Selection of Accelerometer Location on Bricklayers Using Decision Trees

Computer-Aided Civil and Infrastructure Engineering 28 (2013) 372–388

Selection of Accelerometer Location on BricklayersUsing Decision Trees

Liju Joshua & Koshy Varghese∗

Building Technology and Construction Management Division, Department of Civil Engineering, Indian Instituteof Technology Madras, Chennai 600 036, India

Abstract: Preliminary investigations on accelerometer-based activity recognition in construction have shownthat it has good potential to be utilized for recognizingcategories of work in a construction trade. Selecting theaccelerometer locations is an important consideration inactivity recognition studies, but currently it is decided pri-marily on the basis of comfort requirements. This articleproposes a methodology for selecting the location of ac-celerometers using video annotations and decision trees.A video annotation tool is used to track the movementof body segments and decision tree algorithm helps toprioritize the relevant body segments for classifying ac-tivities. A two-phase experimental study was conductedto assess the methodology. In the first phase, video an-notation studies were carried out on four bricklayers,and based on decision tree analysis three locations: rightlower arm, left lower arm, and waist were selected. Inthe second phase, an activity recognition study was con-ducted on another group of bricklayers with accelerome-ters attached at the selected locations. The results of studyshow that the location of accelerometer has a significantinfluence on accuracy and the proposed methodology iseffective in selecting accelerometer locations. In the cur-rent study only bricklaying activity was considered, how-ever, the methodology is generic and has the potential tobe applied to objectively evaluate accelerator placementlocation for a wide range of structured activities.

1 INTRODUCTION

Work sampling is the process of analyzing construc-tion activities to assess the proportion of time spentin productive and unproductive activities. Toon (2011)

∗To whom correspondence should be addressed. E-mail: [email protected].

considers work sampling as a cornerstone of productiv-ity assessment programs in construction sites becauseit gives an objective measure of site performance. Thevalidity of any work sampling study depends on howaccurately worker activities are being recognized andcategorized by the observer. The traditional method ofobserving and categorizing is manual and can be veryeffective if carried out by trained personnel. But a man-ual method of worker activity classification is laboriousand tedious.

Modern technologies are being used to automatethe data collection process and ongoing investigationsare primarily in the areas of material management(Umetani et al., 2011), progress measurement (Omariaand Moselhi, 2008), and safety control (Cho and Haas,2003; Chi et al., 2009; Carbonari et al., 2011). Us-ing emerging technologies to automate worker activ-ity classification will enhance the efficiency of worksampling studies. In recent times vision-based meth-ods have been explored to automate worker activ-ity classification (Peddi et al., 2009; Weerasinghe andRuwanpura, 2009). But accuracy of using vision-basedmethods is hampered by illumination invariance andocclusions (Chi and Caldas, 2011). Moreover, image-processing techniques are computationally intensiveand, this currently limits the wide application of videoin construction (Gong and Caldas, 2010).

Sensor-based data collection methods offer many ad-vantages and have been used in various civil engineer-ing applications such as bridge structure monitoring(Gangone et al., 2011), structural damage monitoring(Raich et al., 2012), and pavement monitoring (Lajnefet al., 2011). Studies on different types of sensors forclassifying activities show that accelerometers provedto be the most information rich and accurate sensorsfor activity recognition (Parkka et al., 2006; Westerterp,2009). An accelerometer measures and records the

C© 2013 Computer-Aided Civil and Infrastructure Engineering.DOI: 10.1111/mice.12002

Selection of accelerometer location on bricklayers using decision trees 373

value of acceleration along the x and/or y and/or z-axis.Accelerometer-based activity recognition has shownpromise in areas of application such as health care andsports (Karantonis et al., 2006; Senanayake et al., 2006).The use of accelerometer for activity recognition hasalso been investigated in construction. Preliminary stud-ies on recognizing masonry activities have shown that ithas good potential to be used for construction workeractivity recognition (Joshua and Varghese, 2011a). Inthis preliminary study an activity recognition perfor-mance of 80% was obtained for classifying bricklayingactivities with accelerometers attached to the waist ofthe bricklayer.

Experimental studies show that the classifier perfor-mance is extremely sensitive to accelerometer location(Bao and Intille, 2004; Lester et al., 2006). The ac-celerometer data pattern at different locations variesdepending on the body movements for various activ-ities. The accelerometer patterns at certain locationsplay a significant role for discriminating activities dur-ing the training stage in activity recognition. Hence de-termining the appropriate location of the accelerometeris critical to the accuracy of activity recognition studies.

Bouten et al. (1997) consider the place of attachmentof accelerometers as an important issue and rates sub-ject comfort as the first criteria in deciding the loca-tion. In studies limited to usage of a single sensor, waisthas been the preferred choice as it causes minimal dis-comfort (Mathie et al., 2004). However, for recogniz-ing complex tasks, multiple sensors may be required. Asmultiple sensors increase the complexity of the system,determining the appropriate locations on the body thatmeets both the comfort requirements and yields accu-rate results is important.

The objective of this work is to formulate and eval-uate an approach to select the location of accelerome-ters on a construction worker for effective activity clas-sification. The scope of the current study is limited tobricklaying. Bricklaying was chosen as it is a commonconstruction activity, which has a significant manualcomponent and is reasonably structured.

The approach formulated uses video-based annota-tion to track body segment movements and a deci-sion tree algorithm to rank appropriate segments foraccelerometer location. Preliminary concepts of thisapproach were documented in Joshua and Varghese(2011b). In this earlier study, video annotation was usedon a single bricklayer working in a laboratory environ-ment to identify the single most important body seg-ment for accelerometer location. In the present study,video data from four bricklayers working at a construc-tion site are gathered and the decision tree algorithmis used to orderly represent all the body segments toshow their relative importance in classifying activities.

The dominant body segments are identified from thedecision tree using information gain criteria and theseare selected for attaching accelerometers consideringcomfort requirements and integration possibilities. Toevaluate the results from the proposed methodology,accelerometers are attached at selected locations on an-other group of bricklayers, and data from accelerome-ters are used for activity recognition analysis. The ac-tivity recognition accuracies for different locations andbricklayers are then compared and conclusions on theeffectiveness of the methodology are made.

The article is organized as follows. A review on meth-ods for mapping body movements and evaluating theirrelevance in classification is presented in the next sec-tion. The methodology developed for generating move-ment patterns and evaluating sensor locations is de-scribed in Section 3. The experimental plan is discussedin Section 4, and the video annotation and activityrecognition studies are presented in Section 5. The dis-cussion of the results is given in Section 6, followed byconclusion of the work.

2 BACKGROUND

In the past, studies have been carried out to compareand select the locations of accelerometers for humanactivity recognition (Atallah et al., 2011; Olguin et al.,2006; Zappi et al., 2007; Maurer et al., 2006). But thisinvolves attaching accelerometers at every possible lo-cation on the human body and analysis of voluminousdata. It is time consuming and expensive to carry outsuch studies in the field and hence it is necessary toidentify an alternative method for determining sensorlocations.

Godfrey et al. (2008) rate the accelerometer loca-tion as an important consideration in activity recog-nition studies and recommends sensor attachment tothe body parts whose movements are of any particu-lar interest. As classification is the primary objective inactivity recognition studies, the body parts whose move-ments are relevant for discriminating the various classesof activities are the ideal locations for keeping the ac-celerometer. Hence a review of methods used for study-ing human body movements and techniques for select-ing the relevant body parts is presented in this section.

