ieee sensors journal, vol. 15, no. 10, october 2015 5859...

IEEE SENSORS JOURNAL, VOL. 15, NO. 10, OCTOBER 2015 5859

Ambulatory Monitoring Using PassiveComputational RFID Sensors

Asanga Wickramasinghe, Student Member, IEEE, and Damith C. Ranasinghe, Member, IEEE

Abstract— Rapidly emerging batteryless sensors are creatingtremendous opportunities for truly wearable sensors foractivity recognition. However, data streams from such sensorsare characterized by sparsity and noise, which make activityrecognition a challenging task. In this paper, we study thefeasibility of passive computational RFID sensors for ambulatorymonitoring. In particular, we focus on recognizing transfers out ofbeds or chairs and walking. Ideally, all these activities need to bemonitored by movement sensor alarm systems to alert caregiversto provide supervision during the ambulation of older people inhospitals and nursing homes to prevent a fall. Our novel approachto partition continuous sensor data on natural activity boundariesand to identify transfers out of beds or chairs and walking astransitions between sequences of movements overcomes issuesposed by the sparsity and the noise. We demonstrate throughin-depth experiments the high performance (F-score > 93%) andthe responsiveness of our approach.

Index Terms— Passive computational RFID sensors,body-worn sensors, activity recognition, ambulatory monitoring,natural activity boundary segmentation.

I. INTRODUCTION

FALLS are detrimental to patients. In hospital settings,30% of falls have resulted in injuries and 4-6% of fallers

sustained serious injuries including fractured skull, subduralhaematoma, excessive bleeding and even death [1]. The costof falls related injuries in Australia alone in 2011 is reportedto be A$498.2 million and this is estimated to increase uptoA$1375 million by 2051 [2].

One of the recommended falls prevention strategies toreduce falls in hospitalized patients is to increase monitoringopportunities [2]. This is most commonly achieved in practiceusing alarm systems [3]–[6]. These systems aims to identifypatient transfers out of a bed or a chair and walking [7], andsubsequently alert caregivers with the aim of staff promptlyattending to the patient; thereby potentially reducing the riskof a fall or providing immediate assistance in case of afall [3]–[6], [8]. However, current technologies such as those

Manuscript received February 5, 2015; revised June 5, 2015; acceptedJune 10, 2015. Date of publication June 25, 2015; date of current versionAugust 14, 2015. This work was supported in part by the CollaborationsPathways Program through the Department of State Development,Government of South Australia, and in part by the Australian ResearchCouncil under Grant DP130104614. The associate editor coordinatingthe review of this paper and approving it for publication wasProf. Octavian Postolache.

The authors are with the University of Adelaide, Adelaide,SA 5005, Australia (e-mail: [email protected];[email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSEN.2015.2449862

based on pressure sensors on beds for bed exiting activityrecognition are confronted with large numbers of false alarms,delays and limited to identifying bed-exits since sensors areused to instrument furnitures [3]–[5]. As oppose to sensorsattached to the environment, use of battery powered body-worn sensors for Activity Recognition (AR) have been studiedextensively in previous research [9]–[17], but their applicationto monitor older people have been limited by the obtrusiveand bulky nature (≈ 30 grams [8]) of these devices as wellas requirements such as maintenance of batteries.

The ability to wirelessly power an emerging class ofbody-worn sensors exemplified by passive (batteryless)computational Radio Frequency Identification (CRFID)sensors [18] is creating new possibilities for human motionanalyis. Passive sensors have a distinctive advantage as unob-trusive and easy to wear [19] devices for activity recognitionbecause they are: i) batteryless; ii) lightweight; and iii) small.Consequently, such sensors are ideal as wearable sensorsfor older people where inconspicuousness, wearing comfortand ergonomic requirements are significant considerationsfor translation of technology into practice [20]. Therefore,we consider the use of a passive body-worn CRFID sensorto monitor ambulatory movements: i) transfers out of bed(bed-exits); ii) transfers out of chair (chair-exits); and iii) walk-ing, to realise an ambulatory monitoring framework suitablefor a movement sensor alarm system.

Despite the clear advantages of passive sensors, their datastreams have two unique characteristics, namely sparsity andnoise. Sparsity (low data rates and variable time elapsesbetween sensor observations) emanates from the limited capac-ity of passive sensors to regularly transfer sensor data to datasinks because passive sensors are constrained by their abilityto harvest adequate power, as highlighted in Section II, priorto acquiring data from an on-board sensing device such asan accelerometer [18], [21]. Furthermore, inadequate powerto the on-board sensor as well as having to sacrifice accuracyand precision of sensor device measurements, for example useof lower resolution, to achieve lower power consumption ofsensing circuitry results in increased noise in the acquired data(measurement noise).

In this article we investigate the feasibility of recognizingactivities from a single passive body-worn CRFID sensorattached over clothing and develop an innovative frameworkcapable of partitioning sparse data streams at approximateactivity boundaries in real-time and an approach for recogniz-ing transfers out of bed or chair that overcomes the sparsityand noise in sensor observations. The proposed framework is

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5860 IEEE SENSORS JOURNAL, VOL. 15, NO. 10, OCTOBER 2015

Fig. 1. (a) WISP indicating the MSP430F2132 microcontroller (MCU) andADXL330 3 axes accelerometer. (b) The body-worn WISP indicating theacceleration sensor reference frame. (c) Block diagram of the WISP with anaccelerometer.

evaluated in an in-depth study with ten volunteer participants.Our contributions in this paper are as follows:

1) We propose two data stream partitioning methods basedon detecting natural activity boundaries from sensor datastreams to overcome the limitations in using conventionaldata stream partitioning methods (Section III-A). Theseschemes are simple, inexpensive, bears no assumptions onsampling rate and relies only on received sensor obser-vations and therefore suitable for real-time applications.

