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Application on Tele-Rehab - MIPS


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Application on Tele-Rehab - MIPS

  1. 1. B. Tan and O. Tian Paper Presentation: 6 – 8 March 2014, Seoul Page 1 Using BSN for Tele-Health Application in Upper Limb Rehabilitation Benedict Tan and Oliver Tian HutCabb Consulting Private Limited Singapore, Singapore Abstract - Improved upper limb rehabilitation requires careful and re-constructed information around stroke patients’ muscle activation characteristics and kinematic features in functional movement. Body Sensor Networks (BSN) are deployed to provide an immersive engagement of the rehabilitation exercise and translation into an augmented reality world for a higher order of analytics and consultation by medical consultants. Results of the analysis generate contextual intelligence to improve therapy programmes in order of an increased magnitude with derived information on model schemas, pattern deviation and effectiveness of diagnostics. Keywords – body sensor networks, augmented reality, data mining and analytics, user profiling, intelligent systems, software engineering, computer applications, wireless networking, embedded systems, multimedia & signal processing, pervasive computing, personals services, cloud computing, computer control, and automation. I. INTRODUCTION A. Rampant emergence of Internet-of-Things (IoT) Internet-of-Things (IoT) is a term coined by Kevin Ashton in 1999 during a marketing presentation made at Procter & Gamble (P&G) in 1999 about the potential of Radio Frequency Identification (RFID) global system in monitoring product movements through the RFID electronic tagging [1]. Since then, it has quickly caught on to refer to a society of physical objects being simultaneously connected to the internet via the same Internet Protocol (IP). This therefore allows previously disparate devices to be connected by the individual via the internet. IoT as a mechanism can be further perpetuated into two distinct types of communication: thing-to-person and thing- to-thing communication [2]. This has been made possible by the diffusion, as well as convergence, of innovations such as mobile devices, WiFi, Cloud Computing, data analytics, software applications, and sensor technology. IoT lends a major role in the realization of the concept known as ’Ubiquitous Computing’, which was founded in late 1980s by Mark Weiser, together with the birth of the internet [3]. IoT is hence applicable to many currently under-served industries. B. IoT in Healthcare In essence, IoT represents the world of connected devices which uses data collection and communication technologies to digital content and context-aware services [4]. This trend brought about the paradigm change which resulted in a widespread diffusion of information through computing. With the development of pervasive services, the invention and widespread proliferation of technologies and applications in seamless and lower costs of communications further strengthened at least three domains of pervasive computing: home networking, automobile network solutions, and mobile E-business. By the use of such technology to bring together various devices that were previously independent of each other, this has became an extension of the concept of pervasive computing, from which the basis for the Internet-of-Things was developed. Today, Internet-of-Things has progressed as a novel paradigm which bridges the gap between the worlds of the virtual internet and the reality of objects, by integrating the functions of “things” in the real world with the virtual world through software applications [5]. As information systems are the foundation of new productivity sources, IoT based healthcare systems play a critical role and have significant contributions in growth of medical information systems. However, to take advantage of IoT, it is essential that medical enterprises and community should embrace such converging technologies in terms of performance, security, privacy, reliability and return-on- investment. Tracking, tracing and monitoring of patients and medical objects are very essential and are challenging research directions in applying IoT, hence making the essential role of IoT in healthcare systems dissimilar among different healthcare components. Hence, the participation of IoT between useful research and present realistic applications warrants attention. In this paper, we discuss the application of remote rehabilitation services over the cloud infrastructure for post- stroke patients using technologies for sensor data collection and analytics, wireless communications, interactive digital media as well as contextual profiling.
