assisted rehabilitation is likely to be cost-efficient.

Rehabilitation robotics refers to the use of robotic devices (sometimes called ”rehabilitators“) that physically-interact with patients in order to assist in movement therapy.

Rehabilitation robotics is directed to improve mobility and independence in daily life of patients. It uses specific ex-excises related to the therapeutic problem and patients practice movements. The rehabilitation robotics controls this automatically. The pattern of movements follows a theoretical concept developed and disseminated by respected authorities. However, now the proof of evidence for each concept is missing. Especially, no validated data to compare different therapeutic strategies are missed. The health economical demand is to demonstrate the effectiveness of robotics and rehabilitative procedures [1].

Two important issues that the current robot-assisted rehabilitation systems do not address: they are limited by their inability to simultaneously assist both arm and hand movements (signal evaluation and robot steering is quite complicated using signals from arm, hand or body (head, neck, shoulder). Current robot-assisted rehabilitation systems can comprehensively alter with limits the task parameters based on patient’s feedback to impart effective therapy during the execution of the task in an automated manner.

Moreover, the third important problem of current robot-assisted rehabilitation systems is intelligent robot control. Behavior control for an autonomous robot is a very complex problem, especially in the rehabilitation and medical domains. This is due to the dynamics of patients muscle movements and real-time EMG patient signal feedback.

7.2 Research Chapter Objectives

Stroke is an urgent case that may cause problems like weakness, numbness, vision problems, confusion, trouble walking or talking, dizziness and slurred speech. It is a leading cause of death in the United States. For these reasons, brain stroke is considered an emergency case as same as heart attack and needs to be treated immediately before causing more problems.

The main objective of the proposed research is to propose a Hybrid Intelligent remote diagnosis Technique for Mobile Health Application for Brain Stroke diagnosis. Another objective is monitoring human health conditions based on emerging wireless mobile technologies with wireless body sensor.

The research work focuses also on delivering better healthcare to patients, especially in the case of home-based care of chronic illnesses.

On the other hand, our designed prototype investigates the implementation of the neural network on mobile devices and tests different models for better accuracy of diagnosis and patient emergency.

Integration of mobile technology and sensor in development of home alert system (mhealth system) will greatly improve the lives of elderly by giving them safety and security and preventing minor incidents from becoming life-threatening events.

7.3 Literature Review

7.4 Description of the Research Telemedicine Platform

The target of this project is the development of an intelligent hybrid rehabilitation robot controller based on a Telemedical platform for a portable rehabilitation robot monitor system. The Telemedical platform allows to manage the monitoring of high-risk patients of cardio-vascular diseases, detect critical events and control the rehabilitation process using wireless sensors and robots. The proposed system consists of:

1. Various wireless sensors, used in an adaptable, scenario based setting.
2. A mobile processing unit for signal processing and feature extraction.
3. A mobile device as data transmitter controller.
4. A Robot controller unit for intelligent behavior control of the robot.
5. A robotic arm unit for interaction with the patient.

7.4.1 A State of the Art Telemedicine Robot Rehabilitation System

Stroke is a leading cause of disability in the world, and yet Robot-assisted and telemedicine technology is currently available for individuals with stroke to practice and monitor rehabilitation therapy on their own. Telemedicine uses common technologies that provide conduit for tele-consultation exchange between physicians, nurses and patients. The third phase of our proposed product is to develop a hybrid rehabilitation robot controller and a telemedicine in a portable rehabilitation robot monitor system with 3-D Exercise Machine for Upper Limb, coordination, range of motion and other relevant perceptual motor activities. The aim of this study is to evaluate a device for robotic assisted upper extremity repetitive therapy; the robot will have four degrees of freedom at shoulder, elbow and wrist; the robot EEG and EMG sensors feedback position and force information for quantitative evaluation of task performance. It has the potential of providing a repetitive automatic of supplementing therapy. The telemedicine system will consist of a Web-based library of status tests and Single Board computer Monitor, and can be used with a variety of input devices, including a feedback joystick, infrared emitter sensor Bar to integrated therapy games Stepmania and Wii, assisting or resisting in movement. The system will enable real-time, interactive integration of medical data, voice and video transmission in the wireless Telemedicine environment.