2.1 Review on human movement studies

Kinematics is defined as the study of body motion with-out references to the forces causing this motion (Anand Chao, 1984). A kinematic study of body movementsis suitable for identifying the body parts where thesensor could be possibly located. Motion capture and

374 Joshua & Varghese

movement notational systems are the two popularmethods that can be used in kinematic analysis of hu-man movement.

Motion capture is a quantitative method that usesreflective marker’s point of reference for document-ing movement. Detailed human movement can be cap-tured using this method, but it is a costly and lengthyprocess (Ebenreuter, 2008). A notational system usessymbols to express the movements of the human bodythrough observation. Labanotation is one of the mostwidely used systems of human movement analysis andhas been used as a tool by dancers, athletes, and occu-pational therapists (Rett et al., 2008). It provides a richdescription of the kinematic structure of body move-ment (Foroud and Whishaw, 2006). But the notationsare too complex (Kahol et al., 2006) and, such an in-tricate system is excessive for studying the basic move-ments of construction tradesmen. Thus it is necessary toexplore simpler methods to study the body movementsof construction workers.

The use of observational methods in constructiondates back to the beginning of the twentieth centurywith work on themes such as worker efficiency. Ob-servation has been recognized as a valuable tool forgathering information on construction sites as the ac-tions of people or the movement of materials or ma-chines can be recorded for detailed study (Oglesbyet al., 1989). Observation studies on working postureshas been a keen area of research in construction er-gonomics and methods like OWAS (Karhu et al., 1981),TRAC (Frings-Dresen and Kuijer, 1995), and PATH(Buchholz et al., 1996) have been used for recommend-ing controls and interventions.

An overview of the different manners in which obser-vational studies may be used in construction research isdiscussed by Leicht et al. (2010) and also recommendsvideo annotations as a valuable tool for observationalstudies. Video annotation is the process of adding an-notations for a particular video, referring to events ob-served in the video. The video annotation process con-sists of three steps: configuration, coding, and analysis,and have been described in detail by Rahi et al. (2009).Configuration is the first step in which specifications aremade in the video annotation software to develop an an-alytical framework, suitable for the current annotationstudy. Coding takes place during the annotation studywhere “defined” happenings are assigned to the particu-lar video stream. During the analysis step, the coded in-formation is converted to frequency tables, interactionmatrix, etc., or the coded file is exported to any otherdata analysis software.

The introduction of specialized computer softwarehas allowed for greater efficiency in coding and ana-lyzing video data. Anvil is popular video annotation

software written in Java language (Kipp, 2001). It hasbeen used in a wide range of research areas, includ-ing linguistics, human computer interactions, gesture re-search, and film studies. Anvil has been used in videoannotation study of human body movements (Rahiet al., 2009; Kahol et al., 2006). It is platform indepen-dent and the coding scheme is written in XML specifica-tion file according to a formal description of the tracks,elements, attributes, and their possible values.

2.2 Review on evaluating body segment movements

As per Calvert and Chapman (1982), a reasonable rep-resentation of the human body can be obtained withabout 23 segments if the details of the hands and feetare omitted. These segments along with the joints col-lectively constrain every movement that humans canperform. Hence the events occurring in these segmentswould provide an adequate basis for coding body move-ments (Kahol et al., 2004). The body segments are ar-ranged in a hierarchical manner consisting of parentand child segments as shown in Figure 1. The numberof child segments is 15 and they form the lowest layerin the hierarchy. The motion characteristics of a par-ent segment are based on its child segments. Hence ob-serving and coding the active/inactive status of the childsegments is adequate for mapping the various bodymovements.

The various body segments need to be evaluated tofind out their relevance in classifying activities and this issimilar to a feature selection problem. Selection of fea-tures determines the importance of any individual fea-ture, neglecting their possible interactions. The selec-tion methods that are widely used are based on eithercorrelation or information gain (Blachnik et al., 2010).

As per Battiti (1994), information gain is suitable forassessing the information content of features in com-plex classification tasks, where methods like correlationare prone to error. The main disadvantage with corre-lation metric is that it fails to capture the nonlinear de-pendencies between two variables. The article has alsodiscussed in detail the crucial advantage of informationgain over correlation, which is its capability to measurea general dependence between two variables. For ex-ample, to realize the classification given by “ExclusiveOR” function of two input variables, the correlation be-tween input variable and output variable is zero. But themutual information between input vector and the out-put is clearly defined in terms of a “log” function andthe output class can be determined without any confu-sion. As per Hall and Holmes (2003) information gainis the simplest and fastest feature ranking method. In acomparative study, feature ranking based on informa-tion gain performed better than the correlation metrics


Fig. 1. The human body hierarchy.

(Blachnik et al., 2010). In another study, different vari-ants of feature ranking algorithms based on informationgain were tested on artificial and real data sets, and allgave comparable results (Duch et al., 2004). Hence itwas decided to use information gain criteria for select-ing the relevant body segments for classification.

2.3 Review on information gain concept

The concept of information gain is based on Shannon’sinformation theory and it is a statistical property thatmeasures how well a given attribute separates the in-stances according to the target classification of workcategories (Mitchell, 1997). The term called entropy iscommonly used in information theory and it character-izes the purity of a collection of training instances, S. Ifthe attribute in training instances can take “c” differentvalues, then the entropy of S relative to this c-wise clas-sification is defined as

Entropy(S) ≡c∑

i=1

− Pi log2 Pi (1)

where Pi is the proportion of S belonging to classi. Information gain measures the expected reductionin entropy caused by classifying the training instancesaccording to an attribute A. Thus information gain,Gain(S, A) of an attribute A, relative to a collection oftraining examples S, is defined as

Gain(S, A) ≡ Entropy(S)

− ∑v∈Values(A)

|Sv||S| Entropy(Sv) (2)

where Values(A) is the set of all possible values for at-tribute A, and Sv is the subset of S for which attributeA has value v.

Literature shows that decision trees that are based oninformation gain can be used to orderly represent the

features in classifying activities. The occurrence of anattribute in a tree provides the information about theimportance of the associated attribute. The top nodeis the best node for classification. The other featuresin the nodes of the decision trees appear in descendingorder of importance. Quinlan (1986) has demonstratedin detail how an information gain criterion is used toinduce decision trees from training examples using ID3algorithm.

Martinez and Fuentes (2005) have used C4.5 algo-rithm, an extension of ID3 for selecting important fea-tures across 10 different data sets. Sugumaran et al.(2007) identified the critical features in fault diagnos-tics for roller bearing using decision trees. ID3 algo-rithm has been used to select the important attributesfor decision making in stock investments (Chen, 2011).Decision trees have also been used to select the featuresfor human activity recognition in a smart home environ-ment (Chen et al., 2010).

It can be concluded from the literature review that thebody movements can be mapped by coding the eventsin the body segments through video annotation study.The review also shows that decision trees based on in-formation gain concept can be applied to evaluate andselect the body segments on the basis of their relevancein classifying activities.

