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Personalised Ambient Monitoring: aiding those with Bipolar Disorder. Sally Brailsford John Crowe Christopher James Evan Magill. The PAM project. Enabling health, independence and wellbeing for psychiatric patients through P ersonalised A mbient M onitoring. A sandpit project. - PowerPoint PPT Presentation
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The PAM project
Personalised Ambient Monitoring:
aiding those with Bipolar Disorder
Sally Brailsford
John Crowe
Christopher James
Evan Magill
The PAM project
2
The PAM project
Enabling health, independence and wellbeing for psychiatric patients through Personalised Ambient Monitoring
The PAM project
A sandpit project• Funded by the Engineering and Physical Sciences
Research Council
• Sandpit theme: “Bringing Care to the Patient”
3
The PAM project
4
The PAM team • Sally Brailsford, Southampton
• John Crowe, Nottingham
• Christopher James, Southampton (PI)
• Evan Magill, Stirling
• plus 4 PhD students
– Syed Mohiuddin, Pawel Prociow, James Amor and Jesse Blum
Sensors
OperationalResearch
AmbientMonitoring
BehaviouralAnalysis
PAM
The PAM project
5
PAM external steering group• Ms Sylvia Wyatt, Future Healthcare Network, NHS Scotland
• Dr Amy Drahota, Research Fellow, University of Portsmouth
• Mr Peter Jones, Community mental health nurse, Lancashire Care NHS Trust
• Dr Paul Courtney, Consultant Psychiatrist, Hampshire Partnership Trust
• Mr Richard Barritt, Chief Executive, Solent MIND
• Mr James Stubbs, Service User Representative
The PAM project
6
The aims of PAM• To build a system of unobtrusive sensors, linked
(through a standard mobile phone) to a remote computer system, which automatically monitors the activity patterns of people with mental health problems
• To determine whether it is possible to use such a system to obtain ‘activity signatures’ in a manner which is acceptable to the patient and can provide useful information about the trajectory of their health status
• And if this is so, to determine how this information can best be used to maintain health and aid independence
The PAM project
Bipolar Disorder• Severely disabling mental illness which affects
functionality, relationships, employment and quality of life; affects 2% of the UK population (MHF, 2006)
• Bipolar disorder is the 6th most common disabling illness worldwide (WHO, 2004)
• In 2002, the estimated annual cost to the UK NHS of managing bipolar disorder was £199M, of which £70M was spent on hospital admissions (Gupta and Guest, 2002)
• Many pharmacological treatments are available but these can have unpleasant side-effects and adherence is often poor, leading to hospital admission
7
The PAM project
How does BD alter lifestyle?
Manic Depressive
Euphoric behaviour
Increased (excessive) social activity
Psychomotor agitation
Sleep deprivation
Flight of ideas
Low mood
Lack of interest in social interaction
Psychomotor retardation
Insomnia
Concentration problems
The PAM project
9
Managing Bipolar Disorder• Most patients want to manage their own condition,
using medication only when necessary
• Motivated patients of above-average intelligence, interested in self care and independence
• Early warning signs or prodromes can be detected while patient is still “self-aware” and can take action (seek medical help, start medication etc) to avoid hospital admission
• Paper-based “mood diaries” shown to be effective in trials
The PAM project
10
Problems with paper-based systems• Do not provide a sense of control over daily life
• Patients complain about vigilance and energy required
• Problems with accuracy, completeness and honesty of patient-reported data
• Patients may forget to document important details
• Comorbidity and drug response go unmeasured
• No reduction in depressive relapses (Perry et al, 1999)
The PAM project
The aim of PAM• To use a system of electronic sensors to provide an
automated equivalent of a mood diary, which alerts the patient to a change in activity pattern which could signal the onset of a bipolar episode
• Patient would be sent an SMS alerting them to a possible change, which they could then act on (if they chose)
• PAM is mainly aimed at people who live alone
• Aim is to identify a baseline “activity signature” and then identify significant deviations from this
11
The PAM projectDevice Nodes
• Worn– Mobile Phone
• Questionnaire• Gateway Application
– GPS Transceiver– Wearable Accelerometer– Wearable Microphone– Wearable Light Sensor
• Environmental– Microphone– Light Sensor– Passive Infrared Sensors– Micro-switches– Bed Sensor– Camera– Infrared Receiver For Remote Control– PC
The PAM project
Wearable sensor set
GPS module
XYZaccelerometer
Internal accelerometer
GSM location
User input:• General health
questionnaires • Mood self-assessment
Wearable Node• Acceleration
• General light level• Artificial light level
• Ambient sound properties
BluetoothEncounters*
- Bluetooth - 3G / GPRS - User input - Internal
The PAM projectEnvironmental sensor set
Environmental processing unit
• Processing• Storage• Backup• Upload
PIRsensors
Wide-angleCamera
Environmental NodeMonitoring of:
• Remote control activity Main and cupboard doors.• General light level• Artificial light level• Ambient sound.
