Medical Accelerometor Smartphone
Application For PalCom Based Systems
Department of Computer Science Faculty of Engineering LTH Lund University, 2013 ISSN 1650-2884 LU-CS-EX: 2013-02
Medical Accelerometor Smartphone Application
For PalCom Based Systems
Master's Thesis at the department of Computer Science
Examiner: Boris Magnusson
Lund, 2012, October 8
This project is about a medical accelerometer smartphone
application (MASA) that was built to serve as a motor
function analysis tool for patients diagnosed with Parkin-
son's disease (PD). The application is capable of measur-
ing rest tremor from a subject by analysing accelerometer
values gathered by the smartphone. It does this by us-
ing a custom method developed during this project, the
mean RMS method. There is a lot of research in measur-
ing diﬀerent kinds of movements with accelerometers in
the medical community. The research is currently limited
due to the lack of large scale tools. The goal of the MASA
application is to remove this limitation. This project has
established the foundation of the solution to problems sur-
rounding the development of such an application as well
the basic application and its architecture.
I would like to thank all the participants of the research,
which was conducted at Clinic of Neurology at Lund Uni-
versity hospital. I would also like to thank Katarina A
Johansson for helping me gather the accelerometer values
from the patients motor function evaluations, my mentor
Bjorn Johnsson for teaching me the basics of the Pal-
Com framework, Boris Magnusson for granting me this
project, Carmen Arevalo for giving me great feedback on
the accelerometer data gathered and finally Johan Abra-
hamsson and Vanja Maslo for editorial notes.
2.1.2 Current diagnostic methods . . . . . . . . . . . . 11
2.2.1 An accelerometer in a smartphone . . . . . . . . . . 12
3 Problem description and proposed solution
3.2.1 Large scale accelerometer data gathering . . . . . . . . 17
3.2.2 Interchangeable analyses methods . . . . . . . . . . 18
3.2.4 Interpretable presentation of data . . . . . . . . . . 18
3.3.1 The execution of the research . . . . . . . . . . . 19
4 The Medical accelerometer smartphone application system
4.2.2 The MASA system architecture . . . . . . . . . . . 22
5 The mean RMS method and research results
5.5 Evaluation of the mean-RMS method . . . . . . . . . . . 32
5.5.2 Related work and discussion . . . . . . . . . . . . 34
6 Summary and future work
With Parkinson's disease (PD) the loss of motor function is an inevitable result of the
disease and it is clinically apparent in the advanced stages of the disease[3,6]. Most clinical
evaluations of motor function are the result of the subjective interpretation of a clinician
and the detection of abnormalities is commonly based on assessments that are only useful
for diagnostic purposes [1,4,7]. Subjective interpretations of symptoms are less useful
as they vary between clinicians and perceived changes may occur even if the symptoms
remain constant. Consequently, there is much to be gained from the development of clinical
tools that can gather variables that objectively quantify and measure the occurrence of
e.g. trembling. Methods such as camera-assisted motion analysis, electromyography for
kinesiology studies, optoelectronic systems for kinematics and accelerometer data analysis
are potential tools in this setting . The accelerometer stands out as it is relatively
cheap compared to the majority of the other previously mentioned instruments and
can be used outside a laboratory environment. There have been many failed attempts
to use accelerometer data to measure movement because of unacceptable variability in
the resulting data. However, in recent years, there have been many improvements to
the accelerometer measurement technique such as use of threshold filters and custom
To my knowledge, there is currently no cheap, readily available tool that can be used in
the clinical setting described above. I believe that the next natural step for the clinical
research community for gait, PD and stroke is to make use of commercial smartphones.
Smartphones already have a triaxial accelerometer present in their internal hardware and
the technology is mass produced, cheap and easy to use. Also, through application devel-
opment the triaxial accelerometer is readily adaptable to suit specific needs and that is
the subject of this project.
The purpose of this project is to establish the basic principles for collecting, measuring and
analysing the data from a smartphone's accelerometer. The MASA system was built to
satisfy this purpose. The MASA (Medical Accelerometer Smartphone Application) system
consists of two parts: the analytical methods and the application system.
One analytical method is developed in this project and it serves primarily as a pillar for
quantifying data for the evaluation of rest tremor. The analytical method is known as the
mean RMS method. To achieve data quantification the analytical method uses diﬀerent
mean values of root mean squares to act as diﬀerent standards of comparisons. Its goal
CHAPTER 1. INTRODUCTION
is to measure and then analyse the PD patient's rest tremble data collected with the
accelerometer and break it down to into interpretable numerical components for analysis.
The analytical method is right now suited for PD patients but can be adapted for a variety
of purposes. The application has been developed in such a way that the analytical method
can be replaced or adjusted to suit other possible future settings.
A basic android application was first developed in order to conduct field research to assess
application and analysis method requirements. During the field research tremble data was
collected from 9 patients diagnosed with PD. The analysis of the data was done in Matlab.
The final application is able to measure and compare diﬀerent kinds of rest trembles
and sort out most of the noise data and spikes. The measurements can potentially be
compared to any other previously gathered data with respect to age, natural trembles and
other illnesses etc.
The final android application system is designed for the end-user, in this case the patient
diagnosed with PD. With the help of the application a patient could potentially be able
to relay tremble data to the hospital for analysis and receive feedback from a doctor.
The relaying of the data could be done at any location with cell-phone coverage. The
data gathered could also be used for research purposes. The application was built on the
Android platform using Java and the communication to the server from the application
makes use of PalCom. The PalCom framework is a programming framework proposed to
easily establish communication between diﬀerent devices.
Chapter 2 provides necessary knowledge to grasp the problems and solutions in the rest
of the report. It includes an overview of Parkinson's disease, the accelerometer and the
Chapter 3 is a detailed description of the common problems faced by research communities
that are looking to the accelerometer as a viable aid for quantifying data of motor function
symptoms. It is then followed by a proposed application solution that could aid the research
and treatment of Parkinson's disease and other disorders with decreased motor function
Chapter 4 presents the internal architecture of the medical accelerometer smartphone
application system and its neighboring components. The chapter also includes parts where
the application could be expanded.
