marhs : mobility assessment system with remote healthcare functionality for movement disorders...

Copyright: UCLA Wireless Health Institute

MARHS: Mobility Assessment System with Remote Healthcare Functionality for

Movement Disorders

Sunghoon Ivan Lee*

Jonathan Woodbridge*

Ani Nahapetian*†

Majid Sarrafzadeh*

*Computer Science, UCLA

†Computer Science, CSUN

Wireless Health Institute (WHI) - UCLA

• Campus Community– School of Medicine– Medical Center– School of Engineering– School of Nursing – School of Public Health– College of Letters & Science– Anderson School of Management

• Unique approach– End-to-end integration from

sensing to medical informatics to call center

– Develop and verify new healthcare methods and services

– Establish standards for efficacy, reliability, interoperability, and security

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Motivation

• Worldwide trend of aging societies– Number of older adults expected to be 71 million or roughly 20% of

the population by 2030. [1, 23]

• Need for the care for age-associated ailments– Alzheimer– Diabetes– Parkinson’s Diseases

• We are particularly interested in movement disorder ailments such as Parkinson’s disease, stroke, arthritis, and tremor, the most common age-associated ailments [5].


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Motivation• Movement Disorders

– Affects the function of motor neurons restrict movements (limbs,

gait or speech)• Drawbacks of the current systems

– Currently, assessment method is subjective and based on limited ordinal scales [7, 10, 13]

– Or the equipment is too expensive or very large in size– Requires the presence of a physician or clinical professionals.– Requires additional software to log the results


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Objective• Highly mobile and efficient device to assess the motor

performance of patients to enable remote healthcare• Accurate and quantitative measurements on motor performance

• We developed a handgrip device to bring such services– Grip muscle control ability is a simple, accurate and economical bedside

measurement of muscle function and the progression of the movement disorders[13, 6, 15, 17, 19, 20, 11, 18, 14].

• Fully automated system that enables remote healthcare services – User (both patient and doctors) friendly interfaces– Automated system from capturing, storing, to retrieving.– Rich data analysis provided by Data Analysis Unit (DAU) of our system


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Data Analysis Unit

• The DAU can show that the resulting signals provide valuable information reflecting the condition of patient ailments.– The DAU simulates the classification of signals of particular interest

from other signals.• Patients with CIDP vs. all other patients

– By successfully classifying signals of a group of interest, we show that the resulting signals of the proposed system contain important information defining the characteristics of that group

– The DAU also provides information about features that contribute the most in the classification process.

• The DAU utilizes a combination of feature ranking, feature selection and classification mechanisms.


Data Analysis Unit

• In summary, the DAU further extends the range of information to enable remote healthcare services.

• We present analysis results on data collected from a pilot clinical trial performed at St. Vincent Hospital in Los Angeles, California, USA.

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MARHS: System Overview


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Hardware• 1. Handgrip Device

– Designed by UCLA WHI

• 2. Force Sensor– FSRTM sensor

(Interlink Electronics, Camarillo, CA, US) [2]

• 3. Communication Toolkit– MSP430 (Texas Instruments, Dallas, Texas, USA) [3]


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Software - Examination• Provides various examinations• Stores the examination results


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Software – Data Visualization• The functionality can be summarized in three-fold

1. Visualization of data analysis on each examination result2. Visualization of performance analysis on a set of results over time3. Visualization of results provided by DAU


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Software – Data Visualization• Visualization of data analysis on each examination result

– Provides well-known metrics to assess the motor performance (i.e., Root mean square error (RMSE))

– Provides more advanced graphical interpretation


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Software – Data Visualization• Visualization of performance analysis on a set of results over

time– RMSE is used to computer the overall motor performance of force

tracking tasks [Kurillo et al. 2004, G. Kurillo and et. al. 2005, Sharp and Newel 2000]


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Software - DAU

• Question: Do these examination results contain some important information about a patient or a certain group of patients that we are interested in?

• Performs comparative analysis of the examination results that can be used to summarize the characteristics of symptoms of patients.– Feature ranking & feature selection & classification algorithm


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DAU - Interface

• The DAU begins with forming a group of signals of interest.– We use the term group of interest (GOI) to generically represent the signals in which we

are particularly interested in analyzing.– E.g., patients with Cerebral Vascular Accident (CVA) and compare these signals against

the signals of patients without CVA.