3 METHODOLOGY FOR EVALUATING BODYSEGMENT MOVEMENTS

The methodology formulated to identify sensor lo-cations is given in Figure 2. Steps 1 to 6 of themethodology are implemented in Anvil, a generic an-notation tool which was described in Section 2. InStep 7, the decision tree generation with ID3 algorithmis performed using WEKA, which is a collection of


Fig. 2. Location selection methodology.

machine learning algorithms and data-preprocessingtools (Witten and Frank, 2005). The output from thedecision tree specifies the potential locations for sensorplacement based on information gain criteria. The step-wise explanation of the framework is given below.

1. The construction worker activity is captured us-ing a video camera. Video capture can be plannedto cover a cycle of the task, so that all thesubactivities in the work can be included for theannotation study. The format in which video isrecorded also needs to be taken into considerationas Anvil supports limited range of formats.

2. The video file is imported in Anvil to carry outframe by frame observation. The work activi-ties are identified and the frames are labeledaccordingly.

3. The frames are divided into segments each with alength of 4 seconds and the segments are codedwith names of the subactivity. The coding scheme,also called “specification” is designed in Anvil totrack and code the worker activities, frame seg-ments, and the active/inactive status of the bodysegments. The 4-second duration is generally con-sidered appropriate for observing and annotatingbody movements.

4. The annotation of body segment movements isthen performed for each frame segment using

the play and replay option in Anvil. The annota-tion can be done by multiple coders or a singlecoder.

5. If the annotations of the same frames are coded bymultiple coders, the Cohen’s Kappa statistic has tobe computed to test the reliability of annotations.A Kappa value above 0.5 is considered accept-able in human movement studies (Frings-Dresenand Kuijer, 1995). If annotations are coded by thesame coder, it is unlikely that there will be a largevariation. However, it is recommended that care-ful scrutiny of the video interpretation is done tominimize any chance of coding errors.

6. The annotation output is imported to spreadsheetand a truth table is generated with true or false val-ues indicating “active or nonactive” status of eachbody segment for every frame segment, identifiedwith the name of the subactivity.

7. The truth table is imported into WEKA for car-rying out decision tree generation. Each row inthe table is a training instance with the class labelof sub-activity and the status of various body seg-ments are the features, with binary value of true orfalse.

8. The body segments that contribute to the classifi-cation appear in the decision tree in the descendingorder of importance. The dominant segments thathave high discriminating capability are selected.

9. The final assignment of accelerometer location isdone taking into consideration the subject comfortand integration possibilities.

4 EXPERIMENTAL PLAN

The experimental plan to test the methodology for se-lecting accelerometer locations is given in Figure 3.As preliminary studies in activity recognition havebeen carried out on bricklayers, it was decided touse bricklaying as the activity to evaluate the pro-posed methodology. Further, bricklaying is a reasonablystructured activity appropriate as a first step to evaluatethe methodology.

As shown in Figure 3, the experiment has two phases.During Phase-1, videos of the bricklaying activity areused to conduct video annotation studies and to gen-erate body segment patterns of the bricklayers. Next,the important body segments for classification are iden-tified using the decision tree algorithm. In Phase-2, ac-celerometers are attached to the selected body segmentsand data from each location as well as video of the ac-tivity are captured. The data and video are then usedto train the classifier algorithm as discussed in Joshua


Fig. 3. Experimental plan.

and Varghese (2011a). The trained network is used foractivity recognition and the results of various locationcombinations are compared by framing and testing rele-vant hypotheses. Based on these comparisons the valid-

ity of the decision tree approach to identifying locationsis evaluated.

5 EXPERIMENTAL STUDIES AND RESULTS

The details of experimental studies carried out in twophases and the results obtained are presented in thissection.

5.1 Phase-1: Location selection

Video annotation studies and decision tree generationare performed in this phase to select the locations ofaccelerometers.

5.1.1 Video capture and annotation studies. Video ofbricklaying activities were captured from an ongoingbuilding construction project. Four bricklayers wererandomly selected, videotaped, and the video data wereused for the video annotation study. The video capturewas planned such that all the activities of the selectedbricklayer were continuously monitored. In this way itwas possible to record several cycles of bricklaying ac-tivities that are common in construction sites. A digitalcamera operating at 10 frames per second was used forcapturing the video and the average duration of capturewas about 20 minutes per bricklayer.

The video data were imported to Anvil software forcarrying out video annotation studies. Figure 4 givesthe snapshot of Anvil interface developed for the study.The interface was designed using the Anvil specificationeditor. The tracks for annotating activity, segmenting

Fig. 4. Anvil interface.


Table 1Classes of bricklaying activities

Serial no. Bricklaying activities No. of instances

1 Setting and checking (SC) 2172 Fetching mortar (FM) 1083 Spread mortar (SM) 714 Fetching brick (FB) 855 Laying brick (LB) 1526 Cutting brick (CB) 87 Filling joints (FJ) 858 Waiting (W) 439 Relaxation (R) 15

10 Confused (C) 1311 Idle (I) 10

video frames, and annotating body segment movementsare shown at the bottom in Figure 4.

The classes of activities in bricklaying identified byOlomolaiye et al. (1995) were used as reference forlabeling the activities. After completion of activity la-beling, video frames are divided into segments each withduration of 4 seconds. Then for every video segment,the actively moving body parts are identified and an-notated using the interface. For example, in Figure 4 itcan be seen that right hand has been identified as themoving body segment for the video segment labeled,Laying brick (LB). In this way body segment patternfor all bricklayers were mapped. As this was a researchstudy, multiple rounds of annotations could be donewith the same video segment by the same annotatorto ensure that body motion was accurately annotated.Further, the annotator was also the videographer andobserved the activity while it was being done. Hencethe “Kappa statistic” was not generated for this work.

In cases where multiple annotators are involved, theKappa statistic needs to be generated for testing the re-liability of observations.

5.1.2 Body segment movement pattern. The body seg-ment pattern need not be identical for all bricklayersas variation can depend on personal styles of motion.To select locations that can be generalized across thebricklayers, the pattern generated for all the bricklay-ers were combined for further analysis. A total of 807instances were obtained from the four bricklayers andtheir breakup among different classes of bricklaying ac-tivities is given in Table 1.

Here each instance corresponds to a video frame seg-ment of 4-second duration and associated with it arebody segment annotations with binary value of true orfalse indicating active or inactive status respectively. Atruth table is thus obtained, indicating the movementstatus of body segments for all the instances. A snapshotof the truth table for 18 instances (partial) indicatingbricklayer movements is given in Figure 5. For exam-ple consider the instance 1, “T” indicates all body partsthat are active and “F” indicates all inactive body partsfor the instance. The last column shows the activity classfor the instance (in this case it is Fetching brick, “FB”).All the coding is done manually using Anvil interface.

Using the above data, “true” values of movement inpercent across the various body segments that was gen-erated for all the instances are given in Figure 6. Theordinate “True % movement” defines the ratio of timethe body segment is moving to the total time of video.It can be seen that Right hand, Right lower arm, andTorso are the prominent moving body segments. Thetruth table can be exported directly to WEKA softwarefor carrying out decision tree generation, which is thenext step in location selection.

Fig. 5. Movement truth table (partial).