Chapter 5 concerns the mathematical analytical method (mean RMS method) created
to break down the smartphone's accelerometer data into interpretable and comparable
numerical components. The chapter also contains results from the research that was
conducted during the project and a minor elevation of the mean RMS method.
Chapter 6 includes a summary of the android application and the mean RMS method.
It also includes a future work section that present areas where MASA application and
analysis methods could be improved or developed.
This chapter provides key concepts in Parkinson's disease and basic knowledge pertaining
to the accelerometer and the Palcom framework.
2.1 Parkinson's disese
Parkinson's disease is a degenerative disorder of the central nervous system. It is the
result of the loss of dopamine-generating cells in the substantia nigra, a region of the
midbrain. The cause of cells death is currently unknown. Early symptoms of PD include
shaking, rigidity, slowness of movement and diﬃculty with walking gait. Advanced stages
of PD may include symptoms such as behavioural problems as well as cognitive decline
Modern treatments make use of levodopa and dopamine agents to manage the early motor
symptoms of the disease. As the disease progresses, these treatments will inevitably become
ineﬀective and cause a complication called dyskinesia, which is marked by involuntary
writhing movements. There are other available treatments for PD but they are beyond
the scope of this essay.
2.1.1 Trembles of interest
The symptoms that are of interest to this project are the movement related symptoms i.e.
shaking, rigidity, slowness of movement, diﬃculty with walking gait (pattern of movement)
and dyskinesia. These symptoms have diﬀerent visual presentations and the severity may
diﬀer between patients.
2.1.2 Current diagnostic methods
Diagnosing PD is complex and involves many diﬀerent diagnostic methods. The methods
that are of interest for this project are the motor function evaluation methods. The
evaluation is done by a specialised nurse or doctor observing the patient performing certain
predetermined tasks such as standing and sitting down from a chair or sitting still and
CHAPTER 2. BACKGROUND
counting backwards from a hundred. The evaluation tasks are designed to provoke certain
symptoms characteristic for PD. The assessment involves visually observing the patient
and subjectively determining the severity of the diﬀerent symptoms. I have included the
motor function evaluation methods in the appendix section of the rapport.
The accelerometer is a device that measures proper acceleration. Proper acceleration is a
change in velocity relative to free-fall (zero-gravity). It does not measure the change in
acceleration but the force it is subjected to during the acceleration. This force is otherwise
known as g-force (although its not actually a force). An accelerometer only measures one
direction. If held vertically it will measure Earth's gravity.
Simply put, if the accelerometer is accelerating freely to the ground it will show zero.
This is because the frame of reference of the entire measuring system is subjected to same
acceleration. However, if the measurement system would face a deceleration like hitting
the ground, its internal frame of reference will still be subjected to Earth's gravitational
There are a number of diﬀerent ways to measure proper acceleration. A commonly used
accelerometer is the capacitive accelerometer, which is used in most smartphones today.
A technical description of capacitive accelerometers is beyond the scope of this paper. For
a detailed description of the technology of capacitive accelerometers, the reader is referred
2.2.1 An accelerometer in a smartphone
The behaviour and reliability of an accelerometer varies depending on the model of the
smartphone it is integrated in. The accelerometer readings can be influenced notably by
the following reasons:
• Multitasking: Smartphone operating systems use multi-tasking systems, meaning
that the processor switches between tasks depending on priority or timer. This might
alter the performance of the accelerometer and consequently cause inconsistencies in
• The hardware specifications (clock frequency, architecture etc.) of a smartphone
model also aﬀects the accelerometer even if the same accelerometer chip is used in
• Threshold: Some operating systems use various thresholds to remove insignificant
noise from the accelerometer. The threshold may vary depending on the type of
operating system on the mobile device. This means that the operating system decides
if the measurements have changed enough to notify the user.
• Access to the accelerometer sensor may very depending on the model and operating
system. This is because the layers between the hardware and the application can
vary in architecture and access. As an example, some smartphones close access to
the accelerometer sensor when it's in hibernation while others do not.
2.3. THE PALCOM FRAMEWORK
In short, diﬀerent smartphones may have varying software and hardware configurations
which is a potential source of measurement error and inconsistency if one is not aware.
2.3 The PalCom framework
The PalCom framework is the result of a project lasting between 2004 and 2007. PalCom
(Palpable Computing) is a framework aimed at easily establishing communication between
digital devices. These devices can range from personal computers to ubiquitous devices.
Examples of such ubiquitous devices can be medical equipment, smartphones, digital cam-
eras and GPS's. The primary gain of using this framework is that an application will gain
the ability to easily share its functionality with other applications and thus expanding its
The implementation of the open framework is done in Java and consists of a set of commu-
nication protocols and logical structures representing various parts of the communication
This paper only provides an overview of relevant concepts regarding the PalCom frame-
work. For more information, the reader is referred to .
The underlying architecture of the PalCom framework is centered on three concepts,
namely devices, services and assemblies. Interplay between these three concepts is achieved
through discovery, connections and tunnels as stated below.
• Devices: A PalCom device can represent any kind of device. This device could be
a digital camera, smartphone or software simulation. The purpose of the device is
to host services and manage its identity in the PalCom environment. For instance
hosting a smartphone as a PalCom device will result in the smartphone showing up
in the PalCom environment.
• Services: A PalCom service is hosted on a PalCom device. It serves as the in-
put/output terminal link to the physical world. It's where commands are defined
and executed e.g updating an internal counter or displaying certain data on a screen.
A service is defined by a service description, which defines the interface which the
system will operate. This description is where the developer specifies the commands
the service will provide for the user.
• Assemblies handle the logic of interaction between services. Through configuration
an Assemble can describe how and when a communication between services should
happen, in other words event handling. But for this to be possible the Assem-
bly needs to have references to the devices and their services participating in the
communication event. The logic of the interaction between the services is handled
through creation of scripts, in other words event scripts.
