Browsing by Subject "Human activity recognition"

Now showing 1 - 13 of 13

Open Access
Activity recognition invariant to position and orientation of wearable motion sensor units
(2019-04) Yurtman, Aras
We propose techniques that achieve invariance to the placement of wearable motion sensor units in the context of human activity recognition. First, we focus on invariance to sensor unit orientation and develop three alternative transformations to remove from the raw sensor data the effect of the orientation at which the sensor unit is placed. The first two orientation-invariant transformations rely on the geometry of the measurements, whereas the third is based on estimating the orientations of the sensor units with respect to the Earth frame by exploiting the physical properties of the sensory data. We test them with multiple state-of-the-art machine-learning classifiers using five publicly available datasets (when applicable) containing various types of activities acquired by different sensor configurations. We show that the proposed methods achieve a similar accuracy with the reference system where the units are correctly oriented, whereas the standard system cannot handle incorrectly oriented sensors. We also propose a novel non-iterative technique for estimating the orientations of the sensor units based on the physical and geometrical properties of the sensor data to improve the accuracy of the third orientation-invariant transformation. All of the three transformations can be integrated into the pre-processing stage of existing wearable systems without much effort since we do not make any assumptions about the sensor configuration, the body movements, and the classification methodology. Secondly, we develop techniques that achieve invariance to the positioning of the sensor units in three ways: (1) We propose transformations that are applied on the sensory data to allow each unit to be placed at any position within a pre-determined body part. (2) We propose a transformation technique to allow the units to be interchanged so that the user does not need to distinguish between them before positioning. (3) We employ three different techniques to classify the activities based on a single sensor unit, whereas the training set may contain data acquired by multiple units placed at different positions. We combine (1) with (2) and also with (3) to achieve further robustness to sensor unit positioning. We evaluate our techniques on a publicly available dataset using seven state-of-the-art classifiers and show that the reduction in the accuracy is acceptable, considering the exibility, convenience, and unobtrusiveness in the positioning of the units. Finally, we combine the position- and orientation-invariant techniques to simultaneously achieve both. The accuracy values are much higher than those of random decision making although some of them are significantly lower than the reference system with correctly placed units. The trade-off between the exibility in sensor unit placement and the classification accuracy indicates that different approaches may be suitable for different applications.
Open Access
Activity recognition invariant to sensor orientation with wearable motion sensors
(MDPI AG, 2017) Yurtman, A.; Barshan, B.
Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
Open Access
Farklı yapay sinir ağı temelli sınıflandırıcılar ile insan hareketi tanımlama
(IEEE, 2017-05) Çatalbaş, Burak; Morgül, Ömer; Çatalbaş, Bahadır
İnsan Hareketi Tanımlanması, taşıdığı önem ve sınırlı öznitelik vektörü ile yüksek sınıflandırma oranlarına ulaşmasında karşılaşılan zorluk nedeniyle popüler bir araştırma konusudur. Bireylerin hareket ölçülebilirliginin akıllı telefonların içinde gömülü bulunan atalet ölçüm birimleri sayesinde artması ile birlikte, bu alanda toplanan veri miktarı artmakta ve daha başarılı sınıflandırıcıların tasarlanabilmesine imkan saglanmaktadır. Yapay sinir ağları, konvansiyonel sınıflandırıcılara göre sınıflandırma sorunlarında daha iyi performans sergileyebilmektedir. Bu çalışmada, Irvine Kaliforniya Üniversitesi (UCI) veri setine yapay sinir ağı temelli bir sınıflandırıcı önermek için çeşitli yapay sinir ağı yapıları denenmiş olup, bu sınıflandırıcılar ile elde edilen başarı oranları literatürdeki aynı veri kümesi için bulunan sonuçlarla karşılaştırılmıştır.
Open Access
Human activity recognition using inertial/magnetic sensor units
(Springer, Berlin, Heidelberg, 2010) Altun, Kerem; Barshan, Billur
This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.
Open Access
Human activity recognition using tag-based localization
(IEEE, 2012-04) Yurtman, Aras; Barshan, Barshan
This paper provides a comparative study on the different techniques of classifying human activities using a tag-based radio-frequency (RF) localization system. Non-uniformly-sampled data containing position measurements of the tags on the body is first converted to a uniformly-sampled one using different curve-fitting algorithms. Then, the data is partitioned into segments. Finally, various classification techniques are applied to classify human activities. Curve-fitting, segmentation, and classification methods are compared using different cross-validation techniques and the combination resulting in the best performance is presented. The results indicate that the system demonstrates acceptable performance despite the fact that tag-based RF localization is not very accurate.
Open Access
Human activity recognition using tag-based radio frequency localization
(Taylor and Francis Inc., 2016) Yurtman, A.; Barshan, B.