Bed occupancysensor
Home appliances monitoring• Microwave• Refrigerator
• Oven
- Bluetooth - WiFi - 433 MHz RF
The PAM project
Example data – wearable light levels
Art
ifici
al lig
ht
Genera
l lig
ht
Working Bus awaiting Bus awaiting Commuting Walking Home
The PAM project
Threads of research activity• The four centres collaborated across the project
but we gravitated towards independent themes (as required by the four PhD students)
• accelerometry & behaviour analysis
• outside the home
• BD modelling
• rule-based sensor network
The PAM project
accelerometry & behaviour analysis
accelerometry & behaviour analysis
The PAM project
19
Accelerometry & Behavioural analysis• Determine what a person is doing (sleeping, eating,
restlessly pacing around, etc) by feature extraction algorithms on sensor data (e.g. the “neuroscale” algorithm)
• Develop an “activity signature” for that individual, describing their normal activity pattern when well
• Develop a set of decision rules which determine whether an individual’s current activity is “normal” – for them – or may indicate the potential onset of a prodrome
accelerometry & behaviour analysis
The PAM project
20
Tri-axial accelerometry
0 0.5 1 1.5 2 2.5
x 104
0
2
4
6
8
10
12Walk
-800 -700 -600 -500 -400 -300 -200 -100 0 100-15
-10
-5
0
5
10
15
20
25Walk
walking
0 2000 4000 6000 8000 10000 12000 140000
2
4
6
8
10
12Lecture
Acc
eler
atio
n
Samples (1 Hz)-800 -700 -600 -500 -400 -300 -200 -100 0 100
-15
-10
-5
0
5
10
15
20
25Lecture
at a lecture
activity over time
clustered activity
accelerometry & behaviour analysis
The PAM project
outside the home
outside the home
The PAM projectOutside the homeExample: tracking movement & position
Off-the-shelf GPS module BT enabled accelerometer
13 Feb 2010 13:06:41; G; 5256.0723; -112.181; 0.0; 13 Feb 2010 13:06:42; A; 0.044; -0.888; 0.484; 13 Feb 2010 13:06:44; A; 0.036; -0.892; 0.492; 13 Feb 2010 13:06:45; A; 0.036; -0.892; 0.496;
logfile.txt
outside the home
The PAM project
Positional data and pre-processingoutside the home
The PAM project
Identifying meaningful locationsoutside the home
The PAM project
Activity data – Bluetooth
• Participants on average encountered more than 1000 unique Bluetooth devices of which:
– 80% were one-off encounters– 15% were “occasional” (1-10) encounters– 4% were “frequent” (10-40) encounters– 1% were “regular” (40 or more) encounters
• This data can be used to monitor social interactions and enhance location information
outside the home
The PAM project
BD Modelling
BD modelling
The PAM project
27
Operational Research modelling of PAM• Aim is to develop a “natural history” model for BD
and use it to test the sensitivity and specificity of the PAM algorithms for detecting change in a patient’s health status, in the context of:-
– A random (personalised) selection of sensors– Unknown reliability of the chosen sensors and
the computer network system – Occasional failure (or deliberate removal) of a
sensor– Variety in patient behaviour, in all states of
health
BD modelling
The PAM project
28
Challenges for modelling BD• No OR modelling approach of BD in the literature,
although some Markov models for depression (Patten et al, 2005)
• No universally accepted staging models for BD found in the medical literature
• Symptoms vary among patients ; and patients may exhibit mixed behaviour (manic and depressed)
• Lack of easily measurable criteria
• Took advice from clinical psychiatrist on our Steering Group
BD modelling
The PAM project
“Normal”
Manic
Depressed
Initial conceptual model of BD
BD modelling
The PAM project
30
Final state transition model
= 0 = 1
…
• The parameter represents mental health state: totally depressed ( = 0), “normal” or healthy ( ≈ 0.5) and totally manic ( = 1)
• Each day, with a certain probability, the person may either stay in the same state, or progress to an adjacent state, in steps of 0.01
BD modelling
The PAM project
31
An illustrative sample path for λ
0
0.2
0.4
0.6
0.8
1
1.2
0 100 200 300 400 500 600
Days
Lam
bd
a va
lues
BD modelling
The PAM project
32
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Lamda
Hou
rs /
Cal
ls
Sleep Phone
Hours of sleep Phone callsNormal 6 4Depressed 10 1Manic 2 12
BD modelling
The PAM project
PAM-detected physical activity levels during various mood states
33
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
0 100 200 300 400 500 600
Days
Ph
ysic
al A
ctiv
ity
Lev
els
Physical Activity Levels (PAL)
PAM detected PAL
BD modelling
The PAM project
rule-based sensor networks
rule-based sensor network
The PAM project
Programming sensor networks (PROSEN)• distribute rules to rule engines embedded in smart
sensors
• flexible programming
• support for run-time updating of rules
• aids personalisation and changing mental states
• initial work in a wind farm setting ….