• Communication is a discovery protocol that makes the PalCom devices aware of each
other. This is achieved through "heartbeats", which are periodically sent broadcasts
messages that urge all devices to identify themselves as "alive". The discovery pro-
tocol is limited to local area networks but with use of tunnels the network can be
CHAPTER 2. BACKGROUND
extended to include a remote network location. The tunnels are simple connections
between two networks that forward PalCom traﬃc such as "heartbeats" from one
location to another. This creates the appearance of two networks being one and the
In order to use the PalCom framework on Android smartphones, a PalCom environment
was an adaptation of "The Thing" for android. It's an application that enables the user
to establish and manage communication between other PalCom applications or external
devices while it can load services dynamically. It follows the same architecture as the
Problem description and proposed
This chapter is a detailed description of the common problems faced by research commu-
nities that are looking to the accelerometer as a viable aid for quantifying data of motor
function symptoms. It is then followed by a proposed application solution that could
aid the research and treatment of Parkinson's disease and other disorders with decreased
motor function symptoms.
3.1 Problem description
Initially, the goal of this project was to build a smartphone application that could regulate a
Parkinson patient's dosage of levodopa with the use of a smartphone's accelerometer. Also,
project was to enable a patient to use the application at any location to send accelerometer
data to a remote location. The purpose of this project was eventually adjusted due to
(mainly medical) obstacles discovered during development that were not obvious the start.
The final application is therefore not designed to guide a patient's intake of levodopa.
Rather, the main purpose of this project is to provide an objective variable that can be
used by a clinician in addition to other information in the assessment of symptoms in a
patient with PD. On a wider note, this project seeks to shed light on the inherent potential
of smartphones and their use in a medical setting. Significant technological developments
in recent years have led to the wide distribution of advanced technology which is relatively
cheap compared to specifically designed medical equipment. As demonstrated in this
paper, this technology can be adjusted to potentially suit medical needs.
Given the performance characteristics of accelerometers available on smartphones and the
nature of the Parkinson's disease, there lies a challenge in distinguishing random move-
ments from tremor or motor function impairment . Creating accelerometer-based sys-
tems for determining the movement diﬀerences between healthy subjects and those with
PD has been done before . The results of these systems can identify changes in move-
ment characteristics associated with PD in domestic environments and are encouraging
from a clinical viewpoint. The average triaxial accelerations obtained from selected seg-
ments of PD patients are capable of providing comparable values to established methods
CHAPTER 3. PROBLEM DESCRIPTION AND PROPOSED SOLUTION
of diagnosing dyskinesia [6,7]. To the best of my knowledge these types of methods have
not yet been tested with a smartphone device. The results of the previously mentioned
studies need to be at least reproduced with a smartphone device in order for this to be a
viable alternative. The challenges are several:
1. The methods used in aforementioned studies need to be adapted to suit accelerometer
data from a smartphone.
2. The results of the data quantification need to be be presented in a manner such that
they can be easily interpreted by a clinician.
3. The interface must be designed in such a way that a patient can use this application
outside a hospital environment.
4. The application must be able to transmit the results to a hospital.
5. The application should support multiple accelerometer recording sessions i.e record
accelerometer data with multiple smartphones at the same time. This being as mul-
tiple accelerometers are required to record complex gait patterns and other move-
So, four basic requirements can be deduced:
1. Large scale accelerometer data gathering.
2. Interchangeable analysis methods.
3. Intuitive interface.
4. Interpretable presentation of data.
The accelerometer in the smartphone measures proper acceleration. There are essentially
two diﬀerent ways of gathering data from a patient. One way is to gather accelerometer
data over a long period of time while the patient is performing random tasks. Another
way is to have patients perform standardised tasks, which makes it easier to distinguish
abnormal involuntary movements and tremor characteristics in illnesses like PD. The ap-
plication developed in this project is primarily designed for standardised movements that
can be performed in or outside a clinical setting.
When attempting to develop software that is capable of gathering clinically useful data,
some fundamental questions are raised concerning the recording of standardised move-
ments: What constitutes clinically significant tremor during a predetermined, standardised
task? What constitutes severe tremor as compared to modest tremor or even normal move-
ment when comparing accelerometer data? Also, a standardised task can be performed in
slightly diﬀerent ways, which can cause abnormalities in the data.
The first thing to do is to sort the data into interpretable components that can be presented
to the user. There are many ways to do this e.g. RMS values, threshold filters ,
custom algorithms, min and max values and harmonic movement analysis. These
methods have been used to extract certain aspects of data collected from standardised
movements. For instance, harmonic movement analysis in the frequency domain would
yield more useful information on walking patterns than RMS. This being as the RMS
doesn't include information significant to evaluating walking patterns. To the point, when
choosing a method to extract interpretable components from accelerometer data, one needs
to consider what specific task is going to be performed.
3.2. THE PROPOSED SOLUTION
3.2 The proposed solution
The solution is a system that meets the four basic requirements presented in the problem
description. This solution is called MASA (Medical Accelerator Smartphone Application).
Before the development of the MASA system research was conducted in order to assess a
more detailed requirement specification such as user interface, analysis methods and large
data gathering. The execution of the research is presented in section 3.3.1 page 19. The
proposed solution is partially shaped from the findings of this research. The research was
performed before the final application was built. This is because the findings from the
reseach would shape the final application.
The chosen smartphone platform is Android. The MASA system will use the PalCom
framework to handle its communication logic. An Android application can use the Pal-
Come framework with an additional application called The Thing. The Thing runs in the
background of the android device and only needs to be setup once.
3.2.1 Large scale accelerometer data gathering
The MASA system should be scalable enough to support a large population of users. It
should be possible to increase the amount of users by installing the application on more
A large population of user results in large quantities of data, so there need to be scalable
data structure to support this. Therefore the storage of the recorded data and user in-
formation needs to be in centralized server with a database. The server could be located
either in a hospital or in a remote location. This solution would enable the MASA system
to be scalable and easily managed by administrators of the system. A user of the applica-
tion would gather the accelerometer data through recording sessions. The sessions would
be associated to a user id and certain predetermined tasks that the user would perform. It
will run a session for the preset amount of time that is needed to draw a conclusion from
the recorded data. The user provides an user id so the recording sessions can be associated
to a patient id in the hospital. The user id also enables the MASA system to avoid storing
sensitive patient information. That information could remain in the hospital's internal
databases. This is done by the hospital having the user id and the real patient informa-
tion. The MASA system would have the user id and the accelerometer data. This way the
MASA system avoids constraining privacy laws set on many hospitals today.