Robot-assisted therapy refers to the use of robotic devices (sometimes called rehabilitators) that physically-interact with patients in order to assist in movement therapy [6, 7]. Virtual reality (VR) is an emerging and promising approach for task-oriented biofeedback therapy [8, 9] Embedded telerehabilitation system used virtual reality and a pair of wireless networked PCs. It is intended for rehabilitation of patients with hand, elbow, and shoulder

Figure 7.1. Shows the full system units, wireless telemedicine unit, signal processing and feature extraction units, robot controller unit system, along with wireless sensors that consist of EEG, ECG and EMG sensors along with telemedicine server. Mobile device and robotic arm.

This model reflects not only the intelligent robotic control as only one aspect of the problem but also the monitoring of high-risk patients and covers the whole process of patients with cardio-vascular diseases and stroke. It also reflects the mobile signal processing and feature extraction unit along with the Intelligent Behavior Controller of the Robotics unit to alter real-time patients’ feedback to impart effective therapy during the execution of the task in an automated manner.

Figure 7.2 shows the details description of the Intelligent Behavior controller of robotic unit using case-based reasoning (CBR) and neural networks, which are recent and important Artificial Intelligence technologies. Also, due to the integration of mobile devices such as cell phones and tablet pc mobile network operators can offer an additional service of monitoring and rehabilitation management. First consultations with Egyptian providers showed their deep interests for such a telemedical management system including additional values such as satisfaction of secure life data management, crisis intervention and rehabilitation improvement by individualization of the therapeutic interaction and intervention.

7.5 A proposed intelligent adaptive behavior control to rehabilitation robotics

Behavior-based control [1] has become one of the most popular approaches to intelligent robotics control. The robot’s actions are determined by a set of reactive behaviors, which map sensory input and state to actions. Despite of the behavior-control part, most of robotics systems use classical behavior-control architectures. These classical architectures can cover all sensory input states of complex environments and thus limits the robot ability to adapt its behaviors in unknown situations. Recently, some AI techniques such as neural networks, neural networks have been applied successfully to behavior-control of mobile robots [4]. However, research on control of rehabilitation robots using AI is still in initial stage [10].

Figure 7.2 An Intelligent Behavior Controller Software Architecture to Rehabilitation Robotics. As shown This architecture presents an intelligent behavioral control model that depends on case-based reasoning. It consists of a hierarchy of four levels, the first level is to decide robot role. The second level is to decide which skill to execute. The third level is to determine the behaviors of each skill and the fourth level is to adapt lower-level behaviors as distance and angels of motions. We have designed this architecture before for German team robot, humanoid soccer [1] and we want to apply it as the main intelligent controller of rehabilitation robot because it shows successful results [2].

1. As shown, each level applies CBR cycle to control and adapt its behaviors.
2. The first two phases apply adaptation rules to adapt behaviors.
3. The last two phases apply the learning capabilities of NN to learn adaptation rules for performing the main adaptation task.

Case-Based Reasoning (CBR) suggests a model of reasoning that depends on experiences and learning. CBR solves new cases by adapting solutions of retrieved cases. Recently, CBR is considered as one of the most important Artificial Intelligent (AI) techniques used in many medical diagnostics tasks and robotics control.

Adaptation in CBR is a very difficult knowledge-intensive task, especially for Robot control. This is due to the complexities of the robot kinematics, which may lead to uncertain control decisions. In this work, we will propose a new hybrid adaptation model for behavior control of Rehabilitation Robot. It combines case-based reasoning and neural networks (NN’s). The model consists of a hierarchy of four levels that simulates the behavior control model of a patient’s motions robot. Each level applies CBR cycle to control and adapt its behaviors. The first two phases will apply adaptation rules to adapt behaviors, while the last two phases will apply the learning capabilities of NN to learn adaptation rules for performing the main adaptation task. The detailed Software Algorithm of the Intelligent Behavior control is shown in Figure 7.3.

