MARHS: Mobility Assessment System with RemoteHealthcare Functionality for Movement Disorders

Sunghoon Ivan Lee∗, Jonathan Woodbridge∗, Ani Nahapetian∗†, Majid Sarrafzadeh∗∗Computer Science Department †Department of Computer Science

University of California Los Angeles California State University NorthridgeLos Angeles, USA Northridge, USA

silee@cs.ucla.edu, jwoodbri@cs.ucla.edu, ani@csun.edu, majid@cs.ucla.edu

ABSTRACTDue to the global trend of aging societies with increasing de-mand for low cost and high quality healthcare services, therehas been extensive research and development directed to-ward wireless and remote healthcare technology that consid-ers age-associated ailments. In this paper, we introduce Mo-bility Assessment and Remote Healthcare System (MARHS)that utilizes a force sensor in order to provide quantitativeassessment of the mobility level of patients with movementdisorder ailment, which is one common age-associated ail-ment. The proposed system also enables the remote health-care services that allow patients to receive diagnoses fromclinical experts without his/her presence. MARHS also con-tains a data analysis unit in order to provide informationthat summarizes the characteristics of symptoms of a groupof patients (e.g., patients with a certain type of ailment) us-ing a combination of feature ranking, feature selection, andclassification algorithms. The results of the analyses on thedata from a clinical trial show that the examination results ofthe proposed system can accurately recognize various groupsof patients, such as, patients with (i) chronic obstructive pul-monary disease, (ii) hypertension, and (iii) cerebral vascularaccident with an average accuracy of 90.05%, 82.60%, and93.54%, respectively.

Categories and Subject DescriptorsC.3 [Special-Purpose and Application-basedSystems]; H.4 [Information Systems Applications]:Miscellaneous

General TermsMeasurement, Design, Performance, Experimentation, Hu-man Factors

KeywordsRemote health, movement disorders, force sensors.

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.IHI’12, January 28–30, 2012, Miami, Florida, USA.Copyright 2012 ACM 978-1-4503-0781-9/12/01 ...$10.00.

1. INTRODUCTIONIn the United States, the number of older adults is ex-

pected to reach 71 million, or roughly 20% of the popu-lation, by 2030 [1, 23]. As a result of the worldwide trendtowards aging societies, the care for ailments associated withthe elderly, such as Alzheimer disease, diabetes, and Parkin-son’s diseases (PD), is expected to be in high demand. Weare particularly interested in movement disorder ailmentssuch as PD, stroke, arthritis, and tremor, the most commonage-associated ailments [5]. Movement disorders affect thefunction of motor neurons and, thus, restrict the movementof the body, including upper limbs, gait and speech. Mod-ern medical treatments for movement disorders are combina-tions of medication, surgical operation, and rehabilitation.In order to investigate the consequences of these medicaltreatments, a method to measure the motor performance ofpatients is often required.

Currently, the ailment progress or rehabilitation perfor-mance are assessed by physicians based on observation of thepatient’s motor performance [10, 7], which makes the mea-surement subjective and based on limited ordinal scales [13,7]. This creates a need for quantitative assessment methods,such that the analysis of a patient’s motor performance canbe made more accurate and objective [8, 13]. Moreover, thecurrent assessment systems for movement disorder ailmentsrequire the presence of a physician and demand high-pricedservices. Due to dramatic advances in recent telecommuni-cations and information technology, remote health systemsare considered as solutions that provide quality healthcareservices at low costs. Telehealth or remote health systemsare defined as the application of telecommunications and in-formation technology to the delivery of healthcare, health re-lated services, and health related information beyond phys-ical boarders [16, 4].

In this paper, we introduce Movement Assessment and Re-mote Healthcare System (MARHS). This system providesaccurate and quantitative measurements of handgrip per-formance of patients with movement disorder ailments. Weutilize the handgrip performance of patients to reflect thelevel of mobility because handgrip strength is known as asimple, accurate, and economical bedside measurement ofmuscle function and the progression of the movement disor-ders [13, 6, 15, 17, 19, 20, 11, 18, 14]. MARHS also providesremote healthcare services by supporting rich data analy-sis on patient’s handgrip performance such that physiciansand medical professionals can make diagnoses remotely. Thedata analysis unit (DAU) of the proposed system shows thatthe resulting signals provide valuable information reflecting

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the condition of the patient’s ailment. In order to addressthis, the DAU recognizes and classifies signals of a partic-ular group of interest (e.g., a patient with a particular ail-ment) from other signals. By successfully classifying signalsof a group of interest, we show that the resulting signals ofMARHS contain important information defining the char-acteristics of that group (i.e., a particular ailment). More-over, the DAU provides information about features that con-tribute the most in the classification process, which can beused to define the ailment characteristics of the interestedgroup of patients. The numeric values of these features canbe used to provide quantitative measurements of the level ofmobility. The DAU utilizes a combination of feature rank-ing, feature selection, and classification mechanisms in orderto address the aforementioned tasks.