A low level communication protocol between the applications is not necessary to create.
The PalCom framework can manage the lower level communication logic while the devel-
oper only needs to establishes the higher level commands, such as send and receive. Instead
the developer focuses on creating PalCom services that interconnect via an assembly. The
communication logic is to be done by establishing scripts in the assemble. The services
are designated modules that have certain roles in the MASA system. These services will
be discussed in more detail in chapter 4.
To make it a robust application it should be able to be used at any location with or without
a cellular transmission signal. If it does not have a signal it will save the recorded data on
in the memory of the smartphone and then transmit it when it gets a signal. Therefore
the MASA system has an internal buﬀer service on the client side of the application.
CHAPTER 3. PROBLEM DESCRIPTION AND PROPOSED SOLUTION
3.2.2 Interchangeable analyses methods
It should be possible to add additional analysis methods to the MASA system. To make
it easier to get the application to use the new analysis methods it would be preferable to
put them on the server side of the MASA system. This will enable a research team or
responsible administrator to update a method without seeing to that all the users update
their application on the client side of the system. The disadvantage of this solution is that
the user needs to transmit the accelerometer data to the server to get the results.
The system architecture should feature future implementation of multiple accelerometer
data gathering sessions. This means that the MASA architecture of the services in the
client and server side have the ability to be configured so that it can support multiple
accelerometer data gathering sessions.
3.2.3 Intuitive interface
The application needs to have good user feedback of what's happening on the device, such
as status of the accelerometer, connection to the database, session status etc. The interface
needs to have restrictions such as locking input fields while in a session and non sensitive
buttons to avoid mis clicks on buttons. These requirements are based on experiences that
was gained from the research that was conducted during this project.
3.2.4 Interpretable presentation of data
There are many methods for measuring and analysing motor and tremor related dys-
functions characteristic to PD. Many of these methods would include variety of diﬀerent
techniques for gathering data with an accelerometer. Below I have reviewed some common
requirements that are posed on accelerometer analysis methods outside of a lab environ-
1. Relative comparability: To move from arbitrary number or measurement to result
that explains a result in terms of good and bad, is done by introducing an appropriate
point of reference. An appropriate reference to a result can be done by comparing the
subject to other healthy subjects with common non disorder related characteristics
or subjects with similar disorder symptoms. For instance a result could be compared
to patients within the same age group or disease development.
2. Time independent: The recording time of a session has varied lengths. This means
that in order to have an adaptable analysis of the data it needs to be time inde-
pendent. In other words longer recording sessions should amount a more precise
3. Spike insensitive: While in a recording session, a user could introduce a spike in the
accelerometer data by accidentally bumping the smartphone into a object or shifting
their weight in an unexpected way. These accidents could easily go unnoticed and
eﬀect a result of an session significantly if not handled properly. There is no way
to completely exclude spikes without eﬀecting the result. But there are methods of
minimizing the eﬀect of a data spike. But these methods are dependent on what
measurement technique is used. Therefore the spike "filtering" should be dealt with
with consideration to what measurement technique is used.
4. Noise filtering: In accelerometer measurements there is always the presence of noise
fluctuations in the recorded data. It's important to consider the eﬀects of the noise
in the analysis method. Handling noise in the data can essentially be done in two
diﬀerent ways: include the noise-data in the recordings and reference material and
introduce a certainty variable in the result, or filter out the noise-data when recording
the data and compare it to filtered reference material. Choosing between these two
methods of handling noise data is dependent on what measurement techniques are
used. But either way, the choice of handling the noise problem should result in an
interpretable variable with a certainty value of the analysis findings.
So in order to create an analysis method suited for an smartphone accelerometer like
the MASA system, these requirements need to be met. Additionally the results of these
methods need to be interpretable by a user.
The goals of this research was to gather accelerometer data with a smartphone on a
variety of diﬀerent clinical methods and compare the results of the clinical methods to
the accelerometer data. The clinical methods are done by assessing an opinion of a PD
patients motor functions. It was conducted with the help at the neurological clinic at Lund
University hospital. The main goal of the comparison was to enable an evaluation of the
smartphone as an accelerometer tool for diagnosing PD.
An additional goal was to gather knowledge of what requirements would be posed on
the MASA system in terms of interface, analysis methods and large scale data gathering
3.3.1 The execution of the research
Research included accelerometer recordings of 9 patients diagnosed with PD performing
5 diﬀerent tasks. A basic accelerometer recording application was first built to provide a
platform for assessing the requirements of the final MASA application. The Smartphone
used to record the accelerometer data was a Sony ericsson Xperia X10. The smartphone
was placed on a patient's right and left bicep in a sport band. It served as a recording tool
to collect data from diﬀerent diagnostic tasks. The tasks that were performed were 1-3,
5 and 7 in the motor function evaluation (Motorisk utvardering) sheet in the appendix.
Accelerometer data was gathered from both arms in tasks 1-3 and 7 while only the right
arm was necessary on task 5. The task performance was graded by a specialized nurse
or doctor on a scale of 0 to 3. The basic application was very simple. It worked by
pressing a start button on the display which initiated the accelerometer recording process.
When the patient was done performing a task a stop button was pressed and the recorded
accelerometer data was saved on a file in the smartphone. The file was then transferred
via bluetooth over to a laptop.
After the accelerometer data was collected it was analysed through a variety of diﬀerent
numerical methods in MATLAB. The numerical analysis methods included fourier analysis
and a RMS breakdown of the data. The results of some numerical methods were then
compiled and compared to the findings of the clinical methods. Some of the findings will
CHAPTER 3. PROBLEM DESCRIPTION AND PROPOSED SOLUTION
be presented in Chapter 5. From the findings an analysis method was created suited for
analysing task one (Resting tremor evaluation) in the motor function evaluation sheet.