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DAU - Interface

• It also allows users to add, delete and modify feature functions.

• Then, execute the data analysis What is being executed?


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DAU - Overview


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DAU – Feature Extraction


• Suppose that M represents the total number of signals (both positive and negative)

• Then, we can extract total M number of arrays of features.

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DAU – Feature Selection & Ranking

• The DAU runs a feature selection technique based on an instance of the wrapper approach.

• The wrapper approach determines a set of features that have small contributions in classifying data based on the results of the feature ranking technique


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DAU – Feature Ranking

• Feature Ranking– The DAU employs estimated Pearson correlation coefficients to rank

the features


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DAU – Feature Selection

• Feature Selection– It utilizes the famous forward selection strategy to construct the

search space– It starts with the highest ranked feature and gradually adds a feature

that is the next highest ranked.– It then evaluates each feature subset based on leave-one-out cross

validation with Linear Discriminant Analysis (LDA) as the classification algorithm


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DAU - Summary


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Pilot Clinical Trial

• Performed at St. Vincent Hospital, Los Angeles, CA• A total of 12 patients of various movement disorders


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• This data analysis considers three different GOIs:

1. a group of patients with Chronic Inflammatory Demyelinating Polyneuropathy (CIDP)– Patient Subject 6 and 8

2. a group of patients with hypertension– Patient Subjects 1, 3, 4, and 5

3. a group of patients with Cerebral Vascular Accident (CVA).– Patient Subjects 3, 7, 10, and 12


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Patient Subjective with CIDP


Patient Subjective with Hypertension

Patient Subjective with CVA

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Feature Pool

• Total 45 different candidate features are considered.– Mean Absolute Difference between the target

waveform & subject-generated waveform– Maximum instance change in magnitude– Magnitude of FFT of the subject-generated waveform

at different frequencies..– etc..

• Please reference the paper for detailsCopyright: UCLA Wireless Health Institute

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Patient with CVA• Positive signals defined as CVA

• The maximum classification accuracy is 93.54% • (Precision = 83.4%, Recall = 89.9%)• The precision is achieved when the highest 2 features are employed!


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Patient with CVA

• f36: the difference in the avg. mag. Errors of the last two temporal segments– Maybe the selected patient dramatically loses the grip control towards to end of the test

• f39: The spectrum energy of the patient at 2 – 4 Hz. – The selected patients have relatively low energy compared to others


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Patient with Hypertension• Positive signals defined as Hypertension

• The maximum classification accuracy is 82.6% (Precision = 82.0%, Recall = 93.0%)

• The precision is achieved when the highest 2 features are employed!


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Patient with Hypertension

• f6: and f7: the top two features compute the number of times that the patient response waveform crosses horizontal lines at magnitude y = 50% and y = 25%, respectively– the selected patients show stronger tremor effects when the magnitude of the grip force is

equal to or lower than 50% of their MVC


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Patient with CIDP• Positive signals defined as CIDP

• The maximum classification accuracy is 90.05% (Precision = 73.4%, Recall = 100%)

• The precision is achieved when the highest 33 features are employed!


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Patient with CIDP

• f1: Average mag. differences between the target and user response• f36: the difference in the avg. mag. Errors of the last two temporal segments

– the selected patients can maintain the grip preciseness (or grip strength) until the very end of the examination


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Conclusion

• Our work presents MARHS that provides – quantitative measurements on handgrip performances for

patients with movement disorders.– remote healthcare services with rich analysis results

• We discussed– Hardware & software architecture– DAU that performs comparative analysis of captured signals.

• We presented– Data analysis results from a pilot clinical trial at St. Vincent

Hospital (Los Angeles, CA)


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Future Works

• Providing the measurement in a standard unit– Newton, Kg, or Lb.– Spring with known coefficient – Position sensor

• More longitudinal study about a single patient– Observing any changes in measurements before a

neurological surgery and after a surgery.– A collaborated research with Dr. Daniel Lu of UCLA

Department of Neurosurgery: Started Jan 22, 2012Copyright: UCLA Wireless Health Institute

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Thank you

• Questions?

• Please feel free to reach me at [email protected]


marhs : mobility assessment system with remote healthcare functionality for movement disorders...

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