Table 2Entropy parameters for all instances

No. ofClasses of instances Probability,activities (C) (Ni) Pi(Ni/� Ni) log2 Pi

Setting andchecking

217 0.268 −1.895

Fetchingmortar

108 0.134 −2.902

Spread mortar 71 0.088 −3.507Fetching brick 85 0.105 −3.247Laying brick 152 0.188 −2.409Cutting brick 8 0.010 −6.656Filling joints 85 0.105 −3.247Waiting 43 0.053 −4.230Relaxation 15 0.019 −5.750Confused 13 0.016 −5.956Idle 10 0.012 −6.334

5.1.3 Decision tree generation. A decision tree is usedto select the body segments whose annotations haveimportant role in classifying the various bricklaying ac-tivities. The algorithm used for generating a decisiontree has three parameters: the set of training examples,the list of attributes, and the attribute selection criteria,and the basic steps involved are given below.

1. Select the best attribute at the root node of thetree. In ID3 algorithm, information gain is used asa criterion for selecting the best attribute.

2. Create descendants of root for all the possible val-ues of the root.

3. Consider subsets of training examples in each de-scendant, and again select the best attribute.

The tree generation will terminate when a set of train-ing examples are obtained for a leaf or when the at-tributes are exhausted.

The evaluation of information gain is the core con-cept in the ID3 algorithm and information gain is cal-culated on the basis of entropy reduction (Section 2.3).This approach is explained using an example.

Table 2 shows the list of instances under differentclasses of activities in the present case along with theirprobability distributions.

The calculation of the entropy for all the available in-stances is given below.

Entropy(S) ≡c∑

i=1− Pi log2 Pi

= 2.917

(3)

In building a decision tree, the next step is to selectthe attribute (A) whose values give the best split or dis-

Table 3Entropy parameters for “Right Lower Arm Status (A) =

True”


Setting andchecking

50 0.140 −2.840

Fetchingmortar

75 0.209 −2.255

Spread mortar 69 0.193 −2.375Fetching brick 23 0.064 −3.960Laying brick 38 0.106 −3.236Cutting brick 8 0.022 −5.484Filling joints 83 0.232 −2.109Waiting 9 0.025 −5.314Relaxation − − −Confused 3 0.008 −6.899Idle − − −

crimination among the classes of activities. The selec-tion is done based on the concept of the informationgain and the steps involved in calculating informationgain are demonstrated below.

All the attributes can take two attribute values,“True” or “False” depending on its movement status.For instance, all the classes of activities and the num-ber of instances when right lower arm is “True” areidentified and listed as shown in Table 3. Then the en-tropy for these instances is calculated in a similar man-ner explained above. The subset of classes belonging tothe case when attribute is true is indicated as St and here“St” is 358.

Entropy(St) =c∑

i=1−Pi log2 Pi

= 2.723

(4)

Fig. 6. Movement values of body segments.


Table 4Entropy parameters for “Right Lower Arm Status = False”


Setting andchecking

167 0.371 −1.423

Fetchingmortar

33 0.073 −3.766

Spread mortar 2 0.004 −5.489Fetching brick 62 0.138 −2.856Laying brick 114 0.254 −1.978Cutting brick − − −Filling joints 2 0.004 −7.811Waiting 34 0.076 −3.723Relaxation 15 0.033 −4.904Confused 10 0.022 −5.489Idle 10 0.022 −5.489

Similarly the entropy for the case when right lowerarm status is false is calculated as shown in Table 4. Inthis example the total number of instances in the subset,when right lower arm status is false is 449 and hence“Sf = 449.”

Entropy(Sf) =c∑

i=1− Pi log2 Pi

= 2.463

(5)

Next the expected entropy is calculated as the sum ofthe entropies of the subset of classes (St, Sf), weightedby the fraction of examples.

Hence the expected entropy with right lower arm (A)is calculated as

Entropy(S, A(true/false)) = (St/S)∗Entropy(St)

+ (Sf/S)∗Entropy(Sf)= (358/807)∗2.723

+ (449/807)∗2.463 = 2.581

(6)

Now information gain is calculated as

Gain(S, A) = Entropy(S) − Entropy(S, A(true/false))

= 2.917 − 2.581

= 0.336(7)

In a similar manner, the information gain for the var-ious attributes is calculated and the attribute with themaximum information gain can be selected to split thenodes of the decision tree. In this way, the set of brick-layers’ instances with class labels is recursively split intosmaller subsets as the tree is being built. The ID3 de-cision tree algorithm interface in WEKA was used toconstruct decision trees. As visualization of this decision

tree is not possible in WEKA, a decision tree applet(Guerts, 2011) was utilized for tree generation and vi-sualization. The “cross validation” accuracy of the treein classifying activities is 89.46%. But here the decisiontree has been proposed to serve as a “feature selection”tool rather than a “classification” tool. That means thetree was used to quantify and select the distinguishingfeatures (body segments) for classification rather thanclassifying a particular data set.

The first four levels of the decision tree are shown inFigure 7.

It can be seen that the body segments are arrangedhierarchically as the nodes of the tree. The decision toselect a body segment to split a node is based on thecriteria of information gain value. For example, rightlower arm has the maximum information gain valueof “0.336” when all the bricklayers’ instances are con-sidered and hence it is used to split all the instancesat the root node of the tree. After the split, informa-tion gain calculation is carried out for two of the sepa-rate sets of instances and the body segments with max-imum information gain values are identified in each setto carry out the next split. In this case, right hand andright upper arm were selected and the nodes are la-beled accordingly. This process continues recursively tillthe leaf nodes are reached, which represent the class la-bels of bricklaying activities. The important segmentsthat have significant contribution in classifying brick-laying activities appear at the top levels in the decisiontree.

Comparing results of the movement chart (Figure 6)and decision tree (Figure 7) it can be seen that the abil-ity of a body segment to discriminate between activi-ties is not directly based on movement level of the seg-ment. The segment with highest movement, the righthand (63.0%), is at the second level of the tree whereasthe right lower arm (44.4%), the segment with secondhighest movement is at the root node of the tree. Fur-ther, the right upper arm which has a movement levelof 12.5% (Ranked 7th) was placed in the second levelof the tree with information gain value of 0.368 whichis higher than that of the right hand segment which hasan information gain value of 0.297. In summary, activityidentification capability of a body segment is based onits motion pattern and not level of movement. Informa-tion gain considers both active and inactive phase of thebody segment movement and hence it is an effective ap-proach to identify the body segments that have the ca-pability to discriminate among the different categoriesof the construction activity.

5.1.4 Location selection process. In this study it was de-cided to select three locations on the human body tak-ing into consideration, information gain levels, comfort,


Fig. 7. Decision tree representation (partial).

Table 5Location selection process

ComfortInformation and

Level Body gain integrationno. segment values possibilities Decision

1 Right lowerarm

0.336√

Selected

2 Right upperarm

0.368 × −

Right hand 0.266 × −3 Torso 0.299 × −

Waist 0.297√

SelectedLeft hand 0.192, 0.091 × −

4 Left lowerarm

0.430, 0.400√

Selected

Left upperarm

0.228, 0.116 × −

Left hand 0.156 × −Head 0.138, 0.059

√ −Torso 0.083 × −

and integration requirements. The process of selectinglocations is presented in Table 5. The body segmentsappearing in the first four levels are listed in this table.In each level, the body segments are arranged in theorder of ranking based on the information gain values.It can be seen that the body segments can appear morethan once even in the same level which depends uponthe training instances at hand.