rule-based sensor network
The PAM project
PROSEN & REED
• REED (Rule Execution and Event Distribution):
– supports the distribution of rules and trigger events
– employs a rule-based paradigm :
• allows sensor networks to be programmed at run time
• allows allow sensor network behaviour to be changed at run time
– allows subscribe-notify service to be constructed– potential for processing, filtering and collating
data
rule-based sensor network
The PAM project
Communications paradigm
• Low-level decision and event driven
• Interact by sending/receiving decisions and events
• Low-level decision:
– <trigger event, condition, action>
Event received from:• components in
PN• Neighbour PN• Policy server
Test of a local state
Executed if the condition is true• manipulate/store
local data• generate events• may generate low-
level decisions
rule-based sensor network
The PAM projectREED Middleware architecture
Decision
Event Event Decision
StorageProcessing Communications
Operation System Interface
Operation System
event
event condition
condition
action
action
…
Middleware Interface
Low-level AI (“novelty” filter)
Sensor diagnostics Sensor controller
…
Decision
Event Function callDecision Space
Initial default decisions
<“power up”, true, “sending HELLO
event”>
<“temp sensor reading update”, “temp < -20”,
“send ‘temp too low’ event to Policy server”>
Decision Space management
rule-based sensor network
The PAM project
39
Mobile phone-centric sensor-based care system
rule-based sensor network
The PAM project
Backend – Gateway Connectionrule-based sensor network
The PAM project
Network Interfacerule-based sensor network
The PAM project
Mobile Phone Based Body Area Network
rule-based sensor network
The PAM project
PAM Sensor Reading (PSR)<Readings>
<Readingset Message_type="gps" Entity="egps" Entity_instance=“aaa_extgps" Frequency="1" Unitoftime="4s" Id="1251993327994" />
<Sr Ref="1251993327994">50.936348, -1.393458, 0.0, 4.0</Sr>
<Readingset Message_type="wl" Entity="w" Entity_instance=“aaa_wearable" Frequency="1" Unitoftime="s" Id="1251993354943" />
<Readingset Message_type="wa" Entity="w" Entity_instance=“aaa_wearable" Frequency="1" Unitoftime="s" Id="1251993354952" />
<Sr Ref="1251993354952">-0.5083, 1.7986, 0.0782</Sr>
<Sr Ref="1251993354952">-0.1173, 1.1339, 0.2346</Sr>
<Sr Ref="1251993354952">-0.0782, 0.8993, 0.1173</Sr>
<Sr Ref="1251993354943">2.0, 0.0</Sr>...
rule-based sensor network
The PAM project
MOBILE RULE-BASED APPLICATIONS
• Custom Symbian S60 Java ME applications installed on the mobile phone interfaced with m-Prolog. (Also Sony-Ericsson)
• PAM-Gateway
– Control data capture from wearable units (such as GPS, accelerometer, ambient light and sound levels)
• PAM-Transfer
– Perform automatic mobile to PC data transmission
• PAM-Q
– Dynamically adjustable questionnaires
rule-based sensor network
The PAM project
RELIABILITY AND ACCEPTABILITY ISSUES
• Mobile phone battery life• On-body gateway disconnection• On-body device form factor issues• Environmental sensor reliability issues• Rule coherence
rule-based sensor network
The PAM project
POWER ISSUES
TIMESTAMP (s)45.25 60.75 76.25 91.75 107.25 122.75 138.25 153.75 169.250
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Profile 1Profile 2Profile 3Profile 4P
ow
er
(W)
rule-based sensor network
No BT & no user applications: 9 hours @ 0.41 w
BT, but no storage: 7.5
hours @ 0.48 w
BT, and storage: 5.5 hours @ 0.67 w
Internal GPS: 5 hours @ 0.68 w
The PAM project
47
Rule Coherence• when rules are:
– changing over time– possibly unique for particular individuals– originating from different stakeholders
• how can we ensure the integrity of the rules
– in particular the lack of conflicts between rules
rule-based sensor network
The PAM project
48
Example: “traditional” feature interaction
• Alice cannot call Charlie
– Originating Call Screening (OCS)
• If Alice calls Bob
– Bob’s Call Forwarding transfers call to Charlie
Alice
Charlie
XOCS
Bob
CFx
rule-based sensor network
The PAM project
49
classes of feature interactions
1. MAI: Two (or more) features control the same device (Multiple Action Interaction)
2. STI: One event goes to different services which perform different conflicting actions (Shared Trigger Interaction)
F Doff
F
on
FDhot
F
hot
Power Saving heater
Envcntrl
temp air con
windcntrl
rule-based sensor network
The PAM project
50
classes of feature interactions
3. SAI: A service performs an action on a device which triggers another feature. The chain might involve any number of links (Sequential Action Interaction, Loops)
4. MTI: The existence of one feature prevents the another one from operating. (Missed Trigger Interaction)
Fclose
D F!