This paper presents data analysis results of the proposedsystem based on data collected from a clinical trial per-formed at St. Vincent Hospital in Los Angeles, California,USA.

This paper is organized as follows. Section 2 discusses therelated works. Section 3 introduces the hardware and soft-ware architecture design of the proposed system. In Section4, the DAU of the proposed system is discussed in detail.Section 5 presents data analysis results using data collectedfrom a clinical trial. Finally, conclusion and future work isdiscussed in Section 6 and 7, respectively.

2. RELATED WORKSMany studies examined embedded systems for assessing

handgrip strength [10, 6, 15]. The system introduced in[10] monitors the muscle activities using force sensors onthe palm and digits. In [6], a system that monitors the gripstrength and the digit muscle activities is introduced. More-over, in [15], an embedded system that assesses the grip forcebehavior during object manipulation is introduced. The pro-posed work, unlike the aforementioned works, not only in-troduces an embedded system that efficiently monitors thegrip performance, but also discusses how the examination re-sults can be processed to correlate to the subjects’ ailmentconditions using data from a clinical trial.

In [11], authors investigate the handgrip strength and en-durance of healthy subjects and patients, and conclude thathandgrip strength and mobility for patients are strongly cor-related. They measure handgrip strength, endurance, andwork, and perform one-to-one comparisons between eachmeasurement and mobility. In other words, the authors as-sume that the measurements are independent of each otherand, thus, correlation between the combination of thosemeasurements and mobility is not considered. On the otherhand, our system considers the correlation between vari-ous combinations of multiple features and the patient’s dis-ease condition. Additionally, the system proposed in [11]can only process features (i.e., strength, endurance or work)that are pre-defined, but the proposed system allows usersto add or remove features from a feature pool and intelli-gently selects features that are useful to characterize a cer-tain group of patients. Moreover, and most importantly, ourwork presents a system that allows remote healthcare ser-vices integrated with assessment of handgrip performance.

Figure 1: The graphical overview of the proposedsystem. A patient is examined based on various testsprovided by the software and the handgrip device.The results are transmitted to a computer using acommunication tool kit and stored in a database.The examination results and the data analysis re-sults can be remotely accessed by clinical experts.

3. MOVEMENT ASSESSMENT ANDREMOTE HEALTHCARE SYSTEM(MARHS)

The objective of the system is to provide handgrip exam-inations, retrieve signals from subjects, store the results inthe database, and finally analyze the signals in the databasein order to provide information such as features that char-acterizes patients’ ailment symptoms. An overview of theproposed system is illustrated in Figure 1. The proposedsystem can be divided into two subsystems: the hardwaresystem and the software system. The hardware system con-tains physical devices such as the handgrip device, the forcesensor, and the communication device. The major objec-tive of the hardware system is to collect the time-varyinggrip strength of the patient and to send the data streamto the software system. The software system communicateswith the hardware system in order to store the examinationresults in the database, and perform the data analysis onthe results. We discuss the detailed system architecture forboth the hardware and software system in Section 3.1 and3.2, respectively.

3.1 Hardware Architecture

3.1.1 Handgrip DeviceThe handgrip device of MARHS is based on medical de-

vices used by clinical professionals (e.g., handgrip device in[22]), as shown in Figure 2. Our device is composed of 6061t-6 Aluminum and Delrin plastic, which are lightweight andrigid. The black plastic component of the handgrip deviceis composed of Delrin plastic. The two cylinders bridged bythe black plastic (Part-C in Figure 2 (a)) are movable alongthe sideline (Part-B in Figure 2 (a)) such that patients cangrasp the device. The movable component of the handgrip

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(a)

Part-A

Part-B

Part-D

Part-C

(b)

Figure 2: (a) The physiologically designed handgripdevice of MARHS (left) and (b) the handgrip devicecurrently in use (right)

device is bound to the fork-like side (Part-A in Figure 2 (a))of the handgrip by a rubber band. Patients can use rubberbands of different tension force in order to customize thesqueeze force in the handgrip device. Additionally, the fork-like side of the handgrip allows patients to further adjust thetension force by placing the rubber band at different widths.Note that patients can adjust the initial position of the mov-able component of the handgrip device by using adjustablepin holes on the sideline (i.e., Part-B in Figure 2 (a)) of thehandgrip. Thus, grasping performance of various patientswith different size of extremities can be optimized. Finally,a force sensor is attached to Part-D in Figure 2 (a), and itmeasures the force generated from the grasping action.

