patient discrimination from in -bottle sensors dataingestible biosensors wearable sensors smart pill...
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Patient Discrimination from In-Bottle Sensors Data
Murtadha Aldeer
Joint work with: Jorge Ortiz, Richard E. Howard, Richard P. Martin
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Few factsHuman lifespans is continuously increasing People aged ≥ 60 years will grow by 250% in 2050 Assistive Health Technology (AHT) increases One strategy: Correct medication
Existing work Proximity sensing-based systems Vision-based systems Ingestible biosensors Wearable Sensors Smart pill bottles/containers
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Ref: Aldeer, Javanmard & Martin; Applied Sys Inovation, vol.2, 2018“A Review of Medication Adherence Monitoring Technologies”
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Motivation-Smart Pill BottlesSmart pill bottles can be the best among other solutions Unobstritive, Battery-powered, Can be equipped with sensors to collect data about user behavior
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ApplicationMedication Adherence Monitoring, Medication non-adherence is a complex problem Smart pill bottles can be used for monitoring medication adherence
(when the medication was taken and by whom)
Drug Abuse Monitoring, ER visits of children (≤5) in the US is the highest due to drug poisoning*, A result from unsupervised medication overdoses Medicine cabinets available at homes are the main sources of medication abuse A real-time monitoring system is required
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*among emergency visits for those aged ≤ 18 years
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How to differentiate users from a pill bottle?Different subjects interact with the pill bottle differently
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IRB number Pro2018001757
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PatientSense architecture
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Two acceleromtersPIP-Tag
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PatientSense
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Data Collection
RF Link
Data Preprocessing
Sensor in the cap
Accelerometer Signal
Patient Discrimination
Training Samples
Classification
𝐾𝐾𝑥𝑥𝐵𝐵𝐾𝐾𝑦𝑦𝐵𝐵𝐾𝐾𝑧𝑧𝐵𝐵𝐾𝐾𝑥𝑥𝐶𝐶𝐾𝐾𝑦𝑦𝐶𝐶𝐾𝐾𝑧𝑧𝐶𝐶
⋮Clustering distances
(K-means)
Extracting pulses
Bag construction
Removing gravity Distance matrix
construction (DTW)
Accelerometer Signal
Sensor in the bottle
Feature Extraction
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Bag of words construction,Building distance matrix
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DTW
Distance matrix
Bag of pulses
……..Extracted pulses
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Why DTW for distance measurement?
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Ref: Keogh & Ratanamahatana; Knowledge and Information Systems (2005) 7“Exact indexing of dynamic time warping”
DTW allows for some elasticity, Computes the differences between the points that are better aligned
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Clustering the DTW (pair wise) distances
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Distance matrix
K means
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Feature construction
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[3 5 3 4 3 0 5 3 0] [1 5 0 1 1 0 3 3 0]𝑓𝑓𝑣𝑣= 3 5 3 4 3 0 5 3 0 1 5 0 1 1 0 3 3 0
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Classification approachTwo class approach Legitimate patient or everybody
else Binary SVM: simple, lightweight in
computations
Multi-class approach Multi-class SVM Random Forest
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Discriminate the patient from any other person
Identify a specific person in a set
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Classification approach
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Evaluation criteria
Number of users Training size Classification algorithm Number of sensors
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Evaluation
Participants 16 healthy adults 14 males; 2 femalesAges: 18 – 64, mean 34.93, SD 11.44Each participants took 10 candy pills
Other settings: Low sampling rate: 10HzCamera is used for ground truth
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Number of users effect
Accuracy over 90%, for different number of usersFor 16 users, accuracy = 94%
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Number of users effect
Training data size: 50%, 60%, 70%, 80%, 90%
Accuracy over 93%, for different training size
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Other learners (Multi-class classification) Multi-class SVM & Random Forest Random Forest gave better performanceWhen focusing on a subset (3 people)*:
Random Forest accuracy: 91%SVM: 87%
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* Ref: U.S. Census Bureau. 2019. U.S. Census Bureau QuickFacts: UNITED STATES.
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Performance with individual sensor
Two-class SVM accuracy: higher than 93% (1 out of 16 users) Random Forest & multiclass SVM: accuracies decline Random Forest accuracy: 82% (3 people) SVM: 75% (3 people)
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Conclusion PatientSense is a patient identification system from a pill bottle uses acceleration traces
PatientSense is AccuratePerforms well with one vs. all situation Off-body
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Questions !
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Thank you
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