2) We propose an ambulatory movement detection algorithmto identify activity transitions to address the issues posedby inadequate sensor observations (sparsity) to directlyrecognise ambulatory movements (bed-exits, chair-exits,and walking) (Section III-C). This algorithm relies onrecognised activities from activity prediction models(Section III-B) trained using a passive sensor specificfeature set (Section III-B) and subsequently re-evaluatingthe predictions to identify ambulatory movements.Our algorithm filters predictions from learnt activ-ity prediction models to reduce false alarms resultingfrom misclassifications caused mainly by noisy sensormeasurements.

3) We conduct detailed experiments using data gatheredfrom a study with ten volunteer participants to evaluateour approach (Section IV). Through our experimentswe empirically demonstrate the ability of our approachto successfully overcome the challenges posed bypassive sensors for ambulatory monitoring. As a result weobserved successful identification of the three ambulatorymovements (i.e. bed-exit, chair-exit and walking).

II. PASSIVE COMPUTATIONAL RFID SENSORS

In this study we utilise a WISP1 (Wireless Identificationand Sensing Platform) [18] tag, which is a passive CRFIDembedded with a 3D accelerometer (ADXL330), illustratedin Figure 1. Like any passive RFID tag, the WISP is

1Details of the WISP can be obtained from: https://wisp.wikispaces.com

Fig. 2. Sensor data acquisition approached from passive CRFID sensors.(a) Reading from tag memory. (b) Embedding within ID.

powered (harvests power from the radiation emitted by theRFID antennas) and read by standard off-the-shelf Ultra HighFrequency (UHF) RFID readers.

In a real world situation, sensor observations from a WISPare sparse because successfully powering and reading theWISP depends on a large number of parameters, including:i) distance of the WISP from the reader antennas; ii) occlusionof the WISP from radio frequency opaque objects such as thehuman body; iii) multipath effects leading to signal cancel-lation at the reader or WISP antenna; iv) interference fromother users of the radio spectrum; and v) the lying posture ofthe WISP wearer on the bed [21], [22]. Further compoundingthe issue of sparsity is the fact that data acquisition frompassive CRFID sensors is non-deterministic. Instead of sensorstransmitting data at a regular rate to data sinks, RFID readersinitiate sensor activation and data acquisition from passiveCRFID sensors in the field of view of readers’ antennaswhere media access is controlled by a random access protocol(e.g ISO-18000-6C [23]).

Mainly, there are two approaches for sensor data acquisitionfrom CRFID sensors: i) reading sensor data logged innon-volatile tag memory; and ii) directly embedding sensordata within a tag Identifier (ID). Figure 2a abstracts the oper-ation of the first approach where the sensor data is first storedin the tags’ non-volatile memory and then read similar toreading user data stored in a regular RFID tag using theREAD command defined in ISO-18000-6C [23] after singu-lating the tag using the QUERY command. As also discussedin [24] and [25], this approach requires considerable amountof power and incurs delays as data needs to be written toand read from a non-volatile memory. Although embeddingsensor data in a tag ID sacrifices the range of the unique tagidentifiers, this approach eliminates the energy intensive andtime consuming operation of writing sensor data to the tags’non-volatile memory as shown in Figure 2b, and hence CRFIDsensors are expected to have a longer communication rangeand a higher data rate compared to the first approach. Similarto the first approach, in the second approach sensor is sampledwhen adequate energy is harvested, but is kept in the volatilememory of the tag. During the next inventory round, which ismarked by a QUERY command, the CRFID sensor transmitssensor data by embedding them in the tag ID.

WICKRAMASINGHE AND RANASINGHE: AMBULATORY MONITORING USING PASSIVE COMPUTATIONAL RFID SENSORS 5861

Fig. 3. 96 bit EPC tag ID format used to acquire sensor data during ourexperiments.

We used the data in tag ID approach for sensor dataacquisition from the WISP to achieve a longer communicationrange and a higher data rate. Figure 3 illustrates the 96 bitElectronic Product Code (EPC) tag ID format re-defined forsensor data acquisition. Here the EPC is logically partitionedinto 3 sections: i) Tag type (8 bits); ii) Sensor data (64 bits);and iii) Tag ID (24 bits). The Tag type is used to identifythe type and hence capabilities of the tag. The Sensor datasection is used to embed acquired sensor data. We only utilized30 bits in this section to embed acceleration data as shownin Figure 3, because each acceleration axis was sampled at10 bit resolution. The Tag ID section is composed of the WISPhardware version and the tag serial number, and a WISP canbe uniquely identified using this Tag ID. Using this approach,when the WISP is adequately powered, a data stream with anupper bound of 40 Hz sampling rate can be obtained.

From each datum sent by a WISP (hereafter referred toas the sensor) and received by a reader, we obtain the5-tuple [a f , al , av , RSSI, aID] where a f , al and av representfrontal, lateral and vertical accelerations measured with respectto the acceleration sensor (Figure 1b), RSSI (Received SignalStrength Indicator) represents the strength (power) of the radiosignal of a sensor observation sent by the sensor and receivedby a specific antenna and measured by an RFID reader, andaID represents the identifier of the antenna that captured theobservation.