  2. 2. B. Tan and O. Tian Paper Presentation: 6 – 8 March 2014, Seoul Page 2 II. MOTIVATION Critical Need in Rehabilitation According to World Health Organization (WHO), 15 million people suffer stroke worldwide each year [6]. Of these, five million die and another five million are permanently disabled. Within China alone, there are more than a million stroke cases yearly with a current estimate of more than seven million stroke patients. Approximately 67%, if any, of survivors become functionally dependent and a further 10% require long-term institutional care, thus imposing a great burden on the family and community. Stroke leads to movement disability and high morbidity. Recovery mainly depends on rehabilitation. The prognosis for upper limb recovery following stroke is poor, a systematic review [7] concluded that complete motor recovery of the upper extremities occurs in less than 15% of patients with initial paralysis. Rehabilitation is a critical enabler that helps stroke survivors maximize their quality of life physically, cognitively, emotionally and socially. Recovery from stroke is a long process that can continue over several years. Most of the recovery occurs in the first 2-3 years, and especially the first 6 months. Rehabilitation needs to continue in hospitals, at rehabilitation centers, in home and residential care. However, due to limited resources (hospitals facilities, healthcare specialists and appropriate equipments) full recovery rate can be relatively low [8]. • Approximately one third of stroke patients recovers their lost functions fully or almost fully, and get back to their pre-stroke activities within a year. • About 50% of stroke survivors who are under the age of 65 may return to work. • However at one-year anniversary after a stroke, about two third of stroke survivors will have some level of disability, ranging from light and moderate to very severe. Existing post-stroke rehabilitation relies on specialists’ manual examination and personal judgment, and the training activity is performed under the specialist’s supervision. There is still, very much, a lack of qualified rehabilitation specialists. Rehabilitation training is a long process, patients and families prefer to be at home or a community place of convenience; rehabilitation training is painful, many patients do not have strength to fully cooperate. As such, there is a dire need for the next generation post-stroke rehabilitation system, which is ubiquitous, intelligent, motivating and immersive [9] [10]. III. EMBRACING BSN TECHNOLOGY TO ACCELERATE STROKE RECOVERY A. Research Goals The objectives of the project is to develop the next generation rehabilitation system, which needs to be Immersive, Impactful, Informative and Intelligent, for post-stroke patients by using technologies embracing Body Sensor Networks (BSN) and Interactive Digital Media (IDM), to efficiently capture human body motion patterns and re-construct into a 3D augmented reality world, coupled with immersive and interactive gaming technologies. The research goals are as follows: 1. Produce real-time capture the patient motion and reconstruct it in a 3D virtual training space; 2. Evaluate, quantitatively, the patient function status and rehabilitation training progress based on medical knowledge and normal cases, providing the basis of system intelligence; 3. Visualize the training process in 3D virtual space, visually guiding the patient in the training, highlighting the progress and existing issues; 4. Design the system using immersive gaming philosophy, so as to motivate the patient, and create an enjoyable rehabilitation training session; and 5. Provide rehabilitation services ubiquitously, and interface communication between doctors, specialists, patients and family members, so that rehabilitation training can be at ease of the home and/or community setting, reducing the cost, bringing convenience to patients and families. B. Principles of Design Consideration There are two major categories of design considerations – User Interactivity and Contextual Intelligence. User Interactivity Accuracy: As a product for healthcare, the system must show high accuracy and reliability in data collection and data processing in order to produce useful medical information. For the same reason, sensors should be sampled at high frequencies to correctly get the phenomenon being monitored. Durability: The portable and mobile kit lends itself to classify as an everyday appliance which must not be burdensome especially for elderly or impaired patients.
