Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer ReviewedPostprint (author's final draft

Anguita, Davide

Ghio, Alessandro

Llanas Parra, Francesc Xavier

Oneto, Luca

Reyes Ortiz, Jorge Luis

English

UPCommons. Portal del coneixement obert de la UPC

Human Activity Recognition on Smartphonesusing a Multiclass Hardware-Friendly SupportVector MachineDavide Anguita1, Alessandro Ghio, Luca Oneto, Xavier Parra2, andJorge L. Reyes-Ortiz1,21 DITEN - Universita` degli Studi di Genova, Genoa I-16145, Italy,{davide.anguita,alessandro.ghio,luca.oneto}@unige.it2 CETpD - Universitat Polite`cnica de Catalunya, Vilanova i la Geltru´ 08800, Spain,xavier.parra@upc.edu, jorge.luis.reyes@estudiant.upc.eduAbstract. Activity-Based Computing [1] aims to capture the state ofthe user and its environment by exploiting heterogeneous sensors inorder to provide adaptation to exogenous computing resources. Whenthese sensors are attached to the subject’s body, they permit continu-ous monitoring of numerous physiological signals. This has appealing usein healthcare applications, e.g. the exploitation of Ambient Intelligence(AmI) in daily activity monitoring for elderly people. In this paper, wepresent a system for human physical Activity Recognition (AR) usingsmartphone inertial sensors. As these mobile phones are limited in termsof energy and computing power, we propose a novel hardware-friendlyapproach for multiclass classification. This method adapts the standardSupport Vector Machine (SVM) and exploits fixed-point arithmetic forcomputational cost reduction. A comparison with the traditional SVMshows a significant improvement in terms of computational costs whilemaintaining similar accuracy, which can contribute to develop more sus-tainable systems for AmI.Keywords: Activity Recognition, SVM, Smartphones, Hardware-Friendly1 IntroductionSince the appearance of the first commercial hand-held mobile phones in 1979,it has been observed an accelerated growth in the mobile phone market whichhas reached by 2011 near 80% of the world population [2]. This shows that ina very short time, mobile devices will become easily accessible to virtually ev-erybody. Smartphones, which are a new generation of mobile phones, are nowoffering many other features such as multitasking and the deployment of a va-riety of sensors, in addition to the basic telephony. Current efforts attempt toincorporate all these features while maintaining similar battery lifespans anddevice dimensions.The integration of these mobile devices in our daily life is rapidly growing. Itis envisioned that such devices will seamlessly keep track of our activities, learnfrom them, and subsequently help us to make better decisions regarding our fu-ture actions [3]. This is one key concepts in which AmI relies on. In this paper, weemploy smartphones for human Activity Recognition with potential applicationsin assisted living technologies. We take into account current hardware limitationsand propose a new alternative for AR that requires less computational resourcesto operate.AR aims to identify the actions carried out by a person given a set of obser-vations of itself and the surrounding environment. Recognition can be accom-plished, for example, by exploiting the information retrieved from inertial sensorssuch as accelerometers [4]. In some smartphones these sensors are embedded bydefault and we benefit from this to classify a set of physical activities (standing,walking, laying, walking, walking upstairs and walking downstairs) by processinginertial body signals through a supervised Machine Learning (ML) algorithm forhardware with limited resources.This paper is structured in the following way: The state of the art regardingprevious work is depicted in Section 2. The description of the adopted method-ology is presented in Section 3. There, the experimental set up for capturing thedata and the mathematical description for the proposed Multiclass HardwareFriendly Support Vector Machine (MC-HF-SVM) are explained. Experimentalresults and conclusions of this research are described in Sections 4 and 5.2 Related WorkThe development of AR applications using smartphones has several advantagessuch as easy device portability without the need for additional fixed equipment,and comfort to the user due to the unobtrusive sensing. This contrasts with otherestablished AR approaches which use specific purpose hardware devices such asin [5] or sensor body networks [6]. Although the use of numerous sensors couldimprove the performance of a recognition algorithm, it is unrealistic to expectthat the general public will use them in their daily activities because of thedifficulty and the time required to wear them. One drawback of the smartphone-based approach is that energy and services on the mobile phone are shared withother applications and this become critical in devices with limited resources.ML methods that have been previously employed for recognition includeNaive Bayes, SVMs, Threshold-based and Markov chains [6]. In particular, wemake use of SVMs for classification as it was also used in [7] and [8]. Althoughit is not fully clear which method performs better for AR, SVMs have confirmedsuccessful application in several areas including heterogeneous types of recogni-tion such as handwritten characters [9] and speech [10].In ML, fixed-point arithmetic models have been previously studied [11, 12]initially because devices with floating-point units were unavailable or expensive.The possibility of retaking these approaches for AmI systems that require eitherlow cost devices or to allow load reduction in multitasking