7.6 Materials and Methods

The telemedical platform covers the process of monitoring, signal processing, and management of telemedical care. The following Figure 7.4 shows the general process of signal processing and feature extraction and interaction with the patient.

The clue is the distributed, level based sensor data evaluation process: the first level includes the sensor nodes themselves with a basic but very fast signal processing. Aggregated data will be sent to the mobile unit/device as second level, this will take real-time (EMG) data read through the mobile device which sends urgent event to the hospital server as shown in Figure 7.5. The system can also respond by immediate recommendation and sends patient data to responsible doctor or nurse. Moreover, the next processing step can be done. The second and third level (server/cloud based signal processing) covers intelligent data processing and decision support for interaction and robot control.

7.7 Conclusion Summary: Artificial Intelligence Technologies

First step in our system is Signal Acquisition phase. EMG wireless sensors include high performance analog filters for signal acquisition, anti-aliasing and instrumentation noise management. Second step is Signal Pre-processing which means noise removal depending on noise type by applying some typical filtering techniques like band-pass filter, band-stop filter and then applying wavelet transform method. Third step is features extraction. This step is divided into two phases. First of them is analyzing data of Brain Stroke based on EMG sensors of muscles readings to enable extracting best features. Second phase is to select significant features for efficient classification since it determines the success of the pattern classification system. However, it is quite problematic to extract the best feature parameters from the EMG signals that can reflect the unique feature of the signal to the motion command perfectly. Hence, multiple feature sets are used as input to the EMG signal classification process. Some of the features are classified as time domain, frequency domain, time-frequency domain, and time-scale domain; these feature types are successfully employed for EMG signal classification. The next step is signal classification phase. Artificial Intelligence techniques mainly based on machine learning have been proposed for EMG signal classification. This technique is very useful for real-time application based on EMG signal. Classification step in our system is divided into four phases. First of them is to study and analyzing Neural Networks (NN) algorithms for EMG Data. Support Vector Machine (SVM) is a powerful learning method used in binary classification. The next phase is to analyze Case-Based Reasoning Retrieval Algorithms in Medicine. Case-Based Reasoning (CBR) suggests a model of reasoning that depends on experiences and learning. CBR solves new cases by adapting solutions of retrieved cases. The four processes of CBR Cycle [13] (Retrieve, Reuse, Revise, and Retain) describe the general tasks in a casebased reasoner. They provide a global external view to what is happening in the system.

The proposed research system aims to study and apply Artificial Intelligence technologies, mobile devices, and cutting edge technologies of Cloud-Computing and take advantage of research achievements in image processing and information communication technologies. The project will create adaptive, collaborative, and innovative cloud computing and mobile application system in Health-Care and environments for Intelligent Information System in Health-Care. To successfully achieve the research program goals, a research framework has been developed that consists of Six layers shown in the following figure. Various research issues and application systems are proposed to be studied and be developed. This is shown in Figure 7.6.

The technology foundation of the research framework will consist of studying mobile computing for stroke emergency diagnosis, intelligent case-based reasoning engine, cloud computing hospital management engine, medical sensor processing for stroke diseases, cloud computing artificial intelligence engine and cloud computing patient database engine.

REFERENCES

1. Shahriyar, R., Bari, M. F., Kundu, G., Ahamed, S. I., & Akbar, M. M. (2009, September). Intelligent mobile health monitoring system (IMHMS). In International Conference on Electronic Healthcare (pp. 5-12). Springer, Berlin, Heidelberg.

2. Royal Tropical Institute: What is mHealth? [http://www.mhealthinfo.org/what-mhealth]

3. Oresko, J. J. (2010). Portable heart attack warning system by monitoring the ST segment via smartphone electrocardiogram processing (Doctoral dissertation, University of Pittsburgh).