  3. 3. B. Tan and O. Tian Paper Presentation: 6 – 8 March 2014, Seoul Page 3 Wear-ability: Rehabilitation must be carried by patient during exercising. Hence, data collection sensor nodes must have an attachment to ensure a steady fixation through the activity routine, accommodating rude movements as well as interferences at network layers to provide reliable communications. Comfort: During physical activity, the comfort factor becomes an important design consideration to provide the highest degree of convenience - unobtrusive devices and small form factor are high on the design scale. Safety: The product must be safe and easy to use for non- specialist. Considering the criticality of the application, the proposed solution should be context aware to in view of the patient environment and physiological state. User Interaction: The product must be engaging and captivating to encourage the patient to “train hard” while exploring new activities. Context Intelligence Context is the background in which an event takes place, which involves any set of circumstances surrounding an event. In training, knowing the specific context of an event is imperative to training effectiveness so that the social process attains a higher level of retention [11] “Contextual Intelligence is the ability to quickly and intuitively recognize and diagnose the dynamic contextual variables inherent in an event or circumstance and results in intentional adjustment of behavior in order to exert appropriate influence in that context “ [12] Identifying the factors and variables that constitute contextual ethos becomes an important aspect of the ability to diagnose. In this research, we explored the following elements: i) Physical Context – understanding the physical attributes of people in the environment such as time, and location etc. ii) Social Context – establishing and leveraging on the relationship and roles of the people and objects such as ratings, reviews, and social attention etc. iii) Behavioral Context – monitoring patterns over time, including interactions with devices and services such as recurrence and actions etc. iv) Content Context – extracting and extrapolating specific contents from public domains and practical daily lifestyle examples. IV. DESCRIPTION OF SYSTEM A. Functionalities In the proposed application, sensor units attached to trunk, upper arm, lower arm and hand, respectively, to capture the movements of the upper limb. The 3D reconstruction is shown on the display screen in front, with an avatar to support the patient in the therapy exercises. The performance is evaluated based on the trajectory difference with the normal person. The amount and types of trajectory diversion reflect various problems, and requiring different training scheme and efforts. Figure 1 Upper Limb Motion capture and 3D reconstruction The patient is first assessed for functional / physical status, by using the assessment module. The assessment module establishes a professional assessment measurement to determine suitable rehabilitation scheme, or training session module for training session to support continuous rehabilitation scheme to allow the patient to start therapy sessions. During training, the assessment module continuously evaluate patient’s progress by comparing the patient’s movement with the norm (as ascertained by the healthcare specialists). The distance measured is then used to visualize the 3D virtual training space. The 3D virtual space shall provide patient with an enjoyable 3D game scenario to get the patient immersive into the training. The patient, family members and specialists can view the training process in various forms of game scenario. As the training progresses, the current rehabilitation schema may be completed or upgraded. Good examples can be stored in the schema base for future reference. In that case, index is created for that scheme to make it accessible.
  4. 4. B. Tan and O. Tian Paper Presentation: 6 – 8 March 2014, Seoul Page 4 Figure 2 Block diagram of the application B. Findings and Achievements of the Research There are several key issues which were researched and acceptable results achieved, and kernel functional modules to be developed into prototype for assessment purposes: 1. Use of Micro-sensor for capture of motion data for upper limb movements and reconstruction of 3D motion hardware and software system to establish and validate algorithms suitable for real time 3D reconstruction. This system has been customized to rehabilitation needs. The key research studied the biomechanical model of the upper limb based on current understanding of the stroke damage and motor brain reorganization so that movement patterns of stroke patients can be better analyzed and facilitated. 2. In Functional Assessment module, which has two functions - one to assess functional scale of patient respective needs and the other a real-time evaluation of upper limb motion capabilities – the patient is navigated through a routine according to the assessment and rehabilitation scheme selected. The motion capabilities of the patient, which concerns performance quality and rehabilitation progress, are also monitored during the training session. In this research aspect, medical experts have been consulted to understand, represent and implement the existing assessment measurement in a digital way. The research draws an extension and enhancement of existing assessment measurement, with care taken to study the principles of rehabilitation, such as the “dependent functional reorganization” and “motor relearning theory”. When developing distance measures to evaluate performance of rehabilitation training, it is not enough to compare normal trajectory, but the quality of trajectory, factors such as smoothness, time taken, are amongst important considerations. While working with medical experts, the distance measurement has been tested with continual improvements and fine-tuning during its usage. 3. In Rehabilitation Training Management module, sessions are created for the patient based on status of assessment results, and previous training records. During training, rehabilitation scheme parameters, movement quality measures and training progress will be recorded, and supervised. C. Issues and Challenges The various technologies have been successfully applied and converged into a seamless hybrid to demonstrate the viability of the application. The prototype is undergoing independent practice trials and validation. From the early results, we were able to distill out common characteristics which define a robust solution. Unlike conventional methods, we encountered several issues and challenges and are taking steps to resolve them [12]. 1) Physical mechanics The wearable physical form factor needed to be small, light-weighted and non-obtrusive. The size and weight of sensors are predominantly determined by the battery factor. A careful trade-off between communication and computation is crucial for an optimal design. 2) Location of sensors For purpose of accurate measurement, sensor location may be subjective to the physical built of the patient user. Sensor attachment is also a critical factor, since the movement of loosely attached sensors creates spurious oscillations which may be disruptive. 3) Applicable algorithms Application-specific algorithms mostly use digital signal pre-processing combined with a variety of artificial intelligence techniques to model user's states and activity in each activity. Most of the algorithms in the open literature are not executed in real-time, or require powerful computing platforms such as laptops for real- time analysis. Furthermore, there is to singly accepted protocol for assessing rehabilitation recovery status. 4) Social phsychology Social issues of wearable systems include privacy/security and legal issues. Due to communication of health-related information between sensors and servers, all communications over network should be encrypted to protect user's privacy. In addition, deriving benefits from medical automation is also a new-found hurdle.