mobile devices hasnowadays become particularly appealing. Anguita et al. in [13] introduced theconcept of a Hardware-Friendly SVM (HF-SVM). This method exploits fixed-point arithmetic in the feed-forward phase of the SVM classifier, so as to allowthe use of this algorithm in hardware-limited devices. In this paper, we extendthis model for multiclass classification.The SVM algorithm was originally proposed only for binary classificationproblems but it has been adapted using different schemes for multiclass problemssuch as in [9]. In particular, we have chosen the One-Vs-All (OVA) method asits accuracy is comparable to other classification methods as demonstrated byRifkin and Klautau in [14], and because its learned model uses less memory whencompared for instance to the One-Vs-One (OVO) method. This is advantageouswhen used in limited resources hardware devices.3 Methodology3.1 Experimental SetupFig. 1. Activity Recognition process pipeline.The experiments have been carried out with a group of 30 volunteers withinan age bracket of 19-48 years. Each person performed the six activities previouslymentioned wearing the smartphone on the waist. The experiments have beenvideo-recorded to facilitate the data labeling. The obtained database has beenrandomly partitioned into two sets, where 70% of the patterns has been used fortraining purposes and 30% as test data: the training set is then used to train amulticlass SVM classifier which is described in the following section. A SamsungGalaxy S2 smartphone has been exploited for the experiments, as it containsan accelerometer and a gyroscope for measuring 3-axial linear acceleration andangular velocity respectively at a constant rate of 50Hz, which is sufficient forcapturing human body motion.For AR purposes, we have developed a smartphone application based onthe Google Android Operating System. The recognition process starts with theacquisition of the sensor signals, which are subsequently pre-processed by apply-ing noise filters and then sampled in fixed-width sliding windows of 2.56 sec and50% overlap. From each window, a vector of 17 features is obtained by calculat-ing variables from the accelerometer signals in the time and frequency domain(e.g. mean, standard deviation, signal magnitude area, entropy, signal-pair cor-relation, etc.). Fast Fourier Transform is used for finding the signal frequencycomponents. Finally, these patterns are used as input of the trained SVM Clas-sifier for the recognition of the activities. The entire AR process pipeline is asshown in Figure 1.3.2 The Multiclass HF-SVM modelConsider a dataset consisting of l patterns where each one is a pair of the type(xi, yi)∀i ∈ [1, ..., l], xi ∈ <m, and yi = ±1. A standard binary SVM canbe learned by solving a Convex Constrained Quadratic Programming (CCQP)minimization problem which is given by the following formulation [13]:minα12αTQα− rTα (1)0 ≤ αi ≤ C ∀i ∈ [1, ..., l] , (2)yTα = 0, (3)where C is the regularization parameter, ri = 1 ∀i and Q is the symmetricpositive semidefinite l × l kernel matrix where qij = yiyjK (xi,xj).After solving this CCQP problem, the αi ∀i ∈ [1, ..., l] values can be foundand used to predict the class of any new pattern using the Feed-Forward Phase(FFP) formulation of the SVM:f (x) =l∑i=1yiαiK (xi,x) + b, (4)where b is the bias term and is obtained by using the method proposed in [15].Clearly, this output is not valid for use in a fixed-point arithmetic approach asthese αi values are by default real numbers ranging between zero and C. Hence anormalization procedure is proposed that will not affect the sign of the classifieroutput but only its magnitude, maintaining the performance of the SVM as it isknown that the class is only determined by the feed-forward function sign. TheHF-SVM described in [13] proposes a new vector β and it is defined as:βi = αi2k − 1C, (5)where k is the number of bits and βi ∈ N0. Also the bias term b of the FFPformulation is removed with the purpose of having a complete integer parameterprediction. The modified formulation is:minβ12βTQβ − sTβ (6)0 ≤ βi ≤ 2k − 1C∀i ∈ [1, ..., l] , (7)where si =(2k − 1) /C ∀i ∈ [1, ..., l]. Note that the cost function keeps un-changed but Eq. 3 disappears. This is consistent because we use an RBF kernelsuch as the Laplacian that has infinite VC dimension and the bias b becomesunnecessary [16].Lastly, to hold true the assumption of having a FFP with only integer values,the kernel K (·, ·) and the input vector x are also represented with u and v bitsrespectively [13]:0 ≤ K (xi,x) ≤ 1− 2−u ∀i ∈ [1, ..., l] , (8)0 ≤ xi ≤ 1− 2−v ∀i ∈ [1, ...,m] . (9)The modified FFP formulation with the β vector is:f (x) =l∑i=1yiβiK (xi,x) . (10)We opted for a Laplacian kernel, instead of the more conventional Gaussiankernel, as it is more convenient for hardware limited devices because it can beeasily computed using shifters:K (xi,xj) = 2−γ‖xi−xj‖1 , (11)where γ > 0 is the kernel hyperparameter and the norm is expressed as ‖x‖1 =∑mi=1 |x|.The complete learning process for each SVM consists of performing gridsearch model selection of the C and γ hyperparameters that converge with theminimum validation error. A k-fold cross validation with k = 10 is employed foreach hyperparameter pair.The output of the FFP varies depending on each learned SVM model as theseare not normalized. Our extension of this binary problem for the multiclass caseemploys the OVA method in which each class c is compared against the otherclasses. This evidently requires a method to allow comparability between theoutput of each SVM. For this reason, we have opted to compute probabilityestimates for each SVM pc (x) and choose the one with the highest probabilityas the actual class c∗ of each test pattern.We have developed the following approach using the J.Platt’s method forestimating probability estimates [17]. The training dataset and the learned SVMmodel are employed to fit the output of the FFP f (x) with a sigmoid functionof the form:p (x) =11 + e(Af(x)+B), (12)in which p (x) is the probability estimate, and A and B are function parameterswhich are properly fitted on the available learning samples.Taking into account that we have the fixed-point arithmetic restriction, thesigmoid function cannot be directly applied on f (x). To solve this, we haveFig. 