Figure 4 illustrates series of sensor observations collectedduring our experiments (see Section IV-A) where it clearlyshows: i) limited number of sensor observations during activ-ities such as transferring out of bed; and ii) variable timeelapses between sensor observations and low sampling rate.As a result there are two key challenges we must overcomebefore using CRFID sensors to recognise ambulatorymovements.

Firstly, recognizing activities of small duration such astransferring out of a bed is inherently challenging due to sparsesensor observations. For instance, during the transfer out ofbed (bed-exit) between time interval 58 s and 60 s in Figure 4,only two sensor observations were received and this limitedsensor information makes direct recognition of the transfer outof bed extremely difficult using machine learning methods.

Secondly, it is common to partition (segment) data streamsto extract number of features (see Section III-B) that aredescriptive of movements for machine learning based activityrecognition because: i) a single sensor observation is inade-quate to describe the movement; and ii) to provide contextualinformation for the current activity [26]. Features extractedfor ambulatory monitoring based on conventional data streampartitioning methods, i.e. fixed time and fixed sample, areover influenced by the inclusion of sensor observations from

past activities and subsequently deteriorate overall activityrecognition performance [9], [26], [27]. This is more detrimen-tal when there are limited number of sensor observations fora particular activity. For instance use of a fixed sized segmentof 512 observations as used by Bao and Intille [9] will containall the activities shown in Figure 4 into one partition and willbe treated as a single activity for extracting features.

III. AMBULATORY MONITORING FRAMEWORK

As shown in Figure 5, our proposed ambulatory monitoringframework consists of three stages:

1) Real-time segmentation of sparse data stream onapproximate activity boundaries that not only leads toextraction of features that are not influenced by previousactivities, but also ensures that we can make a predictionfor each activity.

2) Prediction of activities having sufficient sensorobservations, such as in-bed, standing, walking andin-chair, by extracting features based on segmentsobtained from the first stage increases the activityrecognition performance by only predicting activitieswith sufficient information. Thus we can reduce activitymisclassifications of ambulatory movements, such astransferring out of bed (Figure 4), due to limited sensorobservations.

3) Re-evaluation of activity predictions so we can mitigatepossible instances of misclassifications and subsequentfalse alarms caused mainly due to noise in the datastream. Detecting activities such as bed-exits and chair-exits as transitions between predicated activities whichwe otherwise cannot directly determine.

We elaborate on the proposed framework in detail in thefollowing sections.

A. Real-Time Stream Segmentation

We propose two real-time segmentation methods basedon detecting activity boundaries in the sensor data streamby exploiting the changes in trunk positions during activitytransitions (e.g sitting on a bed or a chair to standing).As shown in the Figures 6a and 6b, sensor wearer’s trunkdepicts various levels of inclinations or tilts on sagittal (θ ) andcoronal planes (α). These trunk inclinations can be estimatedusing the instantaneous acceleration data a f , al and av for

each sensor observation as θ ≈ tan−1(a f /√

a2l + a2

v ) and

α ≈ tan−1(al/av ). Here, acceleration due to human motion isassumed to be negligible with respect to gravity.

We can represents the i th sensor observation at time ti ,ti > ti−1, i ∈ N on the sparse data stream using thepair (ti , si ). Here, si = [a f , al , av , RSSI, a I D], is a 5 tupleobtained from the sensor and the sequence of collecteddata {(ti , si )}i≥1 i ∈ N is a non-uniform time series. Given twoconsecutive sensor observations, si−1, and, si , we define anActivity Boundary Score (ABS) for the sensor observation, si ,as ABSi = |θi−θi−1|+|αi−αi−1|. During activity transitions(e.g sitting-on-bed to standing) a sudden increase in ABS,which we refer to as trunk tilt peaks, is observed due to


Fig. 4. Series of sensor observations collected during experiments (see Section IV-A) indicating the sparse distribution of sensor observations with respectto time and limited number of sensor observations during activity transitions.

Fig. 5. Ambulatory monitoring framework.

Fig. 6. (a) Angle θ . (b) Angle α. (c) Trunk tilt based activity boundarydetection for segmentation (A:sitting-on-bed; B:lying-on-bed; C:standing;D:walking; E:sitting-on-chair).

rotational movements of a person’s trunk. Therefore, trunk tiltpeaks are indicative of activity transitions and hence possibleactivity boundaries.

However, not all trunk tilt peaks correspond to activityboundaries. Our preliminary experiments revealed the feasi-bility of using standard deviation of the ABS, ABSsd , in adataset to select trunk tilt peaks that are more likely to beassociated with activity boundaries. We defined a model withthe condition (ABSi−1 < λABSsd)∧ (ABSi ≥ λABSsd) thatdetects a leading edge of the trunk tilt peak and defines anactivity boundary at time, ti . Here λ, the segmentation parame-ter, controls the sensitivity of the segmentation approach andcan be found using cross validation as discussed in Section IV.

Algorithm 1 Non-Overlapping SegmentationRequire: λ, �tmin , �tmax , (ti , si )i≥11: buffer.clear()2: for i = 0 to T do3: if isEmpty(buffer) then // start a new segment4: tstart ← ti5: end if6: buffer.add((ti , si ))7: if (isActivityBoundary(ti ,λ) and (ti − tstart > �tmin )) or

(ti − tstart ≥ �tmax ) then8: segi ← buffer9: buffer.clear()

10: output segi and continue11: end if12: end for

Figure 6c illustrates activity boundary scores, detectedactivity boundaries when λABSsd = 0.25 and the actual activ-ities using our experimental data set. It is noteworthy that amajority of the sensor observations within detected boundariesare from a single activity (class) even though activities havebeen fragmented (multiple boundaries within the duration ofa single activity). Therefore, we can expect the data streamsegments from our approach to contain information related toa specific activity as opposed to multiple activities.