3.1.2 Force SensorsIn MARHS, a force sensor is attached to the handgrip

device in order to measure the grip strength. We employa commercial FSRTM sensor (Interlink Electronics, Camar-illo, CA, US) [2] in our system. The FSR sensor is a thin-film force sensor that contains a thermoplastic sheet anda conductive polyetherimide film facing to each other [21].When there is no force applied to the FSR sensor, the twosheets are electrically separated, which generates infiniteimpedance. In the case when there is a force applied tothe sensor, the two sheets make electrical contact, and finiteimpedance is generated. As the amount of force appliedincreases, the resistance generated by the FSR sensor de-creases [21]. We employ the FSR sensors in the proposedsystem because (i) the FSR sensor responses accurately andprecisely to the general range of handgrip force, and (ii) theFSR shows good performance in terms of robustness [21].

3.1.3 Communication PortThe communication device delivers the pressure values

captured by the force sensor to the software (Section 3.2). Inour proposed system, an MSP430 (Texas Instruments, Dal-las, Texas, USA) [3] is employed as the communication de-vice. The MSP430 is a communication device that allows theforce sensor on the handgrip device to communicate with thesoftware either in wired or wireless manner. The MSP430uses USB as its communication port at the receiver-side.

The modulation rate is 9600 symbols per second with eachsymbol being 8-bits long. The MSP430 contains a MicroController Unit that allows us to implement software usinga simple handshake mechanism in order to initiate and ter-minate the communication. This dramatically decreases therequired power when the device is communicating wirelessly.

3.2 Software ArchitectureThe examinations provided by the software are designed

such that a patient’s grip strength as well as the precise-ness of a patient’s ability to control the grip strength canbe effectively measured. Due to the various physical condi-tions of different patients, patients have different handgripstrength. Thus, the examinations must be normalized basedon physical conditions of different patients. In order to solvethe problem, the system measures the maximum voluntarycontraction (MVC) and normalizes the examination basedon the value of MVC for each patient. MVC defines that apatient voluntarily grasps the handgrip device with his/hermaximum effort.

Patients may proceed to the actual examination followedby the calibration process. Figure 3 (a) illustrates an exam-ple of an examination using a sinusoidal waveform. Whenthe examination begins, the target waveform (red line) ishorizontally shifted to the left at a constant speed, and thus,users observe a flow of the waveform within the screen. Theblue circle in the middle of the screen corresponds to the levelof pressure acquired from the force sensor. The blue circleis always located in the middle of the x-axis and its positionin the y-axis changes according to the pressure provided bythe patient. The labels on y-axis represent the percentage ofthe acquired pressure compared to the MVCmeasured in thecalibration process. The objective of the examination is tocontrol the grip strength, so that the blue circle can be over-lapped to the waveform as much as possible. In summary,the examination considers both the maximum strength andthe preciseness of patients’ grip control.

The software provides different waveforms including thesinusoidal waveform such as exponential and step-functionwaveform. Moreover, the software provides a number of testparameters, which may change the attributes of the test.For example, patients may change the speed of the wave-form horizontally shifting or the size of the blue circle, whichallows patients to perform the test at different levels of dif-ficulties. Additionally, users may vary the time duration ofthe test.

The result waveforms created by the patients and the an-notative information are stored in the database system inorder to deliver maximum range of information about theexamination. The information stored in the database sys-tem can be remotely retrieved by physicians or clinicians forquality remote healthcare services. Figure 3 (b) illustratesan example of this remote healthcare functionality. This in-terface also allows physicians to provide various inputs tothe DAU (Section 4), and to retrieve the results from theDAU including information such as unique symptoms of aparticular ailment.

The software is programmed in C# .NET and the data-base system is designed in SQL.

4. DATA ANALYSIS UNITIn this section, we introduce the DAU, which extracts im-

portant information from the patient-generated signals and

335

(a) (b)

Figure 3: Screenshots of the software of MARHS. Screenshot in (a) illustrates an example of an examinationon handgrip performance, and screenshot in (b) illustrates an example of the remote healthcare functionalityof MARHS, which allows physicians to retrieve clinical and examination information stored in the database.

summarizes the characteristics of symptoms of patients. Inorder to address this objective, we employ a feature rankingmechanism, a feature selection mechanism, and a classifica-tion algorithm. We classify signals from a group of patientsof particular interest. By successfully classifying signals ofinterest from other signals, we show that the signals gener-ated by MARHS contain valuable information, which mayreflect the characteristics of the selected group of patients.Thus, it allows us to further process the signals to quantifymultiple aspects of patients’ mobility levels. Additionally,the DAU provides information about subset features thatcontribute the most in the classification process using fea-ture ranking and feature selection mechanism. These fea-tures may characterize unique symptoms of patients of ourparticular interest.