4. Webots robot simulator. http://www.cyberbotics.com/

5. Arduino 6 DOF Programmable Clamp Robot Arm Kit http://www.bizoner.com/arduino-6-dof-programmable-clamp-robot-arm-kit-ready-to-use-p-238.html

6. Fang, Q., Sufi, F., & Cosic, I. (2008). A mobile device based ECG analysis system. In Data Mining in Medical and Biological Research. InTech.

7. Pradhan, M., Kohale, K., Naikade, P., Pachore, A., & Palwe, E. (2012). Design of classifier for detection of diabetes using neural network and fuzzy k-nearest neighbor algorithm. International Journal of Computational Engineering Research, 2(5), 1384-1387.

8. Karan, O., Bayraktar, C., Gumuskaya, H., & Karlik, B. (2012). Diagnosing diabetes using neural networks on small mobile devices. Expert Systems with Applications, 39(1), 54-60.

9. Peter Pesl, Pau Herrero, Mobile-Based Architecture of a Decision Support System for Optimal Insulin Dosing, Imperial Comprehensive Biomedical Research Centre, 2010.

10. Kim, J., Park, Y., & Lee, H. (2012, December). Using case-based reasoning to new service development from user innovation community in mobile application services. In International Conference on Innovation, Management and Technology (ICIMT 2012), Phuket, Thailand.

11. Qiang, C. Z., Yamamichi, M., Hausman, V., Altman, D., & Unit, I. S. (2011). Mobile applications for the health sector. Washington: World Bank, 2.

12. Kaur, G., Arora, A. S., & Jain, V. K. (2009). Multi-class support vector machine classifier in EMG diagnosis. WSEAS Transactions on Signal Processing, 5(12), 379-389.

13. Farid, N., Elbagoury, B., Roushdy, M. O. H. A. M. E. D., & Salem, A. B. (2013). A Comparative Analysis for Support Vector Machines For Stroke Patients. Rec Adv Inf Sci, 71-76.

14. http://archive.ics.uci.edu/ml/datasets/

15. Roth-Berghofer, T., & Iglezakis, I. (2001). Six Steps in Case-Based Reasoning: Towards a maintenance methodology for case-based reasoning systems. In In: Professionelles Wissensmanagement: Erfahrungen und Visionen includes the Proceedings of the 9th German Workshop on Case-Based Reasoning (GWCBR).

16. Kahn, J. G., Yang, J. S., & Kahn, J. S. (2010). Mobile health needs and opportunities in developing countries. Health Affairs, 29(2), 252-258.

17. Kulek, J., Huptych, M., Chudek, V., Spilka, J., & Lhotsk, L. (2011, September). Data driven approach to ECG signal quality assessment using multistep SVM classification. In Computing in Cardiology, 2011 (pp. 453-455). IEEE.

18. Hu, S., Wei, H., Chen, Y., & Tan, J. (2012). A real-time cardiac arrhythmia classification system with wearable sensor networks. Sensors, 12(9), 12844-12869.

19. Dragoni, M., Azzini, A., & Tettamanzi, A. G. B. (2012). A neuro-evolutionary approach to electrocardiographic signal classification. In Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE) (pp. 1-11). Universit degli Studi di Parma, Dipartimento di Scienze Sociali.

20. Curran, K., Nichols, E., Xie, E., & Harper, R. (2010). An intensive insulinotherapy mobile phone application built on artificial intelligence techniques. Journal of diabetes science and technology, 4(1), 209-220.

21. http://crsouza.blogspot.com/2010/03/kernel-functions-for-machine-learning.html

22. Rekhi, N. S., Arora, A. S., Singh, S., & Singh, D. (2009, June). Multi-class SVM classification of surface EMG signal for upper limb function. In Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on (pp. 1-4). IEEE.

23. Khokhar, Z. O., Xiao, Z. G., & Menon, C. (2010). Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomedical engineering online, 9(1), 41.