Brusey, James

Gaura, Elena

Kemp, John

Rednic, Ramona

Publisher statement: © 2012 IEEE. Reprinted, with permission, from Rednic, R., Gaura, E., Brusey, J., &amp; Kemp, J., Wearable posture recognition systems: factors affecting performance., In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, Jan 2012. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Coventry University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it

Coventry University Pure Portal

 Wearable posture recognition systems: factors affecting performance Rednic, R. , Gaura, E. , Brusey, J. and Kemp, J.   Author post-print deposited in CURVE March 2012  Original citation & hyperlink:  Rednic, R. , Gaura, E. , Brusey, J. and Kemp, J. (2012) 'Wearable posture recognition systems: factors affecting performance' In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2012) (pp. 200–203). IEEE.  http://bhi2012.embs.org/programme.php    Publisher statement: © 2012 IEEE. Reprinted, with permission, from Rednic, R., Gaura, E., Brusey, J., & Kemp, J., Wearable posture recognition systems: factors affecting performance., In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, Jan 2012.  This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Coventry University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.    This document is the author’s final manuscript version of the journal article, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.       CURVE is the Institutional Repository for Coventry University http://curve.coventry.ac.uk/open  Wearable posture recognition systems: factors affecting performanceRamona Rednic, Elena Gaura, James Brusey, John KempAbstract— This paper presents an investigation into the de-sign space for real-time, wearable posture classification systems;specifically, it analyses the impact of various factors/designchoices on classification accuracy when using C4.5 decisiontrees. The factors can be broadly divided into: 1) systemfactors (such as sensor sampling rate and number of sensorsused) and 2) algorithm and training factors (such as quantityof training data and temporal data features used). Thesefactors are analysed in the context of a case study involvingpostural activity monitoring of Explosive Ordinance Disposal(EOD) operatives. The case study involves classifying a set ofeight postures commonly encountered in EOD missions: sitting,walking, crawling, laying (on all sides) and kneeling. Designguidelines and generic lessons for a wider class of applicationscan be drawn from the work.I. INTRODUCTIONHigh accuracy, autonomous, easy-to-wear, real-time BodySensor Networks (BSNs) for posture classification can ben-efit a number of healthcare scenarios. Examples of pro-posed usage for such systems abound [1]–[5], with re-habilitation [6], Chronic Obstructive Pulmonary Disease(COPD) [3], [7] and classification of daily activities [1] beingcommon themes in the literature.However, in spite of intensive efforts to date, field-deployed posture classification is not common. Much ofthe work so far has focused on laboratory-based experi-mentation, evaluation in controlled environments, and be-spoke solutions for narrowly defined application scenarios.Controlled environments allow greater consistency in datagathering, data annotation, and the subsequent analysis,but may limit the range of real-world conditions that areconsidered in the system design or encountered duringsystem testing/validation. This then potentially limits thesuitability of the system for field deployment. Moreover, thelittle commonality between systems reported in the literaturewith regard to optimal sensor sampling rates, validationmethodologies, optimal (and minimal) sensor positioning orindeed optimal time/frequency based features to use for theposture classifiers makes the development effort for newsystems/applications considerable. This paper intends to easethe development of new BSN classifiers. It investigates thedesign space for wearable posture classification systems interms of the factors listed above and highlights the sensitivityof machine learning based classifiers to these factors.The application chosen for this investigation is that ofmonitoring the postures and activities of operatives under-taking Explosive Ordinance Disposal (EOD) missions. InAll authors are with the Cogent Computing Applied Research Cen-tre, Coventry University, England. Corresponding author: Ramona Rednicarabeladear@gmail.comthis application, the role of a complex, wearable, safety-monitoring system is to prevent the need for healthcare bypredicting the onset of Uncompensable Heat Stress (UHS)in the operative. The risk of UHS is high due to i) theoppressive environment created by the suit, which weighsover 40 kg, and