4.1 Software InterfaceThe prototype interface of the DAU is illustrated in Fig-

ure 4. The DAU begins with forming a group of signals ofinterest. Throughout this paper, we generically use the termgroup of interest (GOI) to represent the signals in which weare particularly interested to analyze. The software inter-face asks users to define the GOI by checking the checkboxas shown in Figure 4, which is used as the ground truth labelin order to evaluate the classification performance. The GOIcan be flexibly defined in order to characterize different setsof the examination results. For example, we can define theGOI as the signals of patients with Cerebral Vascular Ac-cident (CVA) and compare these signals against the signalsof patients without CVA. If we can successfully classify sig-nals of patients with CVA from other patients, we can claimthat the resulting signals of MARHS contain information(i.e., features), which may define characteristics of patientswith CVA. Note that we also use the term positive signalsand negative signals to define the signals in the GOI andthe signals in the complement of the GOI, respectively. The

DAU interface then allows users to add, delete and modifyfeature functions to be considered in the feature selectionand classification process. Finally, the DAU interface dis-plays the data analysis results when users press a commandbutton. The flow of signals in the DAU is illustrated in Fig-ure 5. Each component in Figure 5 is further discussed inthe following subsections.

4.2 Feature ExtractionThe DAU starts the data analysis process by extracting

features from the examination results. Suppose that we rep-resent a vector of extracted features from a single examina-tion result as

s =[s1 s2 · · · sT

], (1)

where T is the number of features. We further suppose thatwg[n] represents the target waveform (e.g., perfect sinusoidalwaveform) and wr[n] represents the waveform generated bythe patient (e.g., the trajectory of the blue circle in Figure3 (a) over a period of time). Then each feature si can berepresented as a function of wg[n] and/or wr[n],

si = fi (wg[n], wr[n]) .

We define fi (·) as a feature extraction function, which iseither in time-domain or in frequency-domain. An exam-ple of feature extraction functions in time-domain can bethe average difference between the magnitude of wg[n] andwr[n]. This example of the feature extraction function canbe mathematically expressed as

f =1

N

N∑n=1

|wg [n]−wr[n]|, (2)

where N is the length of wg[n] and wr[n]. An example offeature extraction functions in frequency-domain can be themagnitude of harmonics at the frequency range between 3 to6 Hz (patients with Parkinson’s disease are known to have

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Figure 4: A screenshot of the software interface ofthe DAU. This prototype interface allows users todefine a group of signals of interest (i.e., a particularailment) and feature functions to be considered inthe analysis. The system displays the results uponusers’ requests.

tremors of frequency around 3 to 6 Hz). This function canbe mathematically expressed as

f =∑

k∈[3−6Hz]

|Wr[k]|, (3)

where Wr[k] is the Discrete Fourier Transform (DFT) ofwr[n]. Note that the formal example of the feature selectionfunction is a linear function and the latter example is a non-linear function.

Once a set of feature extraction functions is defined, weextract features from all signals. Suppose that M representsthe total number of signals including both positive and nega-tive signals. Then, we can extracts total M number of arraysof features, which can be represented as a two dimensionalarray,

S =

⎡⎢⎢⎢⎣

s1

s2

...sM

⎤⎥⎥⎥⎦ =

⎡⎢⎢⎢⎣

s11 s12 · · · s1Ts21 s22 · · · s2T...

. . ....

sM1 sM2 · · · sMT

⎤⎥⎥⎥⎦ (4)

=[s1 s2 · · · sT

], (5)

where sj with j ∈ [1,M ] is a horizontal array of features asin (1) and si with i ∈ [1, T ] is a vertical array composed ofvalues of a feature fi(·) computed from the entire signals.