The proposed activity boundary detection method can beused for efficient real-time sensor data stream segmentationbecause it is computationally simple and relies on processingindividual raw sensor observations, si . Furthermore, it is clearfrom Figure 6c that activity boundaries that are detectedin close proximity can be merged and we achieve this bydefining a temporal constraint (minimum time interval betweenboundaries) on segmentation sizes. Using our activity bound-ary detection method we define two segmentation methods:i) Non-overlapping; and ii) Overlapping.

Non-Overlapping Segmentation (NS): In the non-overlapping segmentation method presented in Algorithm 1,the data stream is partitioned into blocks based on the detectedactivity boundaries. We define maximum �tmax and minimum�tmin segment sizes in terms of time (temporal constraints).

In Algorithm 1, sensor observations are collected untileither: i) an activity boundary is detected where the segmentsize is larger than �tmin ; or ii) segment size is �tmax (line 7in Algorithm 1). Here, delay is bounded by �tmax and


Algorithm 2 Overlapping SegmentationRequire: λ, �tmin , �tmax , (ti , si )i≥11: buffer.clear()2: for i = 0 to T do3: δt ← 0 k ← i4: buffer.add((ti , si ))5: while ti − tk < �tmax do6: if isActivityBoundary(tk ,λ) then7: if ti − tk < �tmin then8: δt ← �tmin9: end if

10: break11: end if12: δt ← ti − tk k ← k − 113: end while14: segi ← buffer.last(δt)15: buffer.retainRecent(�tmax )16: output segt and continue17: end for

TABLE I

FEATURES EXTRACTED FROM ACCELERATION DATA

taking this to consideration a suitable value for �tmax canbe selected. Then, �tmin specifies the time period at whichpossible activity boundaries near to each other are mergedand a suitable value for �tmin can be selected by consideringactivity transition durations.

The segment size or the level of segmentation can be alteredby varying the segmentation parameter (λ) in the boundarydetection method. High values of λ will result in identificationof boundaries that spans multiple activities and these will beamalgamated into one activity and subsequently deteriorate theclassification performance. Therefore, selection of an appropri-ate value for λ for the non-overlapping segmentation methodis important.

Overlapping Segmentation (OS): The overlappingsegmentation method illustrated in Algorithm 2 generates asegment for each sensor observation, (ti , si ), and thereforeincurs no delays in predicting the associated activity. Here,�tmax determines the amount of historical data to be retainedin a segment and, as with NS before, �tmin is used toconstrain the minimum size of a segment.

B. Activity Prediction

1) Feature Representation: In AR research, features fromacceleration signals have been extracted by considering signalsin the time-domain [9] and frequency-domain [9] as well asstudies in biomechanics [28], [29], [31]. Due to the sparsenature of our data steam, frequency domain features such asentropy and energy [9] cannot be meaningfully calculated.Therefore, we have extracted time domain features consideringbiomechanical movements analysed in previous AR studiesusing accelerometers [27]–[29] as described in Table I.

We have also considered features readily availablefrom RFID tags; RSSI and antenna identifier (aID) [31].The RFID tag specific features provide spatio-temporalinformation regarding the sensor wearer. For overlappingsegmentation (OS) method, we use the antenna ID (a I D)of the current reading and mean of the RSSI values for allthe readings from the antenna a I D within a given segmentas features. For non-overlapping segmentation method (NS),a I D is selected as the antenna with the frequent readingswithin the segment and mean RSSI is calculated accordingly.Researchers in [31] have also incorporated activity contextualinformation using Mutual Information (MI) based features.We adopted their approach (specifically MI2 approach in [31])and obtained A number of features equal to the number ofantennas in our deployment. As in [31], we have also usedtime difference between segments (�t) as a feature. In totalwe extracted n = 11 + A features from a segment. Theseextracted features are arranged to form a vector x ∈ R

n todescribe the activity represented by the segment.

2) Machine Learning Algorithms: Extracting features basedon sensor data stream segments (Section III-A) results ina sequence of feature vectors X = (ti , xi )

Ti=1, where

xi ∈ Rn , ti is the time corresponding to xi and T is the

length of the sequence. We use machine learning based activityprediction models to predict the probable activity sequenceY = (ti , yi )

Ti=1, where yi ∈ {1 · · ·C} represented by X .

The feature vectors, x , with known class label, y, are usedby machine learning algorithms to learn an activity model.This is also known as classifier training and the dataset,D = {(Xi , Yi )}mi=1, used to train is known as training dataset. Then, an activity associated with a new feature vectorcan be predicted by the learnt model by simply constructinga feature vector based on a sensor observation segment andpresenting to the learnt model.

In this study, we investigate five machine learningalgorithms that have been successfully used in ARresearch: i) Naïve Bayes (NB) [9]; ii) Conditional RandomFields (CRF) [31] iii) Random Forest (RF) [32]; iv) LinearSupport Vector Machine (LSVM); and v) Non-linear SupportVector Machine using Radial Basis Function (RBF) kernel(NSVM) [30].

