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
benedict.tan@hutcabb.com oliver.tian@hutcabb.com
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. 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. 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. 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. 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. 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.
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