Foff
D Fcold
Env Cntrl blindsmove alarm
Power Saving
temp heat cntrl
rule-based sensor network
The PAM project
51
Conflict Analysis
• Offline and online analysis looking for conflicts between device rules
• Like FI for call control
• Searching for 5 types of conflict:
– STI, SAI, LI, MAI, MTI
• 12 case studies were developed to explore the conflicts
51
Missed Trigger Interaction occurs when the Context Triggering rules delay the activation
of a home gateway.
rule-based sensor network
The PAM project
{jmb, ehm}@cs.stir.ac.uk 52
initial results• 867 tests for combining:
– shared trigger, – multiple action, and– sequential action.
• that is; from 17 features against each other and themselves across the three criteria.
• 410 conflicts detected
• currently being analysed for patterns
rule-based sensor network
The PAM project
in conclusion
in conclusion
The PAM project
54
PAM in practice• A short technical trial of PAM was been carried out
on the four PhD students
• NHS ethics approval obtained for a small patient study (max 4 patients) of PAM: completed in Southampton but still under way in Scotland
• Many practical aspects highlighted in clinical study!
• Further technical developments under discussion
• Further collaborations planned
in conclusion
The PAM project
Moving forward – other PAM like projects
• PSYCHE (Personalised monitoring SYstems for Care in mental HEalth) project develops a personal, cost-effective, multi-parametric monitoring system based on textile platforms and portable sensing devices for the long term and short term acquisition of data from selected class of patients affected by mood disorders.
• http://www.psyche-project.org
55
in conclusion
The PAM projectMONARCA
• MONitoring, treAtment and pRediCtion of bipolAr disorder episodes (Monarca)
• EC funding of near €4m
• An example of recently funded EU projects in this field.
• Monarca is investigating aspects of bipolar disorder disease by adopting a holistic approach to its assessment, treatment and self-management. The project focuses on objective assessment and prediction of bipolar disorder episodes and aims to advance the discovery of new markers for this disease.
56
in conclusion
The PAM projectOPTIMI• Online Predictive Tools for Intervention in Mental
Illness
• EU funding
• The Neuroscience Institute at the University of Bristol.
• The aim is to develop tools to perform predictions based on early identification of the onset of an illness by monitoring poor coping behaviour.
• The system will study an individual's behaviour patterns over a sustained period and spot any baseline changes suggesting they are becoming unwell.
• Will use wearable sensors and sensors fitted to domestic appliances to measure activity levels. EEG readings, voice analysis and physical activity analysis will be used.
57
in conclusion
The PAM project
58
Thank you for listening
The PAM project
State-of-the-art Health Sensor Networks• Wearable Sensor Networks & Body Sensor Networks for
medical and psychiatric monitoring is active an research area:
– Alarm-Net
– CodeBlue
– Care in the Community
– UbiMon
– MobiCare
– LiveNet
The PAM project
“Normal”
Manic Depressed
Initial conceptual model of BD
BD modelling
The PAM project
61
MDN
2
1
213214 22
N = value of parameter X when normal (λ close to 0.5)D = value of parameter X when depressed (λ close to 0)M = value of parameter X when manic (λ close to 1)
where X = number of phone calls made daily, or number of hours of sleep per 24-hour period
Using λ to model behaviourBD modelling
The PAM project
Time t in days
Mental health state
λ(t)
Actual activity on day t
PAM-detectedactivity on day t
Trigger alert?
