Applying singular value decomposition on accelerometer data for 1D convolutional neural network based fall detection

Electronics LettersVolume 55, Issue 6 p. 320-322 Information and communicationsFree Access Applying singular value decomposition on accelerometer data for 1D convolutional neural network based fall detection H. Cho, H. Cho orcid.org/0000-0001-9912-1002 Kookmin University, Seoul, Republic of KoreaSearch for more papers by this authorS.M. Yoon, Corresponding Author S.M. Yoon smyoon@kookmin.ac.kr Kookmin University, Seoul, Republic of KoreaSearch for more papers by this author H. Cho, H. Cho orcid.org/0000-0001-9912-1002 Kookmin University, Seoul, Republic of KoreaSearch for more papers by this authorS.M. Yoon, Corresponding Author S.M. Yoon smyoon@kookmin.ac.kr Kookmin University, Seoul, Republic of KoreaSearch for more papers by this author First published: 01 March 2019 https://doi.org/10.1049/el.2018.6117Citations: 11AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The usefulness of applying singular value decomposition (SVD) on triaxial accelerometer data for one-dimensional (1D) convolutional neural network (CNN) based fall and activity recognition is investigated. Three-dimensional reduction methods, namely, SVD, sparse principal component analysis, and kernel principal component analysis, are compared for their effectiveness in extracting useful features for fall and activity recognition. Experiments conducted on three public falls and activities of daily living datasets show that SVD applied to acceleration data coupled with raw acceleration data or acceleration signal magnitude vector exhibited better 1D CNN fall and activity recognition accuracy than those using other principal component analysis based acceleration features. Introduction Falls cause serious injury to elderly and pose a major health risk to worldwide ageing population. According to the World Health Organisation, falls are the second leading cause of accidental or unintentional injury deaths worldwide. As the global population aged 65 and over continues to grow, real-time technology for cost-effective sensing of human activities such as falls has gained much attention during the last decade. Existing fall detection approaches include: wearable device-based approaches, which utilise sensors adhered to the body to detect motion; ambience-based approaches, which fuse audio, video, and environmental detectors such as floor vibration detector to sense fall events; and vision-based approaches, which utilise cameras to collect image and video data to detect falls [1]. In this study, we investigate a wearable device-based approach that utilises a single triaxial accelerometer for fall and activity recognition. Many existing technologies employ a single or multiple inertial sensors or accelerometers to collect triaxial acceleration signals to build machine learning classifiers for fall and activity recognition. The types of machine learning classifier include support vector machine, k-nearest neighbours, decision tress, shallow/deep neural networks, and so on [2-4]. As activity classifiers are usually built using third person activity data, x, y, and z axes' acceleration signals are often transformed into signal magnitude vector (SMV) to account for the variability in individual signals. The advantage of using SMV in fall and activity recognition is demonstrated in [5]. Among the various machine learning techniques, convolutional neural networks (CNNs) show great potential in identifying salient patterns of activity signals [6]. However, even the state-of-the-art CNN classifiers experience difficulty in distinguishing similar activity signals. Fig. 1 compares the two types of falling activities, i.e. falling right (top row) and falling left (bottom row), sampled for 3 s (Raw) and then transformed to SMV or other one-dimensional (1D) vectors using various dimension reduction methods. We see that when the two types of falls are expressed as either raw signals or SMVs, they are difficult to distinguish; but when the two falls are transformed into 1D signals using singular value decomposition (SVD), sparse principal component analysis (SPCA), and kernel principal component analysis (KPCA), the two falls become more distinguishable. In this Letter, we compare the usefulness of three-dimensional reduction methods, namely, SVD, SPCA, and KPCA, on triaxial accelerometer data for 1D CNN based fall and activity recognition using three public falls and activities of daily living (ADL) datasets. We show that SVD concatenated with the raw