Naïve Bayes (NB): NB is a generative model that assumesfeatures are conditionally independent. It fits a probabilitydistribution on input and output variables using the trainingdata set and learns the underlying process. NB assumes thetraining pairs, (xi , yi ), are i.i.d. (independent and identicallydistributed), disregarding the sequential nature of featurevectors. The decision rule of this classifier can be given as:

y = argmaxk∈{1,...,K }

p(k)

n∏

i=1

p(xi |k) (1)

Here, p(.) represents the respective probability functionsand they are estimated using a training dataset. This estima-tion depends on the selected probability distribution function.In this study, we utilized kernel density estimation to modelthe conditional probabilities and used NB implementationprovided in Matlab (R2014a) environment.


Conditional Random Fields (CRF): CRF is probabilisticgraphical model and is based on conditional probability dis-tribution from exponential family [33] (2). Being a graphicalmodel, CRF can naturally model the sequential nature inactivities. Given a sequence of feature vectors, X = (xt )

Tt=0,

CRF predicts the corresponding sequence of labels,Y = (yt )

Tt=0.

p(Y |X; λ) = 1

Z(X, λ)ex p

⎛⎝∑

j

λ j

T∑

t=1

Fk(yt−1, yt , X)

⎞⎠

Z(X, λ) =∑

Y

ex p

⎛⎝∑

j

λ j

T∑

t=1

Fk(yt−1, yt , X)

⎞⎠

Y = argmaxY∈Y

p(Y |X; λ) (2)

Here, Fks are feature functions, λ is the weight vector(model) and Z(X, λ) is a normalizing constant. Feature func-tions Fk are used to capture the sequential nature of theactivities by correlating previous, (xt−1, yt−1), and current,(xt , yt ), training samples in a sequence. As we are interestedin real-time prediction, we use the linear chain CRF proposedin [31]. In this CRF algorithm, sum-product algorithm is usedinstead of the max-product algorithm during inferencing toobtain real-time predictions.

Random Forest (RF): RF is a ensemble classificationalgorithm which is based on CART algorithm [34]. Like NB,RF also assumes that training data to be i.i.d.. In RF, a numberof decision trees (DT), B , are trained using sub sets of datasampled uniformly with replacement from the training dataset,D (i.e. bagging). The DT algorithm successively partitions adata set at each tree branch by considering a single feature untilall the data points of the resulting partitions belong to a singleclass. However, in RF, randomly selected subset of featuresare evaluated at each branch to decide the feature to be usedfor partitioning the subset of data at the corresponding node(i.e. splinting criteria), as oppose to evaluating the entire set offeatures as in traditional decision tree learning settings. Thistraining procedure results in a large collection of de-correlatedtrees.

Given a new feature vector extracted from sensor datasegment, output from RF is obtained using the majority voteof all the trees in the DT ensemble [34]; thus RF achievesa higher generalisation performance than a single DT basedon the concept of law of large numbers. Random Forestimplementation provided by randomforest-matlab library2 isused in this study. For RF we set the number of featuresconsidered for a splitting criteria at a node to be

√n, where

n is the dimensionality of a feature vector.Support Vector Machine: Support Vector Machine (SVM)

is rooted on the structured risk minimization concept [35].The standard SVM is a binary classification algorithm, i.e.yi ∈ {+1,−1}. Like both RF and NB, SVM also assumes thatthe training data is i.i.d.. During training SVM classifier learnsa hyperplane that separates the classes with the maximummargin which leads to better generalization performance by

2https://code.google.com/p/randomforest-matlab

solving the following (3) convex optimization problem.

minw,ξi

1

2‖w‖2 + C

m∑

i=1

ξi

subject to: ∀i yi 〈xi , w〉 ≥ 1− ξi

∀i ξi ≥ 0 (3)

Here, w is the learned model (weight vector), ξi s are slackvariables introduced to account for linearly inseparable datasets and C is the penalty for margin violations. Then, theSVM decision function is given by:

y = sign(〈w, x〉) (4)

where y is the prediction for a feature vector x .SVM supports non-linear decision boundaries using kernels;

when kernels are used the input features, xi , are mapped on toa higher dimensional space where a separating hyperplane inthat space is learned. Consequently, the resulting hyperplaneis a complex decision surfaces in the input space. We uselibraries LIBLINEAR [36] and LIBSVM [37] for LSVM andNSVM classifiers. In these libraries, multi-class classificationis achieved based on one-versus-one strategy.

C. Ambulatory Movement Detection Algorithm

We propose an ambulatory movement detection algorithm(see Figure 5) to detect bed-exits, chair-exits and walkingby re-evaluating activity predictions. Our approach solvesthe problem of highly sparse activity transition informationencapsulated in the sensor data stream due to the passive natureof the sensor.

Direct use of activity predictions for ambulatory movementdetection can lead to poor performance due to activity pre-diction errors leading to subsequent false alarms (incorrectlyreporting an ambulatory movement). This may cause alarmfatigue among care givers and can lead to the rejection ofthe technology [38]. However, when data stream is sparse,previous approaches to filter predictions, such as determiningprediction consistency using subsequent predictions [11], [27]or using a Kalman filter [10] cannot be applied withoutincurring delays.