ii) the physical exertion typical in suchmissions, particularly in hot environments. A predictive UHSonset algorithm was developed [8], that takes as its inputsthe operative skin temperature at twelve locations on thebody, together with the operative’s real-time posture and thusactivity level. In the EOD mission scenario, the postures mostoften encountered are crawling, kneeling, sitting, standing,walking, and laying (face down, face up and on one side).The paper is structured as follows: section II, providesa brief description of two wearable systems used in thisresearch: a real-time classification system produced by theauthors and an additional off-the-shelf system also usedfor high sampling rate data collection. Details of the datagathering process are also provided. Experimental results arepresented in section III. Finally the paper concludes withobservations drawn from the work.II. EXPERIMENTAL METHOD: SYSTEMS AND DATACOLLECTION TRIALSA. Wearable systems—hardware descriptionTwo different systems were used to capture 3D accelerom-eter data from multiple body locations in order to classifyposture and activity: 1) a real-time, in-house instrument and2) an off-the-shelf, data acquisition only instrument.The first instrument used (Class-act) is based around theuse of Gumstix Verdex XM4-bt [9] devices as the processingand communication platform. A bespoke expansion boardallows the connection of several acceleration sensor boardsto each Verdex device via a digital I2C bus. The combinationof Verdex device and expansion board is referred to hereas a node. The sensor boards were designed as a low-cost, small size, low-power wearable solution based oncommodity components, and each consists of a microcon-troller, a temperature sensor, and a tri-axial accelerometer(STMicroelectronics LIS3LV02DQ, ±2g range). Two nodesare used (for the upper and lower body), with up to elevenaccelerometers wired to them. The nodes communicate viaBluetooth, both node-to-node and node-to-base station andthe data is gathered at 10 Hz per sensor. Data processing andtransmission of posture information is routinely performedby one of the body nodes. However, for the work here, itwas transmitted and stored for processing.The second system used is based on wireless Shimmer [10]devices which contain 3 Freescale MMA7260Q accelerom-TransitionStanding Sitting KneelingWalkingTransitionTransition1 min 1 min 1 min 1 min 1 min 1 min 1 min 1 min Transition ~1−7 secTransition ~1−7 secTransition ~1−7 secLying on back Lying on front Crawling OnesideFig. 1. Regime R1, consisting of the eight postures identified forclassification by the system.eters, along with an MSP430F1611 microcontroller (theintegrated gyroscope was not used in this work). Data wasgathered at 100 Hz, from up to seven nodes.B. Experimental trialsData was collected from subjects performing three differ-ent activity regimes, the first of which is given in Figure 1.The other regimes, following the same basic structure, wereinclusive of the same set of postures but were task driven(for example loading objects into rucksacks when kneeling)or EOD mission driven. These regimes, along with thejustification for their use and the methods of data annotationapplied, are described in detail by Brusey et al. [11] andGaura et al. [12].The Class-act system was used for collecting data from17 subjects (seven female and ten male), between 1.59 mand 1.87 m tall and weighing between 49 kg and 88.9 kg.The Shimmer system was used for collecting data from ninesubjects from within the set above. A total of 40 data setswere gathered, representing 6 hours and 20 minutes of data.For the Class-act system, a total of eleven sensors wereplaced: symmetrically (left and right) on the calves, thighsand upper and lower arms; on the chest; and on the hipand ankle (right). For the Shimmer system the sensors wereplaced on the calf (right), thigh (right), upper arm (right),lower arm (right) and chest. Due to a limitation of theBluetooth protocol, the Shimmer based system was limitedto seven nodes plus the base station.C. Data ProcessingThe data processing flow for the purpose of the workpresented in this paper is shown in Figure 2. Note that amedian filter with a window size of three samples is appliedto help eliminate single-sample transient errors. A slidingwindow of 30 samples is used for the feature extractionprocess. Both the raw data and the selected data feature areused for classification. The feature computation was requiredin order to differentiate static from dynamic postures. Inprevious work by Brusey et al. [11] the need for featuresis investigated in depth. C4.5 decision trees, as implementedClassificationC4.5 decision treeData AnnotationMedian FilterCalibrationFeatureExtractionLOSOXV      Data3D AccelerationFig. 