The locations of hands and upper arm were not se-lected due to comfort and integration issues. Bricklay-ers use their hands very often for handling material andperforming work. Attaching accelerometers to handswill limit the free movement and thus interfere with theactivities of the worker. The muscles at the upper armcontract and expand forcefully during body movementsresulting in loose attachment of the accelerometer, lead-ing to spurious signals called artifacts. These considera-

tions reduce the potential locations to five—right lowerarm, left lower arm, waist, torso, and head.

Although the head is a good location for integratingthe accelerometer (with the worker helmet), its infor-mation gain value is low compared to other body seg-ments. The torso was also not selected as there is limitedscope for securely attaching the accelerometer. Hencethe three locations of right lower arm, left lower arm,and waist were selected. Waist is a location that causesminimum discomfort to the worker and it is possibleto attach the accelerometer easily with a belt or pouch.The lower arms of the body support most of the brick-laying activities and as these parts do not handle anytool or material, there is little chance for interference.Further, the accelerometers can be attached securelywith arm bands to ensure that workers can wear it withno discomfort.

5.2 Phase-2: Location evaluation study

In this phase experimental studies were carried out todetermine the activity recognition accuracies obtainedby attaching accelerometers at the selected locations onthe bricklayers. The recognition accuracy obtained ateach location was compared with that indicated by thedecision tree. Further an evaluation of the best singlebody location to fix the accelerometer is also carriedout.

5.2.1 Accelerometer data collection. Triaxial ac-celerometer data loggers with sample rate of 40 Hz andrange of ±6 g were used for the study. Accelerometerswere firmly attached to two arm bands and to abdomi-nal belt so that bricklayers can comfortably wear themduring their work.

The construction site selected for this study was dif-ferent from that utilized for carrying out the video anno-tation studies (Phase-1). Accelerometer data were col-lected from four bricklayers and a photo of bricklayer


Fig. 8. A photo of data collection at site.

wearing arm bands and abdominal belt during the datacollection stage is given in Figure 8. Video capture ofbricklaying activities was simultaneously carried out toserve as the ground truth. The video data are used foridentifying the worker activity which is used for label-ing the accelerometer data. The duration of each datasample ranged from 30 to 40 minutes of work activity.The selection of the worker and the time of data collec-tion were solely based on convenience. The objectivesof the study were explained to the workers before thecommencement of the data collection. The accelerome-ter data were stored in the data logger and later trans-ferred to computer for carrying out activity recognitionanalysis.

5.2.2 Activity recognition analysis. Activity recognitionanalysis for the different locations was carried outthrough a five-step process shown in Figure 9 and it isbriefly described below. A detailed explanation of theprocess is given in the work by Joshua and Varghese(2011a).

1. The accelerometer data along x, y, and z axesare imported to spreadsheet and labeled with theclasses of bricklaying activities using the videodata. Both the accelerometer data and videodata were synchronized before the start of thedata labeling. The accelerometer data from thedata logger are already time stamped and thishelps in proper synchronization with the videostream.

2. Data stream is broken into segments each withlength of “256 data samples,” which has beenwidely used as standard length in activity recog-nition studies. As the sampling frequency selectedfor the accelerometer was 40 HZ, the “256 data

Fig. 9. Activity recognition process.

samples” were equivalent to a length of “6.4seconds.”

3. Time domain and frequency domain features wereobtained for every data segment. Figo et al. (2010)have presented a survey of various features thatplay an important role in human activity classi-fication. Based on the recommendations of thisstudy feature selection for the current work wasdone. The time domain features used in the studyare mean, median, peak, mean absolute devia-tion, variance, first-quartile and third-quartile tomeasure central tendency and variability, and cor-relation to understand the association relation-ship. Correlation is especially useful in differen-tiating between activities that involve translationin a single dimension. Pearson’s product-momentcoefficient was used to calculate the correlationcoefficients between the signals for the variousaxes. To obtain frequency domain features the ac-celerometer data in time domain was convertedinto frequency domain using Fast Fourier Trans-form (FFT) analysis. Frequency domain featuresextracted for this study are dc component, en-tropy and energy. The dc component is the firstcomponent in the frequency spectrum which is re-lated to the influence of gravity and hence it givesindication regarding the static postures of theworker. Energy features can be used to assess the


strength of the motor act. It is calculated as thesquared sum of its spectral coefficients normalizedby the length of the sample length. Entropy canbe computed using the normalized information en-tropy of the discrete FFT coefficient magnitudesexcluding the dc component. It helps to differenti-ate between signals that have similar energy valuesbut correspond to different activity patterns. Thusa set of 33 (11 × 3) features was extracted fromevery data segment that are used in the next stageof the recognition process. The feature generationwas performed in MS Excel.

4. The features were imported to WEKA for train-ing with J48 algorithm which is the Java implemen-tation for C4.5 decision tree classifier. It is a fastand popular classifier used for training in humanactivity recognition studies. Bao and Intille (2003)found that when compared with other classifiersC4.5 gave the highest accuracy in recognizing hu-man activities.

5. In the final step, 10 runs of stratified 10-fold cross-validation were carried out to evaluate the accu-racy of activity recognition at different locations.In 10-fold cross-validation the data set is randomlysplit into 10 mutually exclusive folds of approx-imately equal size. The classifier is trained andtested for 10 runs. For each run onefold is re-served as the test set and the remaining folds arecollectively used to train the model and this is re-peated for each fold. The cross-validation estimateof accuracy is the overall number of correct clas-sification divided by the number of instances inthe data set. In stratified cross-validation, the foldsare stratified so that they contain approximatelythe same proportion of labels as the originaldata set.

5.2.3 Activity recognition results. The results of activ-ity recognition for the bricklayers for different sen-sor location combinations are given in Table 6. Thevalues represent the cross-validation estimate of accu-racy, which is the overall number of correct classifica-tion divided by the number of segments in the data setfor each bricklayer. The global average accuracies foreach sensor combination are also shown in the last col-umn and this is the average accuracy results for all thebricklayers.

It can be observed that in certain sensor combina-tions, additional sensors did not result in improvementof recognition accuracy. This is due to the complex in-teraction of data features from the multiple sensors dur-ing the training stage, thereby lowering the performanceof classifier algorithms. Certain data features are irrele-vant and can be detrimental, causing confusion for the

classifier algorithm employed (Han and Kamber, 2006).Many feature filtering techniques are available to re-duce this interaction and this will be considered as fu-ture scope for investigation.

As part of interpreting the results, two hypotheseswere formulated and tested to understand the effect ofaccelerometer location on recognition accuracy and thevariation of recognition accuracy across bricklayers.

Hypothesis-1: There is no significant differencein the global average recognition accuracies ofthe bricklayers across different sensor locationcombinations.

Analysis of variance (ANOVA) tests at 0.01 signifi-cance level was carried out to compare the global aver-age accuracy of recognition for different locations andthe results are given in Table 7. The data fulfilled the re-quirements of the ANOVA test done. The Skewness &Kurtotis of the data sets were within acceptable limits.The data also passed the Levene test statistic for vari-ance. Further, the data in each group are independentas the data are assigned randomly to each fold in the10-fold cross validation analysis.