Our preliminary studies revealed that most of the activitymisclassifications occur around activity transitions such assitting-on-bed to standing and the first change in an activityprediction is typically the correct prediction for the currentactivity. We incorporated our findings to moderate the conse-quences of activity misclassifications on detecting ambulatorymovements by defining the prediction filtering function givenin (5).

f (yi ; tcutof f ) ={

yk; if ti − tk ≥ tcutof f

yi ; otherwise(5)

The parameter tcutof f determines the degree of filtering(see Figure 7a), yi is the current prediction at time ti , yk

is the previous activity prediction at time tk, k < i , whereyt �= yk . As illustrated in Figure 7a the filtering functionoutputs the same activity for a predefined time interval, tcutof f ,immediately after a change in the activity prediction stream


Ambulatory Transition Corresponding Transitionmovement uninterested movement

Bed exit 1, 2 Bed entry 3, 5Chair exit 3, 4 Chair entry 2, 6Into walking 1, 4 Out of walking 5, 6

(c)

Fig. 7. (a) Illustration of the effect of the filtering parameter. (b) State model.(c) State transitions.

Fig. 8. The clinical room setting and the antenna placement used for theexperimental study.

is observed and thus prevents further changes in activitypredictions. The filter leads to less erroneous inferences ofambulatory movements. Subsequently, the filtered activityprediction stream is utilised to identify multiple ambulatorymovements. A suitable value for the filtering parameter tcutof f

can be selected based on activity durations.In an ideal setting, changes in state between

in-bed to in-chair should not be possible as a personneeds to walk between the bed and the chair. However,it is possible to misclassify the activities in waking stateas those of in-bed or in-chair and consequently miss anambulatory movement. Therefore, we have also consideredtransitions between states in-bed and in-chair as describedin Figure 7b and 7c, because detecting a bed-exit or chair-exitactivity is regarded as transitioning into any state other thanin-bed or in-chair, respectively.

IV. EXPERIMENTS AND RESULTS

A. Data Collection

Ten young healthy volunteers aged between 23 and 30 years(mean 26.4 ± 2.12) were recruited to participate in thisstudy. This study was reviewed by the Human ResearchEthics Committee TQEH/LMH/MH and no ethical matters ofconcerns were identified. Each volunteer wore a WISP over thegarment at the sternum level (Figure 1b). The data collectionprocedure was carried out in a clinical study room (Figure 8),furnished with a hospital bed and a chair, at the Basil HetzelInstitute, Woodville, South Australia.

Three activity routines were defined: i) getting into bed;lying-on-bed and getting out of the bed; ii) walking (betweenbed and chair); and iii) sitting down and/or getting up fromchair. Each participant performed three separate activity scripts

where each activity script was obtained by random ordering ofactivity routines defined previously. Thus, each trial containeddata collected from a single volunteer using a selected activityscript. Activities were recorded and annotated by an observerusing an in-house software tool. We recorded 5 groundtruth labels: i) sitting-on-bed; ii) lying-on-bed; iii) standing;iv) walking; and v) sitting-on-chair. The data set used in thisstudy is publicly available in the project web site.3

B. Statistical Analysis

In this study, we obtain precision, recall (sensitivity)and specificity based on True Positives (TP),True Negatives (TN), False Positives (FP) and FalseNegatives (FN) according to following definitions.

Precision = T P

T P + F PRecall = T P

T P + F N

Speci f ici ty = T N

T N + FT

We evaluated performance mainly using the F-scoremetric (6), which is the harmonic mean of precision and recall,since our aim is to improve recall (i.e. reduce false negatives)without deteriorating precision (i.e. minimize false positives)in the context of an imbalanced dataset. We further presentour results using G-mean metric (7).

F-score = 2.Precision.Recall

Precision + Recall(6)

G-mean = √Sensi tivi ty.Speci f ici ty (7)

Evaluation was carried out using the 10 fold cross-validationstrategy and all the parameters were selected based on thevalidation results. Results are shown as mean ± standarddeviation (SD). Statistical significance is measured using atwo-tailed two-sample t-test at 5 % significance level.

C. Activity Recognition Based on Segmentation Methods

From our preliminary experiments, we observed that aparticipant took approximately 2 s for a transition and spentat least 4 s in each activity. Considering this we selected�tmin = 1 s (i.e. transition time/2) and �tmax = 5 s(i.e. �tmin + activity duration).

Initially, we investigated a suitable range for the segmen-tation parameter λ for the NS method, because overly sizedsegments with NS leads to missing ambulatory movements(see Section III-A). We segmented the data stream withλ ∈ {i/10}, i = 1 · · · 15 and selected a range that retainsmore than 95% of each type of ambulatory movement inthe segmented data stream with respect to the ground truth.Selection of 95% is based on the fact that we will beable to capture 95% of the ambulatory movements if anactivity prediction model is able to predict activities with100% accuracy. Recognizing 95% of ambulatory movementare still a significant achievement compared to reported resultsfor detecting a single ambulatory movement, particularlybed-exits [4], [5], [29]. From this initial experiment we

3http://autoidlab.cs.adelaide.edu.au/research/ahr


Fig. 9. Activity recognition performance (mean F-score) for segmentationmethods. (a) NS. (b) OS.

TABLE II

PERFORMANCE (% MEAN ± SD) OF BEST ACTIVITY PREDICTION

MODELS BASED ON MEAN F-SCORE FOR THE TWO SEGMENTATION

METHODS WITH SEGMENTATION PARAMETERS

identified that for λ ≤ 1.0, NS method was able to retain 95%of each activity and subsequently used for further evaluation.