4.3 Feature Selection and ClassificationThe DAU runs a feature selection technique based on an

instance of the wrapper approach. The wrapper approachdetermines a set of features that have small contribution inclassifying data based on the results of the feature rankingtechnique [9, 12]. Feature ranking technique is a metric toevaluate the performance of a feature in the classificationprocess. The reason that we employ feature ranking andfeature selection techniques are as follows. First, given apre-defined set of features as in (1), not all of these features

Figure 5: The graphical overview and data flow ofthe DAU. The DAU extracts identical features fromboth positive and negative class data. Then, theDAU performs wrapper approach to select impor-tant features based on the results of the featureranking and classification (LDA).

play important role in classifying a certain GOI. This maylead to a problem during the classification process that acollection of many useless features can be accumulated tooverwhelm some useful features (i.e., overfitting problem).Second, it is more efficient computationally since it filtersout features that are less useful. Lastly, information aboutthe rank of features according to the level of contributionsin the classification process is valuable to us because fea-tures with higher rank can be defined as unique symptomsfound only in that GOI. In MARHS, we employ the esti-mated Pearson correlation coefficients to rank the featuresaccording to the level of correlation to the class labels (i.e.,positive or negative signals). The Pearson correlation coef-ficient for a feature fi, can be estimated using

R(i) =

∑Mj=1

(sji − E(si)

) (yj − E(y)

)√∑M

j=1

(sji − E(si)

)2 ∑Mj=1 (y

j − E(y))2, (6)

where yj with j ∈ [1,M ] represents the class of the signal j(i.e., +1 for positive and −1 for negative class signal) andE(·) represents a function computing the mean value of theinput vector. Then, we use R(i)2 as a feature ranking cri-terion that estimates goodness of linear fit of an individualfeature to the class vector y [9].

Given the rank of all features, the wrapper approach needsto decide (i) how to construct the subset feature search spaceand (ii) which classification algorithm to be used to assessthe performance of different subset features. The simplestsearch space is to consider all possible feature combinationsand perform the chosen classification algorithm to evaluatethe performance for each feature combination. However, forT features, the evaluation must be performed for 2T − 1combinations, which is not computationally feasible. Thus,we utilize the famous forward selection strategy to constructthe search space, which starts with the highest ranked fea-ture and gradually adds a feature that is the next highestranked. Then, the size of the search space is reduced toT − 1. In order to evaluate each feature subset, we uti-lize leave-one-out cross validation with Linear DiscriminantAnalysis (LDA) as the classification algorithm. Each featuresubset is evaluated using the average classification accuracybased on leave-one-out cross validation. Algorithm 1 showsthe pseudo-code for the feature selection algorithm used inthe DAU.

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Algorithm 1Wrapper approach feature selection algorithmusing forward selection

F = [f1, f2, · · · , fT ] {entire feature sets sorted in a de-creasing order of feature ranking from (6)}S {M by T data matrix as in (4)}y {M by 1 matrix of classes as in (6)}V = [φ] {array that will contain average classificationaccuracy of each feature subset}for i = 1 to T do

F = F [1 : i] {selecting the most significant i features}A = [φ]for j = 1 to M do

S′= S − sj {leave-one-out training set}

y′= y − yj {class label for training set}

X′= S

′[:, F ] {considering subset feature}

θ = LDA(X

′, y

′) {training}y∗ = θ ∗ s {predicted class of the testing set sj}if y∗ == yj then

A [j] = 1 {correctly classified}else

A [j] = 0 {incorrectly classified}end if

end forV [i] = E(A) {computing the average classification ac-curacy}

end forn = argmaxi V [i]return [f1, f2, · · · , fn] {subset of features producing themaximum accuracy}

4.4 Other Feature Ranking and ClassificationMethods

In addition to Pearson correlation coefficient, we trieda number of different feature ranking methods such as in-formation theoretic approach or single-feature classificationapproach. We also tried different classification algorithmssuch as Naive Bayes, decision tree, support vector machine(SVM), and adaptive boost (AdaBoost) instead of LDA.However, any combination of the aforementioned methodsdid not show any superior performance compared to the pro-posed combination of Pearson correlation method and LDA.

5. CLINICAL TRIAL AND DATAANALYSIS

This section presents the data analysis results of our sys-tem including the DAU based on data obtained from a clini-cal trial of the proposed MAHRS, performed at St. VincentMedical Center (Los Angeles, CA). A total of 12 patientsubjects participated in this clinical trial. The clinical in-formation about patient subjects is provided in Table 1. Allpatients were examined under the same environment suchthat the examination results are not affected by factors otherthan the mobility performance of subjects. For example,each subject performed the examination while he/she wasseating upright, had elbow flexed 90 degrees with arm closeto the body, and had wrist and forearm resting on a table.Note that all subjects had performed the test using the sinu-soidal waveform as the target waveform with test duration

of 20 seconds in order to make the results comparable toeach other.