It can be seen from Table 7 that the null hypothe-sis got rejected showing that accuracies are significantlyvarying indicating that location must be given due con-sideration in activity recognition studies.

Hypothesis-2: There is no significant difference in theaccuracy of recognition for each bricklayer whencompared with the global average accuracy.

Independent sample “t” test at 0.01 significance levelwas carried out to compare the recognition accuracy ofaccelerometer location/location combinations on eachbricklayer with the global average and the p-values ob-tained from the results are given in Table 8. The caseswhere the null hypothesis can be rejected are indicatedwith asterisk (∗) marks.

It can be seen that for the right lower arm location,the null hypothesis did not get rejected for any of thebricklayers. The interpretation is that when data fea-tures at right lower arm are used for training, the recog-nition accuracies do not vary significantly among thebricklayers. This is an indication that there are sim-ilarities in movement of right lower arm for all thebricklayers. It can be seen from Table 6 that the globalaverage accuracy for the left lower arm is high (77.55)when compared with the right lower arm (74.17). How-ever, the results of the hypothesis test results showthat there is significant variation of recognition accu-racy among the bricklayers when only data from theleft lower arm location are used. Hence right lower armlocation is recommended as an appropriate common


Table 6Results of activity recognition

Sensor combinations Accuracy recognition result (%)

Bricklayer

Right lower arm Left lower arm Waist 1 2 3 4 Global average√

72.66 75.82 75.96 72.26 74.17√81.5 75.32 74.47 78.92 77.55√71.06 72.09 75.06 75.23 73.36√ √79.91 76.91 75.4 78.04 77.56√ √78.41 73.55 74.16 79.74 76.46√ √71.74 77.14 77.06 78.62 76.14√ √ √78.66 76.73 76.07 80.48 77.99

Table 7Test results for Hypothesis-1

ANOVA test results

Levene statistic Sig. F Calculated F Critical P-value Null hypothesis

0.289 0.943 18.8091 2.8084 0.000 Rejected

Table 8Test results for Hypothesis-2

P-values fromSensor independent samples

combinations test results

Right Left Bricklayerlower lowerarm arm Waist 1 2 3 4

√0.080 0.066 0.042 0.031√0.000∗ 0.017 0.001∗ 0.144√0.009∗ 0.165 0.062 0.036√ √0.010 0.482 0.027 0.612√ √0.033 0.002∗ 0.016 0.000∗

√ √0.000∗ 0.274 0.308 0.006∗

√ √ √0.451 0.173 0.044 0.007∗

∗Statistically significant.

location for all bricklayers as the variability of resultsat this location is minimal.

It can be seen in Figure 7 and Table 5 that thelocations evaluated, arranged in order of decreas-ing discriminating ability (based on information gain)were (i) Right Lower Arm (Root Node), (ii) Waist(Level 3), (iii) Left Lower Arm (Level 4). A similar pat-tern can be seen from the experimental results shown inTable 8: (i) Right Lower Arm (No rejects), (ii) Waist(One reject), (iii) Left Lower Arm (Two rejects). Thesepreliminary results strongly support the validity of iden-

tifying accelerometer location based on video annota-tion and information gain. However, further experi-mental testing is required to rigorously validate thesefindings.

6 DISCUSSION

The primary objective of this work was to formu-late and evaluate a methodology to select the locationof accelerometers on a construction worker for effectiveactivity classification. Based on a review of literatureand preliminary studies a nine-step methodology uti-lizing video annotations and decision trees was formu-lated. This approach was evaluated through field exper-iments. Although the results of the experiment showedthat the approach is valid, there are a number of factorswhich should be considered for the broader interpreta-tion and wider applicability of these findings. This sec-tion discusses these factors in detail and the approachesto overcome possible limitations.

Video annotation is simple and relatively inexpensivewhen compared with motion capture and other tech-niques requiring sophisticated instrumentation and set-ups. A key factor influencing the success of this ap-proach is ensuring accurate interpretation of the videofor annotation. As this was a research study, accuracywas ensured through detailed scrutiny and multiple re-plays of the video frames by the same observer (whocaptured the video). Such an approach may not beapplicable in all situations. If multiple observers are


required to expedite the annotation process, the relia-bility of the observations should be assessed using theKappa statistic. Further, the use of automated methodsusing image processing or devices such as Kinect fortracking body segment movements can be explored toautomate the annotation process.

The proposed methodology can be applied to rec-ommend accelerometer locations for activities whichare cyclic in nature. Many construction activities suchas masonry, reinforcement tying, formwork carpentry,concrete pouring, etc. satisfy this requirement. How-ever, this approach is not suitable for activities whichare noncyclic and unstructured such as equipment erec-tion. Further, if a cyclic activity is carried out in a non-cyclic manner (frequent breaks, changes in workface,numerous trips for materials tools, etc.) the accelerom-eter analysis might not identify all the specific activitiesaccurately, but it is likely that the low number of pro-ductive work categories will be recorded thus the activ-ity can be flagged for improvement studies.

An important choice regarding accelerometer loca-tion is if it should be standardized for all workersof a particular trade or individualized based on eachworker’s movement patterns. Individualized locationswould increase activity recognition accuracy but consid-erable effort (repeating the nine-step methodology foreach worker) would be required to determine the bestlocation for each worker. As the ultimate goal of con-struction activity classification is to detect unusual levelsof unproductive activities, a very high level of accuracyis not required to make this assessment. Thus from animplementation perspective a standardized location foreach trade/activity-type is expected to be adequate. Fur-ther field investigations need to be conducted to verifythis view.

It was expected that data from multiple sensor lo-cations would give better accuracy and consistencythan single locations. However, the results shown inTables 6 and 8 indicate this was not the case. Asdiscussed the reduced accuracy and consistency is at-tributed to the interaction of data features from multi-ple sensors during the training stage. To improve accu-racy and consistency levels it is suggested that alternatefiltering techniques which reduce interaction need to beinvestigated.

The sample size considered for each phase of the ex-perimental study was four bricklayers. This was a con-venience sample and as the data obtained confirmed tothe variability norms, it is expected that this is represen-tative of typical (right-handed) bricklaying practice inthe region. The experimental study established the stepsand analysis which can be used to identify and evalu-ate accelerometer locations. Studies with larger samples

covering multiple projects would increase the level ofconfidence for wider applicability. The same method-ology can be adopted for the larger scale studies. Thisshould be conducted when a broader level of standard-ization is required.

Advanced decision tree algorithms like C4.5 whichis an extension to the ID3 algorithm were not consid-ered for this preliminary investigation. In future it isplanned to investigate and compare the performanceof various decision tree algorithms against the basicID3 algorithm. The J48 algorithm was used in theactivity recognition study, as it is simple and fast forcomparing recognition accuracies for various sensorcombinations. Improving classification accuracy usingneural network classifiers or approaches like baggingand boosting should also be explored.

The results from each phase of the experimentalstudy were systematically analyzed. For the first phasethe decision tree-based prioritization of body segmentsbased on video annotation and entropy criteria wascarried out accurately. Similarly the data from the ac-celerometer were tested for validity and statistical re-quirements before the statistical analyses were done. Tocompare the results of the two phases, the informationgain-based ranking was compared with the number ofacceptance in the hypotheses tests. Although this com-parison can be considered to be indicative and accept-able for this preliminary study, a more specific com-parison metric has to be formulated and applied to abroader study.