Individual’s activity whennormal, manic or depressed
Decision rules
Sensor accuracy
and reliability
BD modelling
The PAM project
Patient types (the P in PAM) • Different people will accept different levels of
monitoring
• Defined on the basis of prodromes rather than sensors
– Patient types 1 to 10 chose a selection of two different prodromes
– Patient types 11 to 19 chose a selection of three different prodromes
– Patient types 20 to 24 chose a selection of four different prodromes
– Patient type 25 chose all five
• Pragmatic choice given vast number of combinations of actual sensors
63
BD modelling
The PAM project
Model outputs• True positive alerts (TP) and false positive alerts
(FP)
• True negatives (TN) and false negatives (FN)
• Average number of days to detect the onset of a depressive episode (ODE)
• Average number of days to detect the onset of a manic episode (OME)
• The ideal would be a very low FP, a very high TP, and very low ODE and OME
64
BD modelling
The PAM project
Example Results for Dataset
65
Patient types Choices of prodromes
Minimum no. of sensors required
ODE (days)
OME (days)
TP (%) FP (%)
Type 25
Activity level
Sleep
Talkativeness
Social energy
Appetite
Accelerometer; GPS; TV usage sensor; Pressure mat; Light sensor; Microphone; Phone
sensor; Camera; Cupboard door sensors
5.90 2.64 87.48 2.12
Type 20
Activity level
Sleep
Talkativeness
Social energy
Accelerometer; GPS; TV usage sensor; Pressure mat; Light sensor; Microphone; Phone
sensor
8.10 3.12 85.22 0.85
Type 22
Activity level
Sleep
Social energy
Appetite
Accelerometer; GPS; TV usage sensor; Pressure mat; Light
sensor; Microphone; Camera; Cupboard door sensors
8.17 3.54 84.16 1.30
Type 21
Activity level
Sleep
Talkativeness
Appetite
Accelerometer; GPS; TV usage sensor; Pressure mat; Light sensor; Microphone; Phone
sensor; Camera; Cupboard door sensors
8.37 4.01 83.65 1.05
BD modelling
The PAM project
PAM Infrastructure Vision
The PAM project
Implications• PAM was found to be inadequate for almost all the
personalised choices of two prodromes only
• PAM was found to be efficient for most choices of three prodromes.
• PAM was found to be less effective for a few specific combinations of personalised prodromal choices, especially ‘sleep’, ‘talkativeness’ and ‘social energy’, or ‘talkativeness’, ‘social energy’ and ‘appetite’, because these prodromes were associated with relatively few observable behaviours
• To be able to effectively offer choices such as these, the PAM system would need to increase the number of their associated observable behaviours
67
BD modelling
The PAM project
{jmb, ehm}@cs.stir.ac.uk 68
example: Context Triggering System1 % respond to changes upon receiving contextual information
2 cds_cts(Trigger,T) :-
3 T2 is T+1,
4 assert(happens(listen_for_connection,T)),
5 assert(happens(make_connection,T)),
6 assert(happens(receive_data,T)),
7 assert(happens(checks_data,T)),
8 assert(happens(listen_for_connection,T2)),
9 ((
10 holdsAt(message(Trigger), T2),
11 assert(initiates(checks_data,prompt(Trigger),T)),
12 assert(terminates(checks_data,message(Trigger),T))
13 );
14 assert(terminates(checks_data,message(Trigger),T))).
The PAM project
{jmb, ehm}@cs.stir.ac.uk 69
Conflict Analysis
• Offline and online analysis looking for conflicts between device rules
• Like FI for call control
• Searching for 5 types of conflict:
– STI, SAI, LI, MAI, MTI• 12 case studies were
developed to explore the conflicts
69
Missed Trigger Interaction occurs when the Context Triggering rules delay the activation
of a home gateway.
The PAM project
{jmb, ehm}@cs.stir.ac.uk 70
Detection Approach
• Prolog-based framework
• Evaluates pairs of feature rules to determine whether they are concordant or conflict
70
Example diagram describing MTI conflict detection rule
The PAM project
{jmb, ehm}@cs.stir.ac.uk 71
Device Priority Approach to Resolution
• Allows precedence across devices without their knowledge of each other
• How it works
1. Resolver receives a list of conflicts, device priorities and device rules
• Priorities are declared as ordered preference lists of particular properties (such as power efficiency, bandwidth minimisation, data integrity, etc)
• Rules may be listed for each property2. Resolver determines rules that should be disabled
71
The PAM project
{jmb, ehm}@cs.stir.ac.uk 72
Analysis Results
72
SAI Case Study
Data Transfer Data Transfer SAI
Data Transfer Data Redirect SAI
Data Redirect Data Transfer SAI
Data Redirect Data Redirect Concordance
MTI Case Study
Notification suppression Notification suppression MTI
Notification suppression Response prompting MTI
Response prompting Notification suppression MTI
Response prompting Response prompting MTI