2. Comparison between the MC-SVM and the MC-HF-SVM: Classification testerror for the MC-HF-SVM with different values of k against the MC-SVM which isrepresented with k = 64 bits.designed a method based on Look-Up-Tables (LUTs). By defining a fixed numberof bits t, it is possible to map the probability estimates p (x) given f (x) withoutrequiring floating-point arithmetic. It has been observed that t = 8 is suitablefor this application and it only requires a LUT with 256 elements.4 Experimental resultsFor evaluating the performance of the MC-HF-SVM, a set of experiments werecarried out using the AR dataset described in this paper. They consisted oflearning SVM models with different number of bits k for β estimation and thencomparing their performance in terms of test data error against the standardfloating-point Multiclass SVM (MC-SVM). The results of this comparison aredepicted in Figure 2.The experiment shows that for this dataset k = 6 bits are sufficient forachieving a performance comparable with the MC-SVM approach that uses 64-bit floating-point arithmetic. The test error remains stable (around 1% variation)for k values from 64 to 6 bits, but it increases noticeably to 15% when it reaches5 bits. Moreover, it is also seen from the graph that some values of k producedsmaller errors than the one obtained with the MC-SVM. This finding coincideswith that in [18] and then in [19]. Thus, suggesting that these methods canexhibit a lower generalization error. We believe that the truncation of the modelparameters could be producing a regularization effect.The classification results of the MC-SVM and the MC-HF-SVM with k = 8bits for the test data are depicted by means of a confusion matrix in Table 1,where estimates of the overall accuracy, recall and precision are also given. 789test samples were evaluated with approximately equal number of samples perclass. Both confusion matrices show similar outputs varying slightly in the classi-fication accuracy of the activities walking downstairs and walking upstairs. Theyalso expose some false predictions mostly in the dynamic activities. Static activ-Method MC-SVM MC-HF-SVM k = 8 bitsActivity WalkingUpstairsDownstairsStandingSittingLayingRecall%WalkingUpstairsDownstairsStandingSittingLayingRecall%Walking 109 0 5 0 0 0 95.6 109 2 3 0 0 0 95.6Upstairs 1 95 40 0 0 0 69.8 1 98 37 0 0 0 72.1Downstairs 15 9 119 0 0 0 83.2 15 14 114 0 0 0 79.7Standing 0 5 0 132 5 0 93.0 0 5 0 131 6 0 92.2Sitting 0 0 0 4 108 0 96.4 0 1 0 3 108 0 96.4Laying 0 0 0 0 0 142 100 0 0 0 0 0 142 100Precision % 87.2 87.2 72.6 97.1 95.6 100 89.3 87.2 81.7 74.0 97.8 94.7 100 89.0Table 1. Confusion Matrix of the classification results on the test data using the tra-ditional floating-point MC-SVM (Left) and the MC-HF-SVM with k = 8 bits (Right).Rows represent the actual class and columns the predicted class. The diagonal entries(in bold) show the number of test samples correctly classified.ities instead perform better, particularly the laying activity which obtained anaccuracy of 100%.5 ConclusionsIn this paper, we proposed a new method for building a multiclass SVM us-ing integer parameters. The MC-HF-SVM is an appealing approach for use inAmI systems for healthcare applications such as activity monitoring on smart-phones. This alternative that employs fixed-point calculations, can be used forAR because it requires less memory, processor time and power consumption.Moreover, it provides accuracy levels comparable to traditional approaches suchas the MC-SVM that uses floating-point arithmetic.The experimental results confirm that even with a reduction of bits equal to6 for representing the learned MC-HF-SVM model parameter β, it is possibleto substitute the standard MC-SVM. This outcome brings positive implicationsfor smartphones because it could help to release system resources and reduceenergy consumption. Future work will present a publicly available AR datasetto allow other researchers to test and compare different learning models.Acknowledgments. This work was supported in part by the Erasmus MundusJoint Doctorate in Interactive and Cognitive Environments, which is fundedby the EACEA Agency of the European Commission under EMJD ICE FPAn 2010-0012.References1. Davies, N., Siewiorek, D.P., Sukthankar, R.: Activity-based computing. IEEEPervasive Computing 7(2) (April 2008) 20–212. Ekholm, J., Fabre, S.: Forecast: Mobile data traffic and revenue, worldwide, 2010-2015. In: Gartner Mobile Communications Worldwide. (July 2011)3. Cook, D.J., Das, S.K.: Pervasive computing at scale: Transforming the state of theart. Pervasive and Mobile Computing 8(1) (February 2012) 22–354. 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Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer Reviewe

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Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

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