This analysis method is based on using multiple RMS values to quantify the data into
levels so the accelerometer values could be presented. The method will be referred to as
the mean-RMS method and it will be described in more detail in Chapter 5.
3.3.2 Experiences gained
During the execution of the research the basic application encountered numerous problems
that would help determine some important requirements of the final MASA system. These
are some problems that the basic application faced during the research.
To press start and stop when recording is problematic even when the patient is assisted
with the task. The reason for this is that by just pressing the smartphone introduced
errors in the recording because the smartphones hasn't really finished the recording when
the button is being pressed. To further add to the previous problem the buttons of the
interface also need to be insensitive to light touches. This is because the buttons could
be inadvertently pressed when the smartphone is in a sport band. In order to avoid
unintentional interaction with a recording session and have long press buttons the sessions
needs to be automated. But to have automated recording sessions also implies that for
each recording session it needs to have customized settings. The customized settings would
include a script for how long the recording session should last and a countdown before the
session starts. This solution would satisfy both problems in this section. In addition an
automated recording session enables the possibility for using the application alone.
Early in the research it was realized that every recorded task needed to be analysed in an
individual way. The reason for this is because the patterns that the tasks created in the
accelerometer all have particular patterns that need to have customized analysis methods
in order to generate useful data that could potentially be used as diagnostic aid.
During the recording sessions it was found that a lot of work went into documenting where
the smartphone was placed and which task the session was associated to. From this it was
found that the MASA application needed to have a section for input parameters, such as
left or right arm, user id, task number etc.
The Medical accelerometer
smartphone application system
This chapter includes a description of the first version of the MASA (Medical accelerometer
smartphone application) system. The initial goal of this project was to provide the user
of MASA with five diﬀerent accelerometer analysis methods for diagnosing Parkinson's
disease. But the initial goal was set too high as creating five diﬀerent analysis methods
would be an overreach of what is possible for me to create in the set time frame to complete
this project. The goal of the application was reset to one accelerometer analysis method.
This not a completed system, the unfinished implementation will be detailed in the Future
Work section 6.1 page 35.
The clinical resting tremor evaluation is created to provoke symptoms of what is referred
to as resting tremor. This is task one in the motor function evaluation sheet seen in the
appendix of this report. The patient would be sitting down in a chair with the arms placed
on their thighs. The patient is asked to close his or her eyes and count backwards from
a 100. The evaluation is done by observing (if apparent) the amplitude of arm tremors
under a period of 20 seconds. The amplitude is graded on a scale of 0 to 3, 0 being no
visual signs and 3 being very high amplitude of tremor (4 cm tremors).
In its current state the MASA system features analysing accelerometer data from a rest
tremor evaluation and displays the result from the analysis. The smartphone is placed
on the patients bicep with the help of a sport band. It is during this evaluation the
MASA application would be activated to record the patient's movements. All the input
parameters of the application and recorded data are currently not saved anywhere but
serves as an illustrative purpose for future development.
CHAPTER 4. THE MEDICAL ACCELEROMETER SMARTPHONE APPLICATION
The design of the MASA system is Client Server based. The client part is located on
the Android part of the system while the server is located outside of a hospital. The
Android client is responsible for coordinating a recording session and sending the data to
the server for analysis. The server saves and analyzes the recorded accelerometer data
and sends it back to the client. The communication between the Server and the Client
is established by the PalCom framework. To properly support the PalCom framework
the overall architecture needs to satisfy its basic design principles: devices, services and
assemblies. For the client to work with the PalCom framework the Android device needs to
have The Thing installed. It will enable PalCom services and devices to show up in other
PalCom networks, in this case the server side. This is done by setting up a tunnel in The
Thing, it will forward all the communication to the other end of the tunnel thus enabling
the Server to detect other Android devices and in turn running the MASA application.
The MASA system architecture focuses on the creation of sessions. A session is a collec-
tion of data referring to a patient that performed an standardised task. For instance a
completed session has collection of accelerometer data that refers to a standardised task.
A session also includes user id and other information relevant to the standardised task
such as last time a medication was taken.
4.2.2 The MASA system architecture
When Palcom is running on both the server and client side they are capable of commu-
nicating with each other with their corresponding assemblies: the Client assembly and
server assembly. The assemblies on both sides handles the communication logic with
their services. Each service has individual responsibilities, such as providing storage or
accelerometer services. Here is an overlay of the MASA system architecture.
Figure 4.1: An overview of the MASA system
• GUI service: Creates events that tell the system when to start, stop, reset and save
recording data. The GUI is created as an Android Activity, this means that the
Service is not always available to the PalCom network. Therefore a PalCom device
is needed. The device in turn is hooked up to an ordinary service. This enables the
GUI activity to show up as a separate entity in the PalCom network when GUI is
activated on the smartphone. The reason for this is that the GUI service is only
available when the GUI is active in the android operating system.
• Accelerometer service: It works by a service registering itself as receiver, then the
accelerometer will stream accelerometer data to whomever is registered. A receiving
service can stop receiving data by unregistering itself from the accelerometer service.
• HD service: Is a buﬀering service created as a recording session storage point. When
the session is started the accelerometer service will stream its data to the HD service.
When its done streaming, the HD service is told to save the data in a bundle and wait
for further commands. The HD service is also meant to serve as an oﬄine storage
point when the smartphone does not have a connection to the server.
• Analysis service: This is the service that analyses a set of accelerometer data and
returns the result to the service that sent the data. In the future it is meant to
provide multiple analysis services. The Analysis service uses my custom analysis
java classes (Analyzers). There is currently one analysis method, it serves to analyse
accelerometer data from a rest tremor evaluation. MATLAB has good support for
creating java classes from a matlab function. So it's recommended that the developer
creates new analysis functions in MATLAB because it's possible to export them
as java classes. Working with analysing methods is preferable to do in MATLAB
because it has good support fore just that type of development, This being, creating
CHAPTER 4. THE MEDICAL ACCELEROMETER SMARTPHONE APPLICATION
mathematical functions that analyse data. Also note that to export java classes you
would need a paid certain MATLAB API license.