This data analysis considers three different GOIs: (i) agroup of patients with Chronic Inflammatory DemyelinatingPolyneuropathy (CIDP), (ii) a group of patients with hyper-tension, and (iii) a group of patients with Cerebral VascularAccident (CVA). These three GOIs are chosen among var-ious ailments described in Table 1 such that each GOI canbe analyzed with a sufficient number of signals. For exam-ple, a total of 24 signals are available for patients diagnosedwith CIDP (Patient Subjects 6 and 8), a total of 25 signalsfor hypertension (Patient Subjects 1, 3, 4, and 5), and atotal of 17 signals for CVA (Patient Subjects 3, 7, 10, and12). Ailments such as Parkinson’s disease or diabetes arenot considered because the number of available signals istoo small to be used to train and test the system. Figure 6illustrates sample examination results of the three GOIs thatwe consider in this analysis. The target waveform (i.e., per-fect sinusoidal waveform) is depicted by the red dotted line,and the patient generated waveform is depicted by the bluesolid line. Figure 6 (a) illustrates sample signals of patientswith CIDP, Figure 6 (b) illustrates samples of patients withhypertension, and Figure 6 (c) illustrates samples of patientswith CVA.

5.1 Feature PoolThis section presents features used in the data analysis in

order to define the characteristics of different GOIs. A totalof 45 candidate feature functions are defined: 36 functionsextracts features in the time-domain and 9 functions in thefrequency domain. These features are further analyzed asexplained in Section 4, and a subset of these 45 featuresis selected to define the characteristics of the tested GOI.The feature functions are denoted as fi with 1 ≤ i ≤ 45,where the first 36 feature functions are in the time-domainand the following 9 feature functions are in the frequency-domain. The first time-domain feature extraction function,denoted as f1, is based on the average difference in magni-tude between the target waveform and the waveform gener-ated by the subject, similar to the equation defined in (2).This function provides the overall level of performance interm of accuracy. f2 computes the maximum instantaneouschange in magnitude of the subject-generated waveform inorder to investigate how well a subject manipulates the gripstrength. f3 computes the minimum time required for thesubject-generated waveform to cross the target waveformfrom the time that the examination begins. This functionprovides possible information about reaction time for thesubject to restore to the target waveform from deviation.The fourth time-domain function, f4, computes the totalnumber of intersections of two waveforms in order to inves-tigate a subject’s ability in order to control the grip strengthto stay near the target waveform. f5 investigates the num-ber of changes in sign of the slope of the patient-generatedwaveform in order to correlate the examination results topossible effect of tremor. f6 and f7 compute the numberof times that the subject-generated waveform crosses hori-zontal lines at magnitude y = 50% and y = 25%, respec-tively. The time-domain functions from f8 to f22 are con-structed as the following. The 20 second-long waveformsgenerated by subjects are quantized into 15 segments of uni-form length in time axis (i.e., each segment contains thedata of 20/15 seconds). Then, f8 to f22 contain the mean

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Table 1: Limited clinical information about patient subjects tested on the proposed system. The last column(i.e., Number of tests) provides the number of tests performed for each subject. Note that associateddiseases of Patient Subject 2, 10, and 11 are not provided due to the participants’ unwillingness to revealtheir information (N/A stands for Not-Available). However, this unrevealed information does not complicateany of the data analysis results provided Section 5.2, 5.3, and 5.4.

Age Gender Primary diagnosis Secondary diagnosis Number of tests

Patient Subject 1 85 Male Arthritis Left Hip Hypertension 8Patient Subject 2 49 Female N/A - 3Patient Subject 3 87 Female Cerebral Vascular Hypertension 6

AccidentPatient Subject 4 73 Male Chronic Obstructive Hypertension 4

Pulmonary DiseasePatient Subject 5 72 Male Intra-Cranial Hemorrhage Hypertension / Diabetes 7Patient Subject 6 73 Male Fracture L Tibia Chronic Inflammatory 6

Demylenating PolyneuropathyPatient Subject 7 65 Female Progressive Debility Multiple CVA 3Patient Subject 8 73 Male Chronic Inflammatory Hypotension/Pancytopenia 18

Demylenating PolyneuropathyPatient Subject 9 74 Male Parkinson’s Disease - 2Patient Subject 10 60 Male N/A CVA 4Patient Subject 11 74 Female N/A - 2Patient Subject 12 61 Female NTBI CVA 4

5 10 15 200

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(a) (b) (c)

Figure 6: Sample signals of subjects with various conditions. The four signals in column (a) belong to patientswith CIDP (the top two signals from Patient Subject 6 and the bottom two from Patient Subject 8 in Table1). The signals in column (b) belong to patients with hypertension (one from each Patient Subjects 1, 3, 4,and 5). The signals in column (c) are sample signals from patients with CVA (one from each Patient Subjects3, 7, 10, and 12).