2. Data processing flow for system training.Number of sensorsClassification accuracy (% correct)70809010011 9 5 4 3 2 1Fig. 3. Multiple sensors classification results.by the Weka toolkit, were used for classification. Leave-One-Subject-Out Cross-Validation (LOSOXV) was adopted as avalidation method.III. DESIGN SPACE OPTIMIZATIONA. Effect of number of sensorsEleven commonly used sensor positions (as specified inSection II-B) were investigated, and the classification accu-racy evaluated, for the set of 8 postures considered here. 17subjects were used for training, with LOSOXV, for all 9 andbelow sensor combinations. Four subjects only were used forthe 11 sensors combination.The pruning of the number of sensors was based onsignal correlation (eliminating the left-hand side—5 sensorsremained), influence of various sensors on overall accuracy(eliminating the arms—3 sensors remained), and applicationrequirements (focusing on lower body only—2 sensors re-mained, on right thigh and calf). Four sensors variants aswell as single sensor variants were also analysed. In allcases, Windowed Variance (WVar) was used as the extracteddata feature (see Section III-D) with a sliding window of 30samples at 10 Hz.Figure 3 shows the classification accuracy for variousmulti-sensor combinations. Note that the classifier perfor-mance is not significantly sensitive to the reduction in sensornumbers towards a minimal configuration of 2 sensors (rightthigh and calf). The performance degrades significantly,however, if only a single sensor is used.Classification accuracy (% correct)405060708090Calf.RThigh.RCalf.LThigh.LLArm.RUArm.RChestLArm.LUArm.LAnkle HipFig. 4. Overall results for classification using one sensor location.10 20 50 10080859095100Sample frequency (Hz)Classification accuracy (% correct)Fig. 5. Effect of different frequenciesThe optimal position for a single sensor is the calf (Fig-ure 4), with the arm sensors performing worst for this postureset, as expected. The chest sensor appears to perform lesswell than expected from the literature. The hip sensor pro-vides adequate (but lower than calf) performance, althoughin the analysis here only 7 subjects were used for LOSOXVof hip and ankle variants.B. Effect of sampling rateThe most common sampling rates encountered in the liter-ature are 100 Hz and 20 Hz. The sensitivity of classificationaccuracy was thus analysed for sample rates in this interval(Figure 5). The data was collected using the Shimmer systemwith a 7 sensor configuration, from 9 subjects performing allregimes described in Section II-B. The window sizes usedfor feature extraction were adjusted to operate over the sametime period in each case. It can be seen that performanceappears to be insensitive to sampling rate. Thus, 10 Hzsampling is sufficient for this set of 8 postures.C. Effect of training set sizeThe effect of training set size was investigated using datacollected from 17 subjects using the Class-act system. TheNumber of subjects in training setClassification accuracy (% correct)607080901001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Fig. 6. Classification performance when using different number of trainingsetsWVar Energy SMA Mag. SVM RMS Mean EWMA65707580859095100Data feature usedClassification accuracy (% correct)Fig. 7. Classification accuracy when using extracted data features.feature extracted from the data for this investigation wasWVar over a 30 sample window. Nine sensor locations wereused, excluding the hip and ankle sensors. Figure 6 shows theresults obtained when training the classifier on N subjects(for 1 ≤ N ≤ 16) and testing on an unseen subject. This wasrepeated 10 times with training and test subjects randomlyselected (without replacement). It can be seen that there isno significant increase in classifier accuracy when trainingon more than 8 subjects.D. Effect of different featuresThe effect of different extracted data features on classi-fication accuracy was investigated at a sampling frequencyof 10 Hz. The features considered were: Windowed Mean(WM), magnitude, Signal Vector Magnitude (SVM), Win-dowed Variance (WVar), Root Mean Square (RMS), Energy,Signal Magnitude Area (SMA), and EWMA [3], [11], [13].A 30 sample window was used for WM, SVM, WVar, RMS,Energy, SMA. Figure 7 shows the classification accuracyusing LOSOXV over 17 sets of data, classifying all 8 pos-tures. The best overall performance was obtained for WVar.The best classification performance for walking, standing,crawling was obtained for WVar feature. While better per-formance was achieved by EWMA when classifying layingface up, magnitude when classifying laying on one side andmagnitude or mean when classifying laying face