From a research methodology perspective, this studyhas the following limitations:

1. The evaluation study compares only the three lo-cations identified at top levels of the decision tree.A comprehensive comparison of all the 15 poten-tial locations could not be carried out. However,as the same order of location ranking was ob-tained in both studies, it is unlikely that a lowerranked location identified by the decision treewould have resulted in more accurate and con-sistent classification.

2. The integration of the available accelerometertechnology to ensure comfort of the workerhas been given priority in the location selec-tion. In the future, when alternate technologies(such as ring-based or glove-based accelerome-ters) become available other locations might be-come more appropriate for instrumentation. Toevaluate these locations the same methodologycan be used.

3. In the present study all the bricklayers whoparticipated where right-handed. Although it is


expected that the methodology will be equallyapplicable to left-handed workers, field studiesare required to assess if there are any unexpectedoutcomes when workers are left-handed.

This section has discussed the factors which shouldbe considered for wider applicability of the proposedmethod and findings from this study. It can be seen fromthe discussion that there are limitations to apply themethod to situations such as unstructured work and fur-ther investigations are required before the results canbe used in construction practice. However, this work es-tablishes the proof of concept that the proposed methodis appropriate for objectively selecting location of ac-celerometers for structured activity recognition.

7 CONCLUSIONS

This article presented the work done toward formulat-ing a method to select accelerometer locations on a con-struction worker for activity classification. The methodproposed was then evaluated through field experiments.Based on the results of the evaluation the following keyconclusions are made:

1. It was found that the location of accelerometerson the worker influence the activity recognitionaccuracy.

2. The nine-step methodology proposed was found tobe effective in objectively rating the potential ac-celerometer locations.

3. The information gain criteria are more effectiveat prioritizing activity discrimination capability ofbody segments than level of movement.

4. The bricklaying activity evaluated within the scopeof this study identified the right lower arm as an ap-propriate common accelerometer location for ac-curate classification for all the bricklayers.

This study has established an initial proof of conceptof the proposed methodology. As elaborated in the dis-cussion, there are a number of factors which should befurther explored before these concepts can be consid-ered for practice. As the concepts and methods pro-posed show potential to be applied to a wide range ofstructured activities, further studies to evaluate its ap-plicability to other activities are currently underway. Inaddition, studies on exploring the use of alternate algo-rithms for decision tree ranking and developing a morespecific comparison metric for activity recognition anal-yses are also proposed.

REFERENCES

An, K. N. & Chao, E. Y. (1984), Kinematic analysis of humanmovement, Annals of Biomedical Engineering, 12, 585–97.

Atallah, L., Lo, B., King, R. & Yang, G. (2011), Sensor posi-tioning for activity recognition using wearable accelerom-eters, IEEE Transactions on Biomedical Circuits andSystems, 5(4), 320–9.

Bao, L. & Intille, S. S. (2004), Activity recognition from user-annotated acceleration data, in Proceedings of Pervasive2004 (LNCS 3001), Springer-Verlag, Berlin, 1–17.

Battiti, R. (1994), Using mutual information for selecting fea-tures in supervised neural net learning, IEEE Transactionson Neural Networks, 5(4), 537–50.

Blachnik, M., Bukowiec, A., Kordos, M. & Biesiada, J. (2010),Information theory vs. correlation based feature rank-ing methods in application to metallurgical problem solv-ing, in Proceedings of the 10th International Conferenceon Artificial Intelligence and Soft Computing (ICAISC’10),Springer-Verlag, 289–98.

Bouten, C. V. C., Koekkoek, K. T. M., Verduin, M., Kodde,R. & Janssen, J. D. (1997), A triaxial accelerometerand portable data processing unit for the assessment ofdaily physical activity, IEEE Transactions in BiomedicalEngineering, 44(3), 136–47.

Buchholz, B., Pacquet, V., Punnet, L., Lee, D. & Moir, S.(1996), PATH-A work sampling-based approach to er-gonomic job analysis for construction and other non-repetitive work, Applied Ergonomics, 27(3), 177–87.

Calvert, T. W. & Chapman, J. (1982), Aspects of the kinematicsimulation of human movement, IEEE Computer Graphicsand Applications, 2(9), 41–50.

Carbonari, A., Giretti, A. & Berardo, N. (2011), A proac-tive system for real-time safety management in constructionsites, Automation in Construction, 20, 686–98.

Chen, C. (2011), The apply of ID3 in stock analysis, inProceedings of the 6th International Conference on Com-puter Science and Education (ICCSE), IEEE, Singapore,24–7.

Chen, C., Das, B. & Cook, D. J. (2010), A data mining frame-work for activity recognition in smart environments, in Pro-ceedings of the 6th International Conference on IntelligentEnvironments, IEEE Computer Society, 80–3.

Chi, S. & Caldas, C. H. (2011), Automated object identifi-cation using optical video cameras on construction sites,Computer-Aided Civil and Infrastructure Engineering, 26,368–80.

Chi, S., Caldas, C. H. & Kim, D. Y. (2009), A methodol-ogy for object identification and tracking in constructionbased on spatial modeling and image matching techniques,Computer-Aided Civil and Infrastructure Engineering, 24,199–211.

Cho, Y. K. & Haas, C. T. (2003), Rapid geometric modelingfor unstructured construction workspaces, Computer-AidedCivil and Infrastructure Engineering, 18, 242–53.

Duch, W., Wieczorek, T., Biesiada, J. & Blachnik, M. (2004),Comparison of feature ranking methods on information en-tropy, in Proceedings of the International Joint Conferenceon Neural Networks, IEEE, 1415–9.

Ebenreuter, N. (2008), Dance movement: a focus on the tech-nology, IEEE Computer Graphics and Applications, 25(6),80–83.

Figo, D., Diniz, C. P., Ferreira, D. G. & Cardoso,J. M. P. (2010), Preprocessing techniques for context


recognition from accelerometer data, Personal and Ubiq-uitous Computing, 14(7), 645–62.

Foroud, A. & Whishaw, I. Q. (2006), Changes in thekinematic structure and non-kinematic features ofmovements during skilled reaching after stroke: a La-ban movement analysis in two case studies, Journal ofNeuroscience Methods, 158, 137–49.

Frings-Dresen, M. H. W. & Kuijer, P. P. F. M. (1995), TheTRAC-system: an observation method for analyzing workdemands at the workplace, Safety Science, 21, 163–5.

Gangone, M. V., Whelan, M. J. & Janoyan, K. D. (2011),Wireless monitoring of a multispan bridge superstruc-ture for diagnostic load testing and system identification,Computer-Aided Civil and Infrastructure Engineering, 26,560–79.

Geurts, P. (2011), Decision tree applet. Available at:www.montefiore.ulg.ac.be/∼geurts/software.html, access-ed November 2, 2011.

Godfrey, A., Conway, R., Meagher, D. & Olaighin, G. (2008),Direct measurement of human movement by accelerome-try, Medical Engineering and Physics, 30, 1364–86.

Gong, J. & Caldas, C. H. (2010), Computer vision-basedvideo interpretation model for automated productivityanalysis, Journal of Computing in Civil Engineering, 24(3),252–63.