339

Feature Index

Pear

son

coeff

icien

t

0.00.1

0.20.3

0.4

101320426162933342321735828147382124304415941223731827421125313240194561154339361

Figure 7: The ranking (Pearson coefficients) of fea-tures for GOI composed of patients with CIDP. Theresults show that f1 (the right-most index) providesthe highest correlation to CIDP and f10 (the left-most index) provides the lowest correlation.

0.7

00

.80

0.9

0

Number of features

Ave

rag

e p

reci

sio

n

0 10 20 30 40

Figure 8: The average classification accuracy of GOIcomposed of patients with CIDP over the searchspace using forward selection strategy. The resultsshow that the maximum average accuracy of 90.05%is achieved when the highest 33 features are em-ployed.

values of the magnitude of these segment. These quatizedsegments are used to evaluate the changes in grip strengthover time. Furthermore, the time-domain functions fromf23 to f36 compute the difference in mean magnitude of thetwo neighboring segments. These features are used to eval-uate how fast the grip strength of a patient changes overtime. The frequency-domain functions used in this analysisare computed as the following. The first frequency-domainfunction, f37, computes the average difference in magnitudebetween the DFT of the target waveform and the DFT ofthe subject-generated waveform over all possible frequencyrange. The frequency-domain functions from f38 to f45 di-vide frequency range from 0 Hz to 16 Hz into 8 segmentsof uniform length (i.e., 2 Hz), and compute the spectrumenergy for each segment in order to investigate the tremoreffect at various frequency ranges.

5.2 Patients with CIDPThis section presents the data analysis results when the

GOI is defined as the signals of patients with CIDP. In otherwords, we define the positive-class data as the signals of Pa-tient Subjects 6 and 8 in Table 1, and the negative-class dataas the signals of the rest of patients. Some sample signals inthe positive class are provided in Figure 6 (a). As discussed

Table 2: Summary of the first 5 features of the se-lected 33 features for the GOI composed of patientswith CIDP

Rank Label Pearson coefficient1 f1 0.464042 f36 0.462723 f39 0.435094 f43 0.420605 f5 0.37586

in Section 4, the extracted features from both positive andnegative classes are evaluated using equation (6). Note thatM in (6) represents the total number of signals considered inthis analysis (i.e., M = 67) and T represents the total num-ber of features (i.e., T = 45). Moreover, yj = 1 if the jth

signal belongs to the GOI and yj = 0 otherwise. The eval-uated feature ranking for this analysis is provided in Figure7, where the feature functions are sorted in an increasingorder of their ranking. According to the graph, f1 has thehighest correlation to the class label y and f10 has the lowestcorrelation. As shown in Algorithm 1, we employ forwardselection strategy to construct the search space. We initiallystart with a single feature with the highest rank (i.e., f1),and gradually add the next highest ranked feature until weconsider the entire set of features (i.e., f1, f2, · · · , f45). Fig-ure 8 shows the average classification accuracy (V [i] in Al-gorithm 1) over the search space (i.e., number of features inforward selection strategy). The results show that the max-imum accuracy of 90.05% is achieved when the highest 33features are employed. This implies that the classifier couldsuccessfully recognize signals of patients with CIDP at themaximum rate of 90.05%. Moreover, the highest 33 featuresare the distinct and unique features of patients with CIDP,which makes their signals most distinguishable from otherpatients. In Table 2, we provide information about the first5 features out of the selected 33 features. Detail descriptionsabout these features are provided in Section 5.1.

5.3 Patients with HypertensionA similar analysis has been conducted on the GOI com-

posed of patients with hypertension (i.e., Patient Subjects1, 3, 4, and 5). Some sample signals of hypertension areprovided in Figure 6 (b). As discussed in Section 4, the ex-tracted features are evaluated using equation (6). Note thatthe class label vector y is different from that of the previoussection, since yj = 1 when jth signal belongs to patients withhypertension, and yj = 0 otherwise. The evaluated featureranking of this GOI is provided in Figure 9. Figure 9 showsthat f6 provides the highest correlation to the class labelvector y, and f8 provides the lowest correlation. Moreover,we compute the average accuracy of classifying the patientswith hypertension based on leave-one-out cross validationas explained in Algorithm 1. Figure 10 shows the averageaccuracy over possible search space using forward selectionstrategy. The maximum average accuracy is 82.60%, andthis accuracy is achieved when the highest 2 features areemployed. This implies that the signals of patients withhypertension could be successfully distinguished from otherpatients at the maximum average rate of 82.60%. Moreover,the selected features, f6 and f7, are the two unique features

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Feature Index

Pear

son

coeff

icie

nt

0.0

0.1

0.2

0.3

0.4

81614173433234921241841302324015453135382932720112810444237391336 43

525221911176

26

Figure 9: The ranking (Pearson coefficients) of fea-tures for GOI composed of patients with hyperten-sion. The results show that f6 (the right-most index)provides the highest correlation to hypertension andf8 (the left-most index) provides the lowest correla-tion.