down. Allfeatures performed well when classifying sitting.IV. CONCLUSIONThere are many factors that can potentially affect the suc-cess of supervised learning of posture / activity classificationand this paper has examined several key ones. First, thenumber of sensors can often be reduced. In this case, itwas found that for the set of postures examined, two 3Daccelerometers were sufficient. Second, sensing frequencycan be as low as 10 Hz. Third, while in principle, moresubjects will improve the quality of a trained classifier, littleimprovement was found beyond 8 subjects. Finally, selectingappropriate features is critical. To recognise activities in-volving dynamic movement, carefully chosen features (suchas WVar and Energy) are essential for building accurateclassifiers.The combination of supervised machine learning andMEMS accelerometers provide a powerful solution to au-tomatic posture and activity recognition either as an endin itself or to support a larger body sensing system. Theapproach is becoming so well established as to suggest that aparadigm shift is occurring. The important research questionsare no longer “can such systems be built?” but rather “whatguidance can be given to their design?”. This work representsthe first steps towards providing an answer.REFERENCES[1] X. Long, B. Yin, and R. M. Aarts, “Single-accelerometer-based dailyphysical activity classification,” in Proceedings of the 31st AnnualInternational Conference of the IEEE Engineering in Medicine andBiology Soc, ser. EMBC’09, Minneapolis, USA, 2–6 September 2009,pp. 6107–6110, iSBN: 978-1-4244-3296-7.[2] J. Pansiot, D. Stoyanov, D. McIlwraith, B. P. Lo, and G. Z. Yang, “Am-bient and wearable sensor fusion for activity recognition in healthcaremonitoring systems,” 4th International Workshop on Wearable andImplantable Body Sensor Networks (BSN 2007), vol. 13, pp. 208–212,26-28 March 2007, iSSN 1680-0737.[3] M. J. Mathie and B. G. Celler, “A system for monitoring posture andphysical activity using accelerometers,” in 23rd Annual InternationalConference of the IEEE Engineering in Medicine and Biology Society,2001, pp. 3654–3657.[4] J. Liu, T. Kanno, M. Akashi, W. Chen, D. Wei, G. Wu, and N. Takeda,“Patterns of bipedal walking on tri-axial acceleration signals and theiruse in identifying falling risk of older people,” in Proceedings of theSixth IEEE International Conference on Computer and InformationTechnology (CIT’06), vol. 0. Seoul, Korea: IEEE Computer Society,20–22 September 2006, p. 205.[5] B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew, C. J. Büla,and P. Robert, “Ambulatory system for human motion analysisusing a kinematic sensor: monitoring of daily physical activityin the elderly.” IEEE transactions on bio-medical engineering,vol. 50, no. 6, pp. 711–723, June 2003. [Online]. Available:http://dx.doi.org/10.1109/TBME.2003.812189[6] G.-Z. Yang, Body Sensor Networks. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.[7] B. G. Steele, B. Belza, K. Cain, C. Warms, J. Coppersmith,and J. Howard, “Bodies in motion: monitoring daily activity andexercise with motion sensors in people with chronic pulmonarydisease.” Journal Of Rehabilitation Research And Development,vol. 40, no. 5 Suppl 2, pp. 45–58, 2003. [Online]. Available:http://www.ncbi.nlm.nih.gov/pubmed/15074453[8] J. Kemp, “Body sensor networks for health monitoring: A safetycritical mission application,” Ph.D. dissertation, Coventry University,2010.[9] Gumstix, “Gumstix Motherboard IO,” available from http://docwiki.gumstix.org/.[10] Shimmer, http://www.shimmer-research.com/; Accessed 12-October-2011.[11] J. Brusey, R. Rednic, E. I. Gaura, J. Kemp, and N. Poole, “Posturalactivity monitoring for increasing safety in bomb disposal missions,”Measurement Science and Technology, vol. 20, no. 7, p. 075204(11pp), 2009. [Online]. Available: http://stacks.iop.org/0957-0233/20/075204[12] E. Gaura, J. Brusey, J. Kemp, and C. D. Thake, “Increasing safetyof bomb disposal missions: A body sensor network approach,” IEEETransactions on Systems, Man and Cybernetics, Part C: Applicationsand Reviews, vol. 39, no. 6, pp. 621–636, November 2009.[13] N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman, “Activityrecognition from accelerometer data,” in Proceedings of the 17thconference on Innovative applications of artificial intelligence -Volume 3. AAAI Press, 2005, pp. 1541–1546. [Online]. Available:http://dl.acm.org/citation.cfm?id=1620092.1620107

Wearable posture recognition systems: factors affecting performance

https://pure.coventry.ac.uk/ws/files/4019932/Wearable%20posture%20recognition.pdf

Wearable posture recognition systems: factors affecting performance

Abstract

Similar works

Full text

Available Versions

Coventry University Pure Portal