We extracted features based on segments from each segmen-tation method (see Section III-B). We investigated the activityprediction performance of segmentation methods discussedin Section III-A with five different classification algorithmsoutlined in Section III-B. Figure 9 shows the performance(mean F-score) of classifiers with respect to the segmentationparameter, λ.

Based on Figure 9, appropriate values for the segmenta-tion parameter, λ, can be selected for each combination ofsegmentation method and classification algorithm. Althoughthe best performance for each classifier was found at differentvalues of λ, the variation in performance observed was < 8%for each classifier because our segmentation approach suc-cessfully partitions sensor observations into segments relatedto a single activity (see Figure 6c). However, our resultsshow the importance of selecting an appropriate segment size(i.e. segmentation parameter) for each classifier.

Performance of the best activity prediction models basedon mean F-score for each segmentation method is presentedin Table II. Activity prediction models with RF have sig-nificantly ( p < 0.05) outperformed all the other classifierswith both segmentation methods. This result is consistent withfindings in [32] where the RF classifier outperformed otherclassifies for AR using acceleration sensor data. The mainreason for RF to perform better is the fact that it internallyuses a number of de-correlated DTs (Section III-B2) and henceit is more resilient to noisy features generated from WISPdata stream. It is important to note that both CRF and NSVMdepicted significantly higher (p < 0.05) performance whenusing NS.

Fig. 10. Performance of the ambulatory monitoring algorithm for differentsegmentation methods with filtering parameter, tcuto f f using activity predic-tion models given in Table II. (a) NS. (b) OS.

D. Ambulatory Movement Detection

To evaluate the ability of our framework to recogniseambulatory movements shown in Figure 7, we define a TPas follows. If there is a corresponding ground truth withinevaluation time period, δt , after an ambulatory movement isdetected (e.g bed-exit), the identified ambulatory movement isconsidered as a TP, otherwise as a FP. Similarly, TN and FNambulatory movements are identified based on detec-tion of a corresponding uninterested ambulatory movement(e.g bed-entry) defined by the transitions into a respective stateas shown in Figure 7b.

We utilized the above definitions of TP, TN, FP, FN toobtain F-score to analyse the performance. Since a partici-pant’s minimum transition time between activities was approx-imately 2 s and at least 4 s was spent in each activity, it takesat least 8 s for a participant to return to a given activity andhence we took the evaluation time, δt , to be 8 s.

Figure 10 highlights the importance of filtering activitypredictions prior to detecting ambulatory movements;performance without filtering is shown at tcutof f = 0 whereall the classifiers for both segmentation methods depict theirlowest performance. Increasing filtering (i.e. increasingtcutof f ), raises ambulatory movement detection performancebut further increments in tcutof f beyond peak performancedeteriorated the F-score since over filtering removes ambu-latory movements (i.e. activity transitions) from the activityprediction stream.

From Figure 10, we can see that RF has achieved the highestmean performance for NS. Other classifiers except NB depictsimilar performances. The results in Figure 10 show that inthe case of OS, CRF clearly outperform the other classifierson ambulatory monitoring, despite CRF being significantlyoutperformed by RF for activity recognition (see Table II).Since we identify ambulatory movements based on transitionsbetween activities, this observation indicate that although CRFmisclassify activities, CRF correctly predicts longer continu-ous sequences of activities and results in less false activitytransitions; consequently decreasing the recognition of falseambulatory movements. This is mainly because CRF considersthe sequential nature of activities during inferencing and thereal-time CRF implementation used in this study benefitsfrom the relatively longer activity sequences produced byOS compared to NS. Furthermore, LSVM has outperformedNSVM using NS in ambulatory movement detection despite


TABLE III

PERFORMANCE OF THE AMBULATORY MONITORING FRAMEWORK FOR NS WITH THE RF BASED ACTIVITY PREDICTION MODEL

AND OS WITH THE CRF BASED ACTIVITY PREDICTION MODEL (SEE TABLE II)

NSVM being the better performing classifier in terms of meanF-score as shown in Table II. In general, these results area consequence of the different locations at which predictionerrors occurred in their respective activity prediction streamsand demonstrates that the best activity prediction model doesnot always yield the best model for recognizing ambulatorymovements.

Table III shows the performance for each ambulatorymovement considered using the prediction models for RF withNS and CRF with OS. The NS (with RF) based approachclearly provides the highest mean F-score for all ambulatorymovements. For chair-exit NS depicts significantly higher(p < 0.05) F-score than OS. It is also important to notethat high precision for NS (>89%) compared OS (>75%)for each ambulatory movement is indicative of less falsepositives and consequently lower false alarms. However, bothsegmentation methods yield high mean sensitivity (or recall)values indicating that the proposed ambulatory monitoringframework was able to capture over 92% of the ambulatorymovements recorded in the data stream and thus less than 8%of ambulatory movements were missed on average.

In contrast to NS, the relatively lower performance of OSis because of the limited sensor observations related to thecurrent activity available for generating features when thecurrent sensor observation is closer in time to the previousactivity boundary. OS contain partial activity information, i.e.only samples upto the time of the current sample from thelast activity boundary or �tmin at the start of segments asopposed to more complete information related to an activityavailable through segments with NS. The limited number ofsamples in segments at the start of activities affects the qualityof information in features and subsequently the learnt activityprediction model and predictions. For example, features suchas vertical displacement obtained by integrating accelerationvectors are greatly influenced by the number of sensor obser-vations related to an activity within a segment.

