telehealth: opportunities for (communications) research?ctw2012.ieee-ctw.org/ctw_mitra_2012.pdf ·...
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Telehealth:Opportunities for
(Communications) Research?Urbashi Mitra
Daphney Zois, Gautam Thatte & Marco LevoratoUniversity of Southern California
Telehealth:Opportunities for
(Communications) Research?Urbashi Mitra
Daphney Zois, Gautam Thatte & Marco LevoratoUniversity of Southern California
where is the communications?
Telehealth:Opportunities for
(Communications) Research?Urbashi Mitra
Daphney Zois, Gautam Thatte & Marco LevoratoUniversity of Southern California
where is the communications?it’s at the end…
• The KNOWME Network– Highly interdisciplinary adventure– Computer engineering, wireless communications, machine learning, robotics/human motion modeling, preventive health
• Deploy a wireless body area sensing network– Employ off‐the‐shelf sensors– Original application: pediatric obesity– Optimize all layers of the system
• System architecture• Multimodal physical activity recognition• Energy efficient sensor selection• (human) Energy expenditure estimation• KNOWME on real people/free‐living
Objectives
U.S
. 200
3-20
06, A
ges
12-1
9
• What I learned…
KNOWME
Activity Detection for Health
OXI
ACC
ACC
ECG
GPS
biometric waveform
feature
decision
Q: who to listen to and for how long?
• Nokia N95 cellphone fusion center– Alive Technologies ACC and ECG (chest
strap) and mobile‐internal Accelerometer | Pulse oximeter not used
– Cost of Bluetooth measurements >> Cost of Nokia ACC
– Bluetooth implementation imposes TDMA!
Energy constraints are different!
our body sensing network
traditionalsensor network
optimize energyresources at N95
optimize energyresources at sensor
sensors are heterogeneous
• Cell phones are not biometric signal processing devices (yet)
Fusion center capabilities
• Electrocardiograph (ECG)
• Features extracted from data– Used to develop hypotheses
in training– Compared to models in
testing for activity‐detection
From Sensors to Features
• Accelerometer (ACC)
sample = average variance of tri‐axial accelerometer for windowed data
sample sample sample
ECG period(= sample)
• Certain sensors can better discriminate between specific sets of activities
Heterogeneous Sensor Performance
both ACC and ECG
Only AC
COnly ECG
• Certain sensors can better discriminate between specific sets of activities
Heterogeneous Sensor Performance
both ACC and ECG
Only AC
COnly ECG
• Measurements collected at fusion center– Several features extracted from each sensor– Model temporal correlation via AR(1) process
• Features only weakly correlated; assume independence– Some sensors discriminate better in certain cases– Use of feature selection results in low feature‐feature correlation
• A few challenges inherent to optimizing the system…
Signal Model
measurement noise
mean of Gaussian‐modeled featuremeasurement
# of measurements from kth sensorAR(1) parameter
Multihypothesis Testing
• Discriminate between multiple hypotheses– e.g. Lie down, sit, sit&fidget, stand, stand&fidget, walk, run
• “feature” of Bluetooth implementation: TDMA access• Canonical general Gaussian problem
– Each activity ~ multivariate Gaussian
block‐diagonal
N1 x 1N1 x N1
sensors independent
Non‐linear Decision Regions
• Distinct means and covariance matrices for each subject | personalized training
Decision regions for bivariate Gaussians for six activities
Effect of Integer Allocations
• As allocation changes, structure of covariance matrix changes, e.g. for N = 6
• Linear algebraic gymnastics: combinatorial integer optimization Real‐valued vector optimization with continuous N– Real‐time implementation on Nokia N95
Thatte, Li, Lee, Emken, Annavaram, Narayanan, Spruijt‐Metz, and M, IEEE Trans. On Signal Processing, April 2011
• System State: activity performed at time k,
– possible activities supported by the system– probability transition matrix
System State
1: Sit2: Stand3: Run4: Walk
example state spaceand Markov chain
POMDP System Execution
state switches
sensors sensing
state detector
belief state updatesensor selection
decision process
Different from typical POMDPs: dependence on previous state, also control does not affect state, but your ability
to accurately observe the state
• Worst‐case error probability: sensors’ discrimination capabilities
• Energy Cost: transmission power consumption
: communication cost from sensor to cell phone (different for each sensor)
• Total Cost:
Performance Measures
approximated using previous methods
• Imperfect state information requires use of beliefstate
– Sufficient statistics, new update rule needed– ML state estimate
• A general partially observable, stochastic controlproblem
• Solve using DP methods/computationally expensive
State estimation & Optimization
• Can show that cost‐to‐go function is– Positively homogeneous, concave, piece‐wise linear, superlinear
• Suggests an approximation scheme– Minimum Integrated Cost Time Sharing Sensor Selection (MIC–T3S) algorithm
– Use approximate DP to determine solutions, , at corner points of simplex
– Final solution via time‐sharing using belief estimate
Approximation Schemes
Complexity:
• 3 sensors: ACC Mean, ACC Variance, ECG Period• 4 activities: Sit, Stand, Run, Walk•
Simulations
• Statistics from realdata
• Costs from real devices• Energy efficiency
– # ACC Mean
– $ ECG Period
• Detection Accuracy– # ACC mean & ACC var– $ ECG period
• Expect limited useof ECG period
ACC mean
ACC var
ECG period
G. Thatte et al., IEEE Trans. on Signal Proc., April 2011
sitstandrunwalk
• EA: every sensor samenumber of samples
Near‐Optimality of MIC‐T3S
• DP and MIC‐T3S attempt to optimize average total cost• Approximate algorithm (MIC‐T3S) performance coincides with
optimal (DP)
Weight factor λ
Average Total Cost
N = 12 samplesL = 5 horizon length
Trade‐off CurvesAverage De
tection Error
Average Energy Cost
Energy gains achieved by
proposed schemes for detection
accuracy equal to EA
Contributions
• Derived implementable performance approximations for MLactivity detection in WBANs
• Proposed a modified POMDP model to optimize detection inheterogeneousWBANs- Different discrimination capabilities & communication costs of sensors- Limited energy budget of fusion center- Imperfect state information
• Derived:• optimal DP algorithm + three approximate schemes
• Energy gains as high as 68% (versus equal allocation) with 99%accuracy and very few samples!!!
