MARHS : Mobility Assessment System with Remote Healthcare Functionality for Movement Disorders Copyright: UCLA Wireless Health Institute 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

Motivation Worldwide trend of aging societies – Number of older adults expected to be 71 million or roughly 20% of the population by [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]. Copyright: UCLA Wireless Health Institute3

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 Copyright: UCLA Wireless Health Institute4

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 Copyright: UCLA Wireless Health Institute5

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. Copyright: UCLA Wireless Health Institute6

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.

MARHS: System Overview Copyright: UCLA Wireless Health Institute8

Hardware 1. Handgrip Device – Designed by UCLA WHI 2. Force Sensor – FSR TM sensor (Interlink Electronics, Camarillo, CA, US) [2] 3. Communication Toolkit – MSP430 (Texas Instruments, Dallas, Texas, USA) [3] Copyright: UCLA Wireless Health Institute9

Software - Examination Provides various examinations Stores the examination results Copyright: UCLA Wireless Health Institute10

Software – Data Visualization The functionality can be summarized in three-fold 1.Visualization of data analysis on each examination result 2.Visualization of performance analysis on a set of results over time 3.Visualization of results provided by DAU Copyright: UCLA Wireless Health Institute11

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 Copyright: UCLA Wireless Health Institute12

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] Copyright: UCLA Wireless Health Institute13

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 Copyright: UCLA Wireless Health Institute14

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. Copyright: UCLA Wireless Health Institute15

DAU - Interface It also allows users to add, delete and modify feature functions. Then, execute the data analysis  What is being executed? Copyright: UCLA Wireless Health Institute16

DAU - Overview Copyright: UCLA Wireless Health Institute17

DAU – Feature Extraction Copyright: UCLA Wireless Health Institute18 Suppose that M represents the total number of signals (both positive and negative) Then, we can extract total M number of arrays of features.

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 Copyright: UCLA Wireless Health Institute19

DAU – Feature Ranking Feature Ranking – The DAU employs estimated Pearson correlation coefficients to rank the features Copyright: UCLA Wireless Health Institute20

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 Copyright: UCLA Wireless Health Institute21

DAU - Summary Copyright: UCLA Wireless Health Institute22

Pilot Clinical Trial Performed at St. Vincent Hospital, Los Angeles, CA A total of 12 patients of various movement disorders Copyright: UCLA Wireless Health Institute23

Pilot Clinical Trial 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 Copyright: UCLA Wireless Health Institute24

Pilot Clinical Trial Patient Subjective with CIDP Copyright: UCLA Wireless Health Institute25 Patient Subjective with Hypertension Patient Subjective with CVA

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 details Copyright: UCLA Wireless Health Institute26

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! Copyright: UCLA Wireless Health Institute27

Patient with CVA f 36 : 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 f 39 : The spectrum energy of the patient at 2 – 4 Hz. – The selected patients have relatively low energy compared to others Copyright: UCLA Wireless Health Institute28

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! Copyright: UCLA Wireless Health Institute29

Patient with Hypertension f 6 : and f 7 : 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 Copyright: UCLA Wireless Health Institute30

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! Copyright: UCLA Wireless Health Institute31

Patient with CIDP f 1 : Average mag. differences between the target and user response f 36 : 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 Copyright: UCLA Wireless Health Institute32

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) Copyright: UCLA Wireless Health Institute33

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, 2012 Copyright: UCLA Wireless Health Institute34

Thank you Questions? Please feel free to reach me at Copyright: UCLA Wireless Health Institute35