0.70

0.75

0.80

Number of features

Ave

rag

e p

reci

sio

n

0 10 20 30 40

Figure 10: The average classification accuracy ofGOI composed of patients with hypertension overdifferent number of features. The results show thatthe maximum average accuracy of 82.60% is achievedwhen the highest 2 features are employed

that characterize patients with hypertension, which makestheir signals the most distinguishable.

5.4 Patients with CVAThis section shows the data analysis results of MARHS

when the GOI is defined as signals of patients with CVA.In other words, signals of Patient Subjects 3, 7, 10, and 12are labeled as positive signals and the signals of the rest ofpatients are labeled as negative. Some sample signals of pa-tients with CVA are shown in Figure 6 (c). The featuresdefined in Section 5.1 are evaluated based on equation (6),and the results are displayed in Figure 11. This figure showsthat f36 has the highest correlation with CVA, and f9 hasthe least correlation. The average classification accuracy forCVA is computed in the search space created by forward se-lection. The results are displayed in Figure 12. It shows thatthe maximum average accuracy is 93.54%, and the highest2 features are used. This implies that the signals of patientswith CVA could be successfully recognized from other pa-tients at the maximum average rate of 93.54%. Moreover,the highest two features, f36 and f39, are the distinct andunique features that characterizes patients with CVA, whichmakes their signals most distinguishable.

Feature Index

Pear

son

coeff

icie

nt

0.0

0.2

0.4

0.6

820162210744232263334133514274119392324431214028124525296173051811381514337423936

Figure 11: The ranking (Pearson coefficients) of fea-tures for GOI composed of patients with CVA. Theresults show that f36 (the right-most index) providesthe highest correlation to CVA and f8 (the left-mostindex) provides the lowest correlation.

6. CONCLUSIONThis paper presents the Mobility Assessment and Remote

Healthcare System (MARHS), which provides quantitativemeasurements on handgrip performance for patients withmovement disorders. MARHS also provides remote health-care services by allowing the patients to receive diagnosesfrom clinical experts remotely. We discussed the hardwareand software architecture of the system. We also intro-duced the DAU, which shows that the examination resultsof MARHS contain information reflecting the characteris-tics of various ailments. In other words, examination resultsproduced by MARHS can be used to diagnose the diseaseprogress or condition of a patient and, thus, quantitativemeasurements computed based on these examination resultscan provide valuable information. We have performed threedata analysis using signals that we acquired from a clini-cal trial at St. Vincent Medical Center (Los Angeles, CA).The first analysis was to classify signals of patients withCIDP, and we achieved the maximum average classificationaccuracy of 90.05%. We also performed analyses to clas-sify signals of patients with hypertension and CVA, andwe achieved the maximum average accuracy of 82.60% and93.54%, respectively. These results, which show high classi-fication accuracy, help us to conclude that the examinationresults of MARHS may be utilized as a metric to quantifythe disease progress and condition of a patient.

7. FUTURE WORKThere exist many potential research directions that we

can be pursued in the future. We can perform experimentsshowing the correlation between the examination results andthe effectiveness of a certain surgical or therapeutic event.For example, observing any changes in measurements be-fore a neurological surgery and after a surgery may providevaluable information about the consequence of the surgicaloperations on movement performance. Additionally, we canutilize a handgrip device that can measure the grip strengthin a standard unit, such as Newton or kilogram. This designmay provide a wider range of feature extraction functions,which allows us to perform various experiments related todisease progress over time. Our research group has success-fully implemented a new handgrip design using a spring and

341

0.86

0.88

0.90

0.92

Number of features

Ave

rag

e p

reci

sio

n

0 10 20 30 40

Figure 12: The average classification accuracy ofGOI composed of patients with CVA over differentnumber of features. The results show that the max-imum average accuracy rate of 93.54% is achievedwhen the highest 2 features are employed.

a position sensor, and it is currently undergoing another setof clinical trials.

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