Hall, M. A. & Holmes, G. (2003), Benchmarking attributeselection techniques for discrete class data mining, IEEETransactions on Knowledge and Data Engineering, 15(6),1437–47.

Han, J. & Kamber, M. (2006), Data Mining: Conceptsand Techniques, Morgan Kaufmann Publishers, SanFrancisco, CA.

Joshua, L. & Varghese, K. (2011a), Accelerometer-based ac-tivity recognition in construction, Journal of Computing inCivil Engineering, 25(5), 370–9.

Joshua, L. & Varghese, K. (2011b), Video annotation frame-work for accelerometer placement in worker activity recog-nition studies, in Proceedings of the 28th InternationalSymposium on Automation and Robotics in Construction(ISARC 2011), Seoul, Korea, 317–22.

Kahol, K., Tripathi, P. & Panchanathan, S. (2004), Compu-tational analysis of mannerism gestures, in Proceedings ofthe 17th International Conference on Pattern Recognition(ICPR’04), IEEE, 3, 946–9.

Kahol, K., Tripathi, P. & Panchanathan, S. (2006), Document-ing motion sequences with a personalized annotation sys-tem, IEEE Multimedia, 13(1), 37–45.

Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N.H. & Celler, B. G. (2006), Implementation of a real-timehuman movement classifier using a triaxial accelerometerfor ambulatory monitoring, IEEE Transactions on Infor-mation Technology in Biomedicine, 10(1), 158–67.

Karhu, O., Harkonen, R., Sorvali, P. & Vepsalainen, P.(1981), Observing working postures in industry: examplesof OWAS application, Applied Ergonomics, 12(1), 13–7.

Kipp, M. (2001), Anvil-A generic annotation tool for mul-timodal dialogue, in Proceedings of the 7th EuropeanConference on Speech Communication and Technology(Eurospeech 2001), Aalborg, Denmark, 1367–70.

Lajnef, N., Rhimi, M., Chatti, K., Mhamdi, L. & Faridazar, F.(2011), Toward an integrated smart sensing system and datainterpretation techniques for pavement fatigue monitoring,Computer-Aided Civil and Infrastructure Engineering, 26,513–23.

Leicht, R. M., Hunter, S. T., Saluja, C. & Messner, J. I. (2010),Implementing observational research methods to studyteam performance in construction management, Journal ofConstruction Engineering and Management, 136(1), 76–86.

Lester, J., Choudhury, T. & Borriello, G. (2006), A practicalapproach to recognizing physical activities, in Proceedingsof Pervasive 2006 (LNCS 3968), Springer-Verlag, Berlin, 1–17.

Martinez, J. & Fuentes, O. (2005), Using C4.5 as variable se-lection criterion in classification tasks, in Proceedings of the9th International Conference on Artificial Intelligence andSoft Computing (IASTED), Benidorm, Spain, 171–6.

Mathie, M. J., Celler, B. G., Lovell, N. H. & Coster, A. C.F. (2004), Classification of basic daily movements using atriaxial accelerometer, Medical and Biological Engineeringand Computing, 42, 679–87.

Maurer, U., Smailagic, A., Siewiorek, D. P. & Deisher, M.(2006), Activity recognition and monitoring using multiplesensors on different body positions, in Proceedings of theInternational Workshop on Wearable and Implantable BodySensor Networks (BSN’06), IEEE Computer Society, 113–6.

Mitchell, T. M. (1997), Machine Learning, McGraw-Hill,Singapore.

Oglesby, C. H., Parker, H. W. & Howell, G. A. (1989),Productivity Improvement in Construction, McGraw-Hill,USA.

Olguin, D. O. & Pentland, A. (2006), Human activity recog-nition: accuracy across common locations for wearable sen-sors, in Proceedings of the 10th International Symposium onWearable Computers, IEEE, Montreaux, Switzerland, 11–3.

Olomolaiye, P. O., Harris, F. C. & Price, A. F. (1996), The sen-sitivity of bricklayers’ output to changes in skill, Computersand Structures, 58(2), 419–28.

Omaria, S. & Moselhi, O. (2008), Integrating 3D laser scan-ning and photogrammetry for progress measurement ofconstruction work, Automation in Construction, 18, 1–9.

Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J.& Korhonen, I. (2006), Activity classification using realisticdata from wearable sensors, IEEE Transactions on Infor-mation Technology in Biomedicine, 10(1), 119–28.

Peddi, A., Huan, L., Bai, Y. & Kim, S. (2009), Developmentof human pose analyzing algorithms for the determinationof construction productivity, in Proceedings of ConstructionResearch Congress 2009, ASCE, Reston, VA, 11–20.

Quinlan, J. R. (1986), Induction of decision trees, MachineLearning, 1, 81–106.

Rahi, R., Brigitte, M., Guillame, T. & Francois, V. (2009),Observation, annotation and analysis of design activities:how to find an appropriate tool, in Proceedings of 17th In-ternational Conference on Engineering Design (ICED’09),Stanford University, 5, 193–204.

Raich, A. M. and Liszkai, T. R. (2012), Multi-objectiveoptimization of sensor and excitation layouts for fre-quency response function-based structural damageidentification, Computer-Aided Civil and InfrastructureEngineering, 27(2), 95–117.

Rett, J., Dias, J. & Ahuactzin, J. M. (2008), Bayesian reason-ing for Laban movement analysis used in human machineinteraction, International Journal of Reasoning-based Intel-ligent Systems, 1(4), 64–74.

Senanayake, S. M. N. A., Chong, V., Chong, J. & Sirisinghe,G. R. (2006), Analysis of soccer actions using wireless ac-celerometers, in Proceedings of the International Confer-ence on Industrial Informatics, IEEE, Singapore, 664–9.


Sugumaran, V., Murlaidharan, V. & Ramachandran, K. I.(2007), Feature selection using decision tree and classifi-cation through proximal support vector machine for faultdiagnostics of roller bearing, Mechanical Systems and Sig-nal Processing, 21, 930–42.

Toon, S. (2011), Construction productivity and perfor-mance measurement, Bechtel Technology Journal, 3(1),1–10.

Umetani, T., Inoue, K. & Arai, T. (2011), Pose estimation ofconstruction materials using multiple ID devices for partsand packets unification, Computer-Aided Civil and Infras-tructure Engineering, 26, 356–67.

Weerasinghe, I. P. T. & Ruwanpura, J. Y. (2009), Au-tomated data acquisition system to assess construction

worker performance, in Proceedings of Construction Re-search Congress 2009, ASCE, Reston, VA, 11–20.

Westerterp, K. R. (2009), Assessment of physical activity: acritical appraisal, European Journal of Applied Physiology,105, 823–8.

Witten, I. H. & Frank, E. (2005), Data Mining: Practical Ma-chine Learning Tools and Techniques, Morgan Kaufmann,San Francisco, CA.

Zappi, P., Stiefmeier, T., Farella, E., Roggen, D., Benini, L. &Troster, G. (2007), Activity recognition from on-body sen-sors by classifier fusion: sensor scalability and robustness,in Proceedings of the International Conference on IntelligentSensors, Sensor Networks and Information Process (ISSNIP2007), IEEE, Melbourne, 281–6.

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Selection of Accelerometer Location on Bricklayers Using Decision Trees