  5. 5. B. Tan and O. Tian Paper Presentation: 6 – 8 March 2014, Seoul Page 5 D. Current Results In China, the associate project team recruited trial subjects for clinical trials in renowned hospitals including Beijing University 1st Hospital, Shanghai Huashan Hospital, and Nanjing University of Medicine. The subjects are divided into two groups - post-stroke patients and healthy subjects who have matching age and sex profiles to the first group. All of them were tested through rigorous inspection processes by neurologists. The steps of such trials include: i. Basic Data Capture and Recording: Each subject’s basic data were recorded including: 1) age, sex, educational level, smoking history, alcohol history, body weight, history of hypertension and coronary heart disease; 2) motion data measured by the system. ii. Motion Capture and Analysis: The healthy subjects’ motion of the upper limbs were captured and analyzed to obtain normal range of doing some specific movements. iii. Status Evaluation: Evaluate post-stroke patients’ status using distance measurement between the patient’s movement/function and one performed by normal person, and the existing professional assessment measures, such as Fugl Meyer assessment of physical performance, and hand- path ratio parameters. iv. Personalized Rehabilitation Training: Post-stroke patients would perform rehabilitation training at home/community based on personalized training schemes and their status provided by the system; the results would be assessed by medical specialists remotely. Upper limb motion capabilities of the patients were also evaluated, which concerns the performance quality and the rehabilitation progress during the training scheme. In Singapore, the project team had engaged a second level experimentation with selected nursing homes to establish the viability of the product for beta testing. We had sourced and identified participating institutions and developed a similar, yet more practical oriented approach to the Chinese experience. Healthy subjects from selected institutions and post-stroke patients from identified medical institute participated in the trials and provided valuable feedback. With more valuable feedback, the product was further fine-tuned and improved. Additional steps taken included: i. Interactive Gaming Components: Participants were streamed through sessions with a more enriched and light-hearted user interface in the product, with gaming scenarios reflecting more practical life home routines. ii. Analysis and Update on Rehabilitation Plans: The system should be able to provide an acceptable level of analysis to update the roadmap laid out for the rehabilitation progress. V. POTENTIAL USE CASES Several Use Cases were detailed with a high potential of deployment – each paving the way for higher productivity gains and deriving a multiplier effect on the recovery rate. In the following section, two such Use Cases are discussed briefly. Hospital Internet Cloud Servers Database IMURS Server Doctor travelling Home Patient Internet WLAN Office Family Figure 3 Use Case – Remote Home Care The above Use Case depicts the scenario where the patient is undergoing therapy in the convenience of the home, possibly with the assistance of a home caregiver. The patient’s case is being monitored closely by a travelling medical consultant, who may be in transit between medical centers. The exercise results will be immediately available for review by the medical team in the hospital as well as the travelling consultants. Any feedback can be further relayed to the family members via mobile devices while they are working in a remote office. Such a Use Case can also be extended to create a multiplier effect to increase the number of touch-points for doctor- patient ratio, hence increasing the productivity of the medical consultation. More importantly, more patients are expected to receive the attention which has been lacking until now.