• Database: This is the final storage location for the accelerometer and user data
sessions. This database uses standard units and formats appropriated for research
• Database Adapter: A Service that provides a translations of accelerometer and user
data sessions to standard units and formats that matches the database.
• Patient Database: The patient database is to be located in a hospital. This is where
all the sensitive patient information is stored such as patient identification etc.
The communication logic is done by scripts in assemblies. Scripts are a chain of responses
from events. The events are associated to service input and output commands that trigger
the events. The client and server assembles communicate with each other by forwarding
these commands between each other. Below is a sequence diagram of a typical commu-
nication between the services when the MASA system records an event. In this case a
resting tremble evaluation session.
Figure 4.2: This is an sequence diagram of the services in the MASA system in a normal
recording session case. The red diamonds are user interface interaction or updates (de-
pending on arrow direction). The red circles correspondence to the interface states seen
in figure 4.3 on page 25
4.3. THE USER INTERFACE
Figure 4.3: This is a step by step display of the diﬀerent states of the MASA user interface.
4.3 The user interface
The basic design principles of the application limits the user from unnecessary inactive
functions. Such as not displaying a cancel button when there is nothing to cancel. Due to
the simple nature of a recording session it is usually not necessary to display more than one
button at a time in a recording session. All the buttons in the application feature a long
touch click handling. The interface also feature input lock for when the application is in a
recording session. The lock is necessary to avoid the input parameters to be accidentally
changed during a session.
The red numbers in figure 4.2 on page 24 corresponds to the red numbers seen in figure 4.3.
State 2 in figure 4.3 shows the countdown process before the client initiates the recording
process. State 3 is the recording session. State 4 is when the user is presented with a
CHAPTER 4. THE MEDICAL ACCELEROMETER SMARTPHONE APPLICATION
choice of whether to discard the previous recording or to send it for analysis. State 5 is
the analysis result of the recording session, a more detailed description will be featured in
4.4 Requirement comparison
In section 3.2.1 it is stated that the application needs to satisfy four basic requirements
in order to be able to satisfy the problems that are discussed in section 3.1. There is
architectural support for all of the requirements that are discussed but there are features
yet to be implemented, such as interchangeable analysis methods, multiple smartphone
sessions and oﬄine buﬀering. These desired implementations are described in more detail
in section 6.1.
The system should support a large number of active applications, but it has not been
tested yet. The concept of recording sessions is integrated in the architecture although
modification is needed for when the MASA application should support multiple analysis
methods. Furthermore customized settings is a prerequisite for this to work. By the
application having customized settings for every analysis method would enable the client
to receive specification of how to adjust its interface accordingly.
The mean RMS method and
This chapter is about the rest tremor analysis method that was created during the re-
search of this project: The method is referred to as the mean-RMS method and it focuses
on quantifying and presenting the data into interpretable components. The goal of this
chapter is not to prove that the developed method works but to propose an analysis model
for future development. The data that was gathered from the resting tremor evaluation
was used to create this method. Data from the resting tremor evaluation of the patients
is presented here although not the rest of the evaluations. In the end of this chapter is
an evaluation of the mean RMS method. Note that the accelerometer data could be de-
pendent on what type of accelerometer and smartphone is used. The Smartphone used to
record the accelerometer data was a Sony ericsson Xperia X10.
5.1 The accelerometer data
The accelerometer data that was collected has the following parameters:
• x, y, z: A vector that describes the physical movement of an accelerometer. To get a
grasp of the proportions of the values, the sum of a x,y,z-element with the smartphone
laying completely still would approximately add up to 9.8m/s2 (SI units) which is
very close to 9.81m/s2(earth's gravational pull).
• t: The timestamp paired with the vector x,y,z.
The x,y,z vector is merged as euclidean distance1 to enable simple analysis of the vector.
The euclidean distance is referred to as k.
1Otherwise known as L2-norm, it's calculated by k =
x2 + y2 + z2 where k is the L2-norm
CHAPTER 5. THE MEAN RMS METHOD AND RESEARCH RESULTS
5.2 Research results
The results from the research include a rating from the specialized nurse who performed the
evaluation on 9 patients who participated in the research. The patients task performance
was rated on a scale from 0 to 3, 0 being no visual signs of tremor and 3 being very
clear signs with arm tremor amplitude up to 4 cm. The right and left arm are evaluated
individually. The developed analysis method is created in Matlab but was later translated
There is a lot of additional information connected to the evaluation results: Such as time
since last received medication, hand injuries, first apparent symptoms ect.
Patient number 1 2 3
Table 5.1: The left and right arm rows are the result of the clinical rest tremor evaluation.
Patient nbr refers to the diﬀerent patients that participated in the research. Patient 1 and
2 is not used in this research because the accelerometer data was not gathered from the
left arm at that point of the research.
5.2. RESEARCH RESULTS
Figure 5.1: The x-axis is time in milliseconds and the y-axis is the euclidean distance of
x,y,z, can be seen as m/s2. This data is from the patients left arm.
Some of the data from table 5.1 is plotted in figure 5.1. The gathered data has been
divided into 3 groups. Patient 5 and 9 have significant diﬀerences result and evaluation,
and were thus not included in group 2.
• Group 1: Control group of two healthy males ages 26, 32. The plot in group 1 is a
typical representation of that group.
• Group 2: A typical plot of a PD patient (Patient 7) with an evaluated rating of
1. Group 2 includes patients 3-4 and 6-8 and have ratings between mostly 0 and 1.
Their medication was taken on average 2 hours before the accelerometer recording.
• Patient 5: PD Patient with very apparent dyskinesia, rated 1. medication taken
right before the recording.
• Patient 9: PD Patient without medication, rated 2.
• Group 3: This group is created to later be used in the mean-RMS section. It includes
Patient 5 and 9.