signal or SMV returns favourable fall and activity recognition accuracy than other dimension reduction methods or using only the raw signal or SMV. Dimension reduction methods Before we define the three-dimensional reduction methods, we first define a basic feature, SMV, as a Euclidean norm of acceleration values on x, y, and z axes as follows: (1) SVD is a factorisation of a matrix, where given an matrix X, SVD factorises X into U, an unitary matrix, , an rectangular diagonal matrix with non-negative real numbers on the diagonal, and V, an unitary matrix. A truncated SVD computes the k largest singular values to produce low-rank approximation of the original data X. The matrix below is an matrix and , a matrix. forms a dimension reduced matrix. (2) SPCA finds a set of sparse components that can best reconstruct the original data [7]. The solution is obtained by solving a PCA problem with an penalty of the components. The degree of sparsity is adjusted through the hyperparameter . Larger values shrink many coefficients to zero. We set and in our experiments (3) KPCA is an extension of PCA, which performs non-linear dimension reduction using various kernel methods, e.g. linear, polynomial, Gaussian kernels, and so on. Assume that we have a non-linear transformation from an original D-dimensional feature space to an M-dimensional feature space, where . Then each data point is projected to a point . Instead of directly performing standard PCA in the new feature space, we use kernel methods or kernel tricks to simplify the PCA calculation [8]. We use linear kernel in our experiments and extract the largest principal component. In this Letter, all triaxial acceleration signals are reduced to 1D signal by setting . Fig 1Open in figure viewerPowerPoint Comparison of 1D signals of two different falls, i.e. falling right (top) and falling left (bottom), generated using various dimension reduction methods. The triaxial accelerometer signals (i.e. x, y, and z axes signals in the leftmost ‘Raw’ column) are reduced to a 1D signal. From left to right: SMV (signal magnitude vector), SVD (singular value decomposition), SPCA (sparse principal component analysis), and KPCA (kernel principal component analysis) Public dataset Three public falls and ADL datasets, i.e. UniMiB SHAR [5], SisFall [9], and UMAFall [10], were used in the comparative fall and activity recognition performance evaluation experiment. All three datasets contained triaxial acceleration signals for both falls and ADLs. For all three datasets, we used triaxial acceleration signals collected from a single accelerometer. For detailed fall and activity labels, see [11]. Table 1 summarises the content of the public datasets. Table 1. Summary of three public falls and ADL datasets UniMiB SHAR [5] SisFall [9] UMAFall [10] No. of activities (ADLs/falls) 17 (9/8) 34 (19/15) 11 (8/3) No. of subjects (female/male) 30 (24/6) 38 (19/19) 17 (7/10) Sampling rate 50 Hz 200 Hz 200 Hz No. of data/s 50/s 50/s 50/s Window size 3 s 9 s 9 s Data length 151 450 450 Total no. of data 11,771 3900 2655 n-fold CV 30 23 17 Consists of two different age groups: 19–30 years (23 subjects, 12 female and 11 male) and 60–75 years (15 subjects, 7 female and 8 male). We use 23 younger human subject data only since most elderly subject data do not contain fall data. n-fold CV: n-fold leave-one-subject-out cross-validation (number of subjects). Note that for SisFall and UMAFall datasets, we selected 1 out of 4 data points from 200 Hz samples to construct reduced 50 Hz samples (50/s). The window size of SisFall and UMAFall datasets was set to 9 s, longer than those of the UniMiB SHAR dataset (3 s), because some activities such as ‘standing, get into a car, remain seated and get out of the car (a single activity label with multiple actions defined in the SisFall dataset)’ required longer time to complete the activity. We performed an n-fold leave-one-subject-out (LOSO) cross-validation (CV) on all datasets. CNN architecture and experimental setup We used the same 1D CNN architecture as shown in Fig. 2 in all experiments. Overall, the network consisted of two layers of ‘convolution–batch normalisation–rectified linear unit’ constituents. The output of the convolution layers was flattened and inputted to the fully-connected layer having 90% dropout at the end. Three window sizes, 32, 42, and 52 with stride size of 1 was used in both the first and second layers of 1D convolution, and the convolution filter size was set to 16 and 32 for each layer, respectively. We used ADAM optimiser with learning rate of 