  6. 6. B. Tan and O. Tian Paper Presentation: 6 – 8 March 2014, Seoul Page 6 Figure 4 Use Case – Step-down Remote Care The above Use Case depicts the scenario where the patient is undergoing therapy in the step-down facility of a General Practitioner (GP) Clinic supported by a nursing home. The patient’s case is being monitored by a GP doctor in the local clinic, with relevant support from the medical specialist. The GP doctor can provide feedback on the patient’s progress to the nursing home. The exercise results will be immediately available for review by the therapists in the supporting nursing centre. This Use Case can be extended to patients receiving therapy in any of the affiliated GP Clinics where it is most convenient for the patient and at more convenient times of the day. In this way, the patient can continue his/her therapy with a much better recovery rate. The therapist can continue to support the patient with much more frequency and touch points after the patient has discharged from the nursing home. VI. CONCLUSION We have demonstrated that a selection of converging and powerful technologies can form a viable infrastructure to support effective remote consultation in extended rehabilitation. This solution supports a dire need in current step-down care and such a tele-rehabilitation solution has the potential to advance current medical practice many folds. We expect to use this solution to increase the recovery rate of post=stroke patients. This is a practical case study which is expected to derive significant benefits in the healthcare industry, is the result of applying technologies such as sensor data collection and analytics, wireless communications, interactive digital media as well as contextual profiling – a exemplary manifestation of IoT in action. ACKNOWLEDGMENT This project and preparation of this paper is derived from efforts from a symbiotic relationship between research teams from two countries – China (GUCAS) and Singapore (HutCabb). In consultation with years of research on both shores and the efforts to converge powerful technologies in light of the imminent IoT era, the authors would like to thank all parties who have contributed in one form or another to the preparation of this paper. The Chinese Academy of Sciences (CAS), formerly known as Academia Sinica, is the National Academy for the Natural Sciences of the People's Republic of China. It is an institution of the State Council of China. It is headquartered in Beijing, with institutes all over the People's Republic of China. It has also created hundreds of commercial enterprises, Lenovo being one of them. Graduate University of Chinese Academy of Sciences (GUCAS) was founded in 1978; GUCAS is the first graduate school in China with the ratification of the State Council. GUCAS boasts a galaxy of pioneering scientists with lots of achievements. The research team under Professor J Wu put in tremendous efforts to validate the applied technology. REFERENCES [1] K. Ashton. (2009). That 'Internet of Things' Thing. [2] N. Dlodlo, T. Foko, P. Mvelase, and S. Mathaba, (2012) “The State of Affairs in Internet of Things Research” The Electronic Journal Information Systems Evaluation Volume 15 Issue 3 2012, (244- 258), available online at [3] M. Weiser and R. Gold, (1999).The origins of ubiquitous computing research at PARC in The late 1980s. IBM Systems Journal. [4] International Telecommunication Union, (2006). ITU Internet Reports 2006:, Geneva, Switzerland, December 2006. [5] Y. Wang and X. Zhang, (2012). Internet of Things. [6] World Health Report – 2002, from the World Health Organization.. [7] P. Langhorne , et al. (2009) Motor recovery after stroke: a systematic review. Lancet Neurol. 2009; 8(8):741-54 [8] World Report on Disability, Chapter 4 Pg 103-108,, from the World Health Organization.. [9] Z Huang and J. Wu, et al. (2013) Upper Limb Function Analysis of Stroke Patients by Fusion of Surface EMG and Motion Data. RehabTech Conference, Poster Session, (27 Feb – 1 Mar 2013), Singapore. [10] Z Huang and J. Wu, et al. (2011) Motor Impairment Evaluation for Upper Limb in Stroke Patients Based on Micro-senor. The Journal for Advanced Nursing Practice, July/August 2010 Vol 24 No 4, pp 196 – 201, 2010. [11] J. Lave and E. Wenger. (1991). Situated learning: Legitimate peripheral Participation: Cambridge University Press. [12] A. Hadjidja, M. Souila, et al. (2013) Wireless Sensor Networks for Rehabilitation Applications: Challenges and Opportunities "Journal of Network and Computer Applications 36 (2013) 1-15" DOI: 10.1016/j.j nca.2012.10.002