CHAPTER 5. THE MEAN RMS METHOD AND RESEARCH RESULTS
5.3 The mean RMS method
This is a custom method meant for analysing accelerometer data from a resting tremor
evaluation. Quantifying the accelerometer data and creating interpretable components of
it could be done by creating group reference points. The mean-RMS method uses this
technique. By selecting diﬀerent groups of data, lets say group 1 in the previous section,
we could create a quantifying area from this control group. This could be done by taking
the mean RMS2 value of that group. An RMS value could here be seen as the eﬀective
value of an mechanical motion, in this case proper acceleration.
In expression 5.1 ki is accelerometer data set from a group of patients. n is the amount of
sets in the group. ↵mean is the mean-RMS line.
Next step is creating mean-RMS values from equation 5.1 on diﬀerent groups of patients.
In figure 5 we have selected group 1 and group 3 as two diﬀerent reference points. ↵group1
and ↵group3 includes both left and right arm values. The lines are placed around the mean
value of the sample to create quantifying areas. The group 1 line can be seen in figure 6
as a green line and group 3 as a red line.
Figure 5.2: A description of the diﬀerent areas created by ↵area1 and ↵area3 created by
the diﬀerent ↵mean
There are three diﬀerent areas that ↵area1 and ↵area3 creates in figure 5.2: In (within the
green line), Inbetween (outside of green but in red) and Out (outside red). The proportions
of the data in three diﬀerent areas are then calculated. In other words the number of data
points in the three diﬀerent areas divided by the total number of data points in the sample.
area3 ki ↵area1
Expression 5.2 is a set theory annotation of the diﬀerent proportions.
This results in the proportions in, inbetween and out that can be expressed as percentages.
To make the percentages more interpretable they are expressed here in pie chart form.
2The RMS (root mean square) is calculated rms =
mean(x2) where x is a set of data points.
5.4. REQUIREMENT COMPARISON
Figure 5.3: This is a pie chart representation of the proportions of data placements within
the diﬀerent areas of the mean-RMS lines created.
When interpreting the pie charts it is important to consider what groups have been used
to create the reference lines, or baselines. This is important because the baselines create
answers that are relative to which group they are compared to. In this case the baselines
are, group 1 and 3. Group 1 creates a standard for what is considered "normal" while
the group 3 baseline (consisting of two severe cases) sets the bar for what is considered
It is possible to create a point system from the proportions the RMS-method creates. Such
as applying weight points to the diﬀerent proportions. For example:
in + intbetween ⇤ 3 + out ⇤ 6 = s
This s creates an arbitrary scale that can be compared to findings of clinical evaluation
methods. With more statistical data expression 5.1 could be adjusted to create a more
accurate categorical scale by using appropriated weights and mean lines for quantification.
5.4 Requirement comparison
In section 3.2.4 page 18 it is stated that to have interpretable data, an analysis method
of this sort needs to the best of its ability have relative comparability, time independency,
spike insensitivity and noise filtering.
The mean-RMS method addresses relative comparability by using diﬀerent baselines for
comparison of the analysis data. These baselines could be selected from a pool of previous
patients that can act as good comparison points with parameters such as age group,
symptoms, injuries etc. Time independence is achieved through the use of calculating the
proportions of the data distribution. This allows the possibility of longer data gathering
sessions which would yield a more precise proportion result. In other words the proportion
result would converge towards a solution over time. The RMS-method is not completely
insensitive to spikes but is not aﬀected by the amplitude of them either, other than that
it might be counted as an out element.
CHAPTER 5. THE MEAN RMS METHOD AND RESEARCH RESULTS
Figure 5.4: This is a chart of a spike in the data that went unnoticed in the clinical
evaluation. The data is from patient 4 left hand.
As seen on figure 5.4 the data spike eﬀects the result but could be negated by an appropriate
round oﬀ. The RMS-method handles NOISE data not through filtering but by including
noise in the creation of the baselines and including the error in the result. Although the
noise data is present there is no real error magnification in these calculations. The noise
on the accelerometer is about four decimal places.
5.5 Evaluation of the mean-RMS method
With expression 5.3 page 31 it is possible to create values that can be compared to an
arbitrary scale. By adjusting the weights of function (2) to it is possible to calculate the
correlation coeﬃcient between the mean-RMS method results and the clinical evaluation
(scaled from 0 to 3). It is important to consider that the result of this evaluation is not
fully reliable. The reason for this is that there is a very small amount of clinical evalua-
tion results and accelerometer data (that can contain recording method errors) to create
intervals for categorization (statistical insignificance). Furthermore the clinical evaluation
was conducted by one (experienced) evaluator and thus a slight element of human error
present. Therefore this evaluation should only be considered a model for future evaluations
and not a precise result.
5.5. EVALUATION OF THE MEAN-RMS METHOD
Weight formula 5.4 is applied on the mean-RMS method result gathered from patient 3-9.
intbetween ⇤ 3 + out ⇤ 4 = s
The result of the weight formula is seen together with the clinical rest tremor evaluation
in this plot.
Figure 5.5: This plot is weight formula 5.4 versus the evaluated value of the patients. The
red circles represent the patients left arm while the blue is there right. The numbers on
the circles corresponde to patients numbers in table 5.1 on page 28.
The calculated correlation coeﬃcient3 between the clinical evaluation and the mean-RMS
weight column is approximately r = 0.63 which is moderate result. Another way to
calculate the correlation is by using Spearman's rank4 that yields r = 0.72 and p < 0.01.
The correlation coeﬃcients varied depending on what weight function was used, so the
correlation could be higher if a better weight function is found. A better correlation could
also be achieved by using diﬀerent baselines depending on what patient that is being
3The Pearson product-moment correlation coeﬃcient. for more information se http://en.wikipedia.
4It is like Pearson product-moment correlation but instead uses a "grade correlation", for more infor-
CHAPTER 5. THE MEAN RMS METHOD AND RESEARCH RESULTS
5.5.2 Related work and discussion
There have been several proposed accelerometer-based systems that identify changes in
movement charecteristics assosicated with PD in domestic enviroments. The method used
in "An ambulatory dyskinesia monitor" included using a triaxial accelerometer worn on
the shoulder that recorded levodopa-induced dyskinesia. The recorded data was compared
to mean clinical accelerometer scores5 and yielded an r = 0.972 with and p < 0.001 (Spear-
man's rank). The ambulatory dyskinesia monitor study has used a diﬀerent approach for
calculating mean acceleration of the data compared to the mean-RMS method. Here the
translation of the accelerometer data was done by using a finite fourier transformer with
preset time intervals to create mean acceleration amplitude blocks that could be compared
to manually created arbitrary scores.