Zois, Levorato, & M, in submission January 2012
Future challenge
States:• Physical activity states
• Contextual states• Emotional states• ACTUATION states
• How to handle state explosion?
• Implications for other large scale (PO)MDPs
the WHOLE system
KNOWME Architecture
• combine health sensors with mobile phone– metabolic: ACC, OXI, ECG– emotional: GSR
three‐tier KNOWME architecture
Internet
Database Server
Web ServerEnd-to-end encryption of sensitive data
Check right to use systemFilter noisy updates
Web enable data access
3GGSMWi-Fi
Doctor
– location: GPS– user initiated: SMS, Tweets,
video/image/voice tags
Mobile Client Design
• KNOWME execution priority less than other mobile applications (incoming/outgoing calls)
• Device manager– maintains identities of different sensors– sources between different sensors
• Data collector– Receives/synchronizes sensor data from device manager– Health records collected, buffered, sent to local storage, transmitter and analyzer
• KMCore elements determined by implemented Bluetooth/TDMA multiple access
Challenges of System Architecture
• Energy consumption issues are significant– N95 performs all coordination,
processing, computation– Employ data buffering, adaptive
sensor throttling, dynamic sensor selection, data transmission choices
• State detection is the most expensive activity– State detection at phone and
transmit?
Lessons Learned
•– Compress data and do state detection at back‐end server?
• Application stability an issue– Designs done on emulator/debugging
• Floating point: need smart implementation of signal processing algorithms
WiFI: compression more expensive
• Machine learning techniques– Support vector
machines– Feature fusion
methods
• Novel feature design– Cepstral– Time‐domain
• Non‐linear methods
Multimodal signal processing
Accelerometer Temporal Feature Extraction
ECG TemporalFeature Extraction
SVM Modelingwith GLDS Kernel
SVM Modelingwith GLDS kernel
ECG Cepstral Feature Extraction
Accelerometer Cepstral Feature Extraction
GMM Modeling
GMM Modeling with HLDA
DetectedActivities
Weighted Sum Fusion
Feature Extraction Activity Modeling Score Level Fusion
-2 0 2-1
-0.5
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0.5
1
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mpl
itude
Lying
-2 0 20
0.1
0.2
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evia
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-2 0 2-1
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Walking
-2 0 20
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Collected ECG Signal
Peak Detection
Phase Alignment
Normalized ECG beats
Amplitude Scaling
DC bias Removal
(a) System overview
(b) ECG pre-processing( c ) Normalized ECG heartbeats
Li, Rozgic, Thatte, Lee, Emken, Annavaram, M, Spruijt‐Metz, Narayanan , IEEE Trans on Neural Systems and Rehab. Eng., August 2010.
• High within individual variability– Sensor placement– Location, user emotion, fitness– ECG signals appear to be more sensitive to robustness issues
• Within a single activity, significant signal variation– How to model?
• Robustness increased via multi‐session training
Lessons learned
What goes around, comes around
• CDMA?
What goes around, comes around
• CDMA?– Significant effort devoted to working around
Bluetooth/TDMA (implemented on phone/on sensors)
– System architecture issues: device management, thread distinction, maintaining buffers for different sensors
What goes around, comes around
• Pros:– Few sensors short spreading sequences– Multi‐user detection complexity comparable
to machine learning methods– No device management, thread distinction,
buffer maintenance all done in signal processing
• E.g. no need to keep track of non‐functioning sensors, etc.
• The Scientific Method– Short term research to drive long term implementation
the BIG Conclusion
• There are some things that are unknowable unless you…– Build a system– Work with domain experts– Deploy the system
• Significant room for theory, algorithm design,
• (comm theory, not yet?) http://www.nle2ndgrade.com
• The Scientific Method– Short term research to drive long term implementation
the BIG Conclusion
• There are some things that are unknowable unless you…– Build a system– Work with domain experts– Deploy the system
• Significant room for theory, algorithm design
• (comm theory, not yet?) http://www.nle2ndgrade.com
• “it’s like having a doctor in your pocket”
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