V. DISCUSSION

Despite the challenging nature of the data stream fromCRFID sensors (i.e. sparsity and noise), our proposedambulatory monitoring framework was able to successfullyrecognise multiple ambulatory movements (bed-exit,chair-exit and walking) in real-time (mean F-score:NS 94% and OS 89%). In terms of segmentation approaches,

TABLE IV

PERFORMANCE OF PREVIOUS BED-EXIT MOVEMENT ALARMS

they have their strengths and weaknesses; NS performs better(overall) but is less responsive and OS is highly responsivebut depicted lower performance. Therefore, depending on theapplication context, a suitable segmentation approach can beselected. However, for bed-exit activity recognition, OS ismore preferable as it performs as well as NS while beingmore responsive than NS (see Table III).

The major advantages of our framework, specially foracceptance of the technology by caregivers and clinicians, are:i) the use of a lightweight batteryless sensor that is low costand maintenance free; ii) highly accurate recognition of mul-tiple ambulatory movements with very low misses and falsealarms; and iii) low latency to alarm (maximum delay with NSis bounded by the maximum segment size constrains �tmax

while OS will generate an alarm immediately). Recognition ofmultiple ambulatory movements will deliver a comprehensivefalls prevention mechanism and provide caregivers with theability to select appropriate ambulatory movements to monitorbased on falls risk assessments of patients carried out daily asa part of best practice guidelines for falls prevention [2].

More significantly, the batteryless ambulatory monitoringsystem has demonstrated performance comparable with pre-vious studies based on battery powered devices attachedto the waist [39] or strapped to the chest [27]. Althoughmovement sensor alarm systems that consider bed-exits andchair-exits [40] have been developed, we have only foundresearch studies that have reported results for bed-exit alarmsystems [4], [5], [29]. These approaches have also been eval-uated using similar study groups, i.e. healthy adults. Table IVpresents the performance of previous bed-exit movement alarmapproaches where we can see that the proposed frameworkperforms comparably or better.

VI. CONCLUSION AND FUTURE WORK

In this study, we have successfully demonstrated the useof body-worn passive CRFID sensors for ambulatory mon-itoring in the context of movement sensor alarm system.


Our proposed: i) natural activity boundary detection basedpartitioning methods and ii) ambulatory movement detectionalgorithm based on re-evaluating filtered activity predictionshas successfully overcome the challenges posed by the sparseand noisy nature of data streams from passive CRFID sensorsfor monitoring ambulatory movements.

However, our study is not without limitations. Results foractivity recognition demonstrate that the existing featuresfor acceleration and RFID tag data may be inadequate tosuccessfully discriminate the activities as shown by relativelylow F-score (see Table II). Therefore, future work should con-sider engineering new features to discriminate these activities.Furthermore, our evaluation is based on data collected fromyoung healthy adults (mean age 26.4±2.12), which is notrepresentative of the target group of older people. Even thoughbody motions of older frail patients in performing activitiesfollow that of young people, their activity durations may bedifferent and hence our framework needs to be evaluated withdata collected from older people. Finally, during experiments(Section IV-A), one walking activity was not captured due tothe passive nature of the sensor, mainly the RFID tag wasnot adequately powered to sample the accelerometer. Thislimitation can be addressed by: i) incorporating an appropriateantenna design that is tailored to maximise energy harvestingwhile the sensor is worn by a person as recently demonstratedby our group in [19]; and ii) using multiple emitters topower tags in the presence of a single receiver as describedin [41]; or iii) using hybrid powered CRFID sensors as recentlydemonstrated in [42].

We conclude that a single low cost, body-worn, battery-less and lightweight CRFID sensor is a feasible approachfor ambulatory monitoring, for a movement sensor alarm,despite the sparse and noisy characteristics of the sensor datastream. Moreover, we show that our real-time ambulatorymonitoring framework delivers better performance and respon-siveness compared to existing approaches for detecting onlybed-exits [4], [5], [29]. More significantly, our framework isable to accurately recognise multiple ambulatory movements.In general, our work is a significant step forward in activityrecognition research using passive body-worn sensors.

ACKNOWLEDGEMENT

The authors would like to thank Prof. R. Visvanathan atthe Queen Elizabeth Hospital, South Australia andProf. K. Hill at Curtin University for their feedbackand falls expertise. They would like to thankA/Prof. J. R Smith at the University of Washington andDr. A. P. Sample at Disney Research for supporting ourproject through the provision of WISPs.

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Asanga Wickramasinghe received the B.Sc. degreein information and communication technology fromthe School of Computing, University of Colombo,Sri Lanka, in 2010. He is currently pursuing thePh.D. degree with the School of Computer Science,University of Adelaide. His main research interestsare activity recognition from passive sensors, patternrecognition, and software engineering.

Damith C. Ranasinghe received the Ph.D. degreein electrical and electronic engineering from theUniversity of Adelaide, Australia, in 2007, under thesupervision of P. H. Cole and B. R. Davis. Duringhis time, he has held an internship position with theMassachusetts Institute of Technology (MIT), USA,in 2004, and he was a Research Engineer with theAuto-ID Center founded at MIT, from 2005 to 2006.

He has held a post-doctoral research position atthe University of Cambridge from 2007 to 2009.He joined the University of Adelaide in 2010, where

he is currently a tenured Senior Lecturer with the School of Computer Scienceand leads the Research Group with the Adelaide Auto-ID Laboratory.

His research interests include pervasive computing, wearable computing,human activity recognition, gerontechnology, and lightweight cryptographyfor resource-constrained devices.

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