0.001 for UniMiB SHAR dataset and 0.0001 for SisFall and UMAFall datasets, and used categorical cross-entropy as loss function. The number of epochs was set to 50, and the batch size was varied (64 or 32) according to the input feature size. The models with the best validation accuracy were saved. All experiments were conducted on a desktop computer equipped with Intel Core i7-4790 K CPU (4 core, 4.0 GHz) having 32 GB RAM and Nvidia GeForce GTX 960 GPU. The operating system was Ubuntu version 16.04. Fig 2Open in figure viewerPowerPoint Architecture of 1D CNN used in the experiments We compared 15 different signal features given in Table 2. Those containing two features were concatenated to form a longer 1D vector. As for the evaluation metric, n-fold LOSO CV accuracy was used. Table 2. n-fold LOSO CV accuracy (%) of 1D CNN fall and activity recognition using various features of the three benchmark datasets Features (‘+’ indicates concatenation) 1D CNN HAR performance on 3 benchmark datasets: n-fold LOSO CV accuracy (%) (standard deviation) UniMiB SHAR SisFall UMAFall baseline CNN on Raw [2] 74.97% — — raw 74.37% (8.07) 61.37% (7.47) 57.65% (13.11) SMV 72.41% (8.22) 46.89% (5.31) 44.04% (11.48) SVD 73.96% (8.42) 50.24% (6.58) 54.08% (6.97) KPCA 73.56% (7.14) 48.32% (4.36) 51.56% (7.16) SPCA 73.18% (7.42) 46.21% (5.67) 45.74% (8.70) Raw+SMV 74.81% (8.94) 60.50% (6.68) 58.11% (10.83) Raw+SVD 74.02% (8.78) 63.08% (6.87) 64.69% (8.28) Raw+KPCA 75.08% (7.74) 62.51% (5.94) 64.05% (8.45) Raw+SPCA 74.66% (8.57) 61.53% (5.39) 58.94% (11.25) SMV+SVD 75.65% (8.03) 55.51% (4.98) 55.45% (7.76) SMV+KPCA 74.90% (8.13) 54.39% (4.53) 52.01% (4.92) SMV+SPCA 71.89% (8.89) 52.84% (4.17) 51.92% (8.38) SVD+KPCA 74.62% (7.74) 48.55% (5.71) 51.80% (8.92) SVD+SPCA 74.28% (8.05) 49.34% (6.02) 53.82% (8.51) KPCA+SPCA 73.39% (7.14) 49.22% (5.21) 52.23% (7.49) Each dataset's best performance is marked in bold. SMV: signal magnitude vector, SVD: singular value decomposition, KPCA: kernel principal component analysis, SPCA: sparse principal component analysis. Results and conclusion Table 2 reports the n-fold LOSO CV fall and activity recognition accuracy of 1D CNN using various features. The values given after the accuracy indicate the standard deviation of the n-fold CV accuracy. The top row of Table 2 shows the baseline 2D CNN accuracy of UniMiB SHAR dataset using Raw feature given by an existing work [2]; their CNN model is a simple, single layer 2D convolution over the triaxial accelerometer signal. Compared to the existing baseline model [2], our 1D CNN using Raw feature shows similar performance (74.37%). However, when the dimension reduction features are combined, the performance improves to 75.65% (SMV+SVD) for UniMiB SHAR dataset. In the case of SisFall and UMAFall datasets, existing works have so far only investigated binary classification results, i.e. Fall and No_Fall, hence, no baseline accuracy is given for multiclass (34 and 11 classes) classification. For these two datasets, our experiments show that Raw+SVD performs the best (63.08 and 64.69%, respectively). It is interesting to note that Raw feature generally outputs higher standard deviation; this means that using Raw feature tends to output wide range of accuracy. Fig. 3 provides a horizontal bar graph of Table 2. The red vertical line indicates the best accuracy among the 15 features. We see that either Raw or SMV combined with SVD shows top level performance for all three datasets. The second best performing feature is Raw+KPCA (UniMiB: 75.08%, SisFall: 62.51%, UMAFall: 64.05%). When comparing the performance of single features, Raw feature shows the best performance; we see that directly using the raw signal in 1D CNN is favourable than using various dimension reduction features. When comparing the individual dimension reduction methods, SVD performs the best among the three (SVD, KPCA, and SPCA in that order). We recommend Raw+SVD or SMV+SVD as a viable feature transformation of triaxial accelerometer data for enhancing 1D CNN based fall and activity recognition. Fig 3Open in figure viewerPowerPoint n-fold LOSO CV accuracy of 1D CNN fall and activity recognition using various features. From left to right: UniMiB SHAR, SisFall, and UMAFall dataset. The red vertical line indicates the best accuracy among different features. Refer Table 2 for detailed accuracy (%) Acknowledgments Heeryon Cho and Sang Min Yoon were supported by the National Research Foundation of Korea grants (no. 2017R1A2B4011015 & no. 2016R1D1A1B04932889). 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