In "Ambulatory Motor Assessment in Parkinson's disease" a more comprehensive tech-
nique was employed through the gathering of triaxial accelerometer data from six diﬀerent
parts of the subject. The work included developing an algorithm that could distinguish
between the on and oﬀ states6 during a PD patients daily life activities. Among the
results it was concluded that gathering data from the trunk area of the body yielded a
sensivity of 0.97.7 and 0.96 in specificity by analysing the peak frequencies of the subjects
Taking into account the data from related works, the results from using the mean-RMS
method (spearman rank of 0.72) are encouraging especially considering that the method
is in earlier stages of development. There is also the option of implementing and further
evaluating the methods applied in related works in the MASA system as they do not
require any other hardware than the triaxial accelerometer provided by a smartphone.
5The score was based on the AIMS and the Goetz score, much like the motor function evaluation score
performed in this project
6This is one way of describing levodopa induced dyskinesias (LID)
7A statistical measument of performance of a binary classification test. http://en.wikipedia.org/
Summary and future work
This chapter includes a summary of the android application and the mean RMS method.
It also includes a section on future work, which focuses on areas where MASA application
and analysis methods could be improved or developed.
Much of the future work is drawn from the purposes solution section 3.2.1 to 3.2.4 page
17 to 18 the four base requirements
6.1.1 Development work
Databases. The Database, Database Adapter and Patient Database needs to be set up so
the MASA system can store the result from data gathering sessions. This is also a useful
feature during future analysis method development.
Oﬄine support. The android device should be able to store accelerometer data in its
memory when there is no connection to the server. The stored data should be sent to
server once the application establishes a connection.
Multiple analysis methods. There is need for the application to feature more analysis
methods that can potentially serve as clinical motor function evaluations. The application
should have interface support for choosing which type of evaluation that should be per-
formed. This results in changes of how a session is created, in other words a session needs
to feature what type of tasks that are being performed and possibly what analysis method
that should be used.
Multiple accelerometer recording session support. There are two reasons for the
implementation of Multiple accelerometer recording session support. The first reason is
that there are many research projects that use multiple accelerometers in an attempt to
gain insight into complex motor functions. The second reason is that it could accelerate
recording sessions of patients significantly. Many evaluations require gathering accelerom-
eter data from many locations of the patient's body, such as left and right arm. Two
CHAPTER 6. SUMMARY AND FUTURE WORK
smartphones can gather data simultaneously which would decrease recording session time
6.1.2 Mean RMS method work
For the MASA system to feature more analysis methods, more insight into accelerometry
and how clinical studies are designed would be required.
• The mean-RMS method has not been clinically proven yet is a viable method to
be included in a clinical research. Such studies can be designed to gather data from
diﬀerent patient categories and establish better baselines for e.g. specific age-intervals
or stages or presentations of PD.
• Function 5.3 page 31 can be used to create an arbitrary scale to correlate the mean-
RMS methods result with clinical motor function evaluation method used in patients
today. Improvements to this function could be done by adjusting the weights (the
multipliers) so it results in a higher correlation rate.
There have been plenty of studies in the gait, stroke and PD research community that
focus on advancing methods for motor function evaluations with use of an accelerometer.
Many of these studies have been inhibited by the cost of instruments that are required
in this kind of research. This project has attempted to show that a smartphone with a
built in accelerometer has the potential to serve as a cheap and viable tool in large clinical
studies. Initial research led to the establishment of four basic requirements that needed to
be met in order to satisfy the needs of users, these being multiple analysis method support,
large scale data gathering capabilities, intuitive interface and interpretable presentation of
the data. An additional goal was to test the application by gathering accelerometer data
from patients diagnosed with PD. Accelerometer data was gathered from 9 patients with
PD performing 5 diﬀerents tasks from a motor function evaluation sheet. The gathered
accelerometer data helped create the mean-RMS method, which is customised to analyse
data for rest tremor evaluation.
After the four requirements were formulated, the MASA system was developed. It features
an android application built as a client that connects to a central server. The purpose of
the server is to analyse and store results from a recording session done by the android
client application. A session is a collection of accelerometer data recorded from a patient
that has performed a standardised task. The MASA system makes use of the PalCom
framework to achieve a scalable and flexible internal communication architecture. The
MASA system is modeled after the PalCom designing principles: assembles, services and
devices. For the application to be used it makes use of PalCom on the android platform
"The Thing" installed on the device. This enables an android application to make use of
the PalCom framework.
The MASA system in its current state is capable of performing a rest tremor accelerom-
eter data analysis for research and demonstration purposes. The application features an
interpretable presentation of the result in the form of a pie chart. The MASA system could
potentially make use of existing methods [1-7] to help further development in featuring
more analysis methods from the clinical evaluation methods sheet. If the application is
to be used by patients on a large scale, additional analysis methods need to meet four
basic requirements i.e. relative comparability, time independence, spike insensitivity and
noise filtering. The mean-RMS method satisfies these requirements to a large degree. The
mean-RMS method result is relative to what subject group it is compared to (relative
comparability). The result approximates over time due to calculating data placement pro-
portions around established baselines (time independent). The method is not completely
insensitive to spikes but is not aﬀected significantly by a few random ones. Noise data is
included in the creation of the baselines and does aﬀect the result significantly but there
is need for it to be highlighted in the result. The performance of the mean-RMS method
is currently rated to moderate with a Pearson product-moment correlation of 0.63 and a
spearman rank of 0.72. It could be improved by using a better weight function and more
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FEATURE Cancer pain management Managing adult cancer pain: The latest NCCN guidelines Pain management is important for cancer patients during therapy and some-times after treatment is completed—not just at the end of life. BY CARL SHERMAN Pain is common in cancer — one-third of patients undergoing treatment and three-fourths of those with advanced