This article provides a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization. A publicly available dataset is used where the position data of multiple RF tags worn on different parts of the human body are acquired asynchronously and nonuniformly. In this study, curves fitted to the data are resampled uniformly and then segmented. We investigate the effect on system accuracy of varying the relevant system parameters. We compare various curve-fitting, segmentation, and classification techniques and present the combination resulting in the best performance. The classifiers are validated using 5-fold and subject-based leave-one-out cross validation, and for the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30%, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%, respectively. The results indicate that the system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements.
Open Access
Inter- and intra-subject variations in activity recognition using inertial sensors and magnetometers
(2012-02) Yurtman, Aras; Barshan, Billur
Open Access
Karşılıklı bilgi ölçütü kullanılarak giyilebilir hareket duyucu sinyallerinin aktivite tanıma amaçlı analizi
(IEEE, 2014-04) Dobrucalı, Oğuzcan; Barshan, Billur
Giyilebilir hareket duyucuları ile insan aktivitelerinin saptanmasında, uygun duyucu yapılanışının seçimi önem taşıyan bir konudur. Bu konu, kullanılacak duyucuların sayısının, türünün, sabitlenecekleri konum ve yönelimin belirlenmesi problemlerini içermektedir. Literatürde konuyla ilgili önceki çalışmalarda araştırmacılar, kendi seçtikleri duyucu yapılanışları ile diğer olası duyucu yapılanışlarını, söz konusu yapılanışlar ile insan aktivitelerini ayırt etme başarımlarına göre karşılaştırmışlardır. Ancak, söz konusu ayırt etme başarımlarının, kullanılan öznitelikler ve sınıflandırıcılara bağlı olduğu yadsınamaz. Bu çalışmada karşılıklı bilgi ölçütü kullanılarak duyucu yapılanışları, duyuculardan kaydedilen ham ölçümlerin zaman uzayındaki dağılımlarına göre belirlenmektedir. Bedenin farklı noktalarında bulunan ivmeölçer, dönüölçer ve manyetometrelerin ölçüm eksenleri arasından, gerçekleştirilen insan aktiviteleri hakkında en çok bilgi sağlayanları saptanmıştır.
Open Access
A memory efficient novel deep learning architecture enabling diverse feature extraction on wearable motion sensor data
(2022-09) Koşar, Enes
Extracting representative features to recognize human activities through the use of wearables is an area of on-going research. We propose a novel hybrid net-work architecture to recognize human activities through the use of wearable motion sensors and deep learning techniques. The long short-term memory (LSTM) and the 2D convolutional neural network (CNN) branches of the model that run in parallel receive the raw signals and their spectrograms, respectively. We compare the classification performance of the proposed network with five commonly used network architectures: 1D CNN, 2D CNN, LSTM, standard 1D CNN-LSTM, and an alternative 1D CNN-LSTM model. We tune the hyper-parameters of all six models using Bayesian optimization and test the models on two publicly available datasets. The proposed 2D CNN-LSTM architecture achieves the highest aver-age accuracies of 95.66% and 92.95% on the two datasets, which are, respectively, 2.45% and 3.18% above those of the 2D CNN model that ranks the second. User identification is another problem that we have addressed in this thesis. Firstly, we use binary classifier models to detect activity signals that are useful for the user identity recognition task. Useful signals are transmitted to the next module and used by the proposed deep learning model for user identity recognition. Moreover, we investigate feature transfer between the human activity and user identity recognition tasks which enables shortening the training processes by 8.7 to 17 times without a significant degradation in classification accuracies. Finally, we elaborate on reducing the model sizes of the proposed models for human activity and user identity recognition problems. By using transfer learning, pooling layers, and eight-bit weight quantization methods, we have reduced the model sizes by 17–116 times without a significant degradation in classification accuracies.
Open Access
Multiple view human activity recognition
(2012) Pehlivan, Selen
This thesis explores the human activity recognition problem when multiple views are available. We follow two main directions: we first present a system that performs volume matching using constructed 3D volumes from calibrated cameras, then we present a flexible system based on frame matching directly using multiple views. We examine the multiple view systems compared to single view systems, and measure the performance improvements in recognition using more views by various experiments. Initial part of the thesis introduces compact representations for volumetric data gained through reconstruction. The video frames recorded by many cameras with significant overlap are fused by reconstruction, and the reconstructed volumes are used as substitutes of action poses. We propose new pose descriptors over these three dimensional volumes. Our first descriptor is based on the histogram of oriented cylinders in various sizes and orientations. We then propose another descriptor which is view-independent, and which does not require pose alignment. We show the importance of discriminative pose representations within simpler activity classification schemes. Activity recognition framework based on volume matching presents promising results compared to the state-of-the-art. Volume reconstruction is one natural approach for multi camera data fusion, but there can be few cameras with overlapping views. In the second part of the thesis, we introduce an architecture that is adaptable to various number of cameras and features. The system collects and fuses activity judgments from cameras using a voting scheme. The architecture requires no camera calibration. Performance generally improves when there are more cameras and more features; training and test cameras do not need to overlap; camera drop in or drop out is handled easily with little penalty. Experiments support the performance penalties, and advantages for using multiple views versus single view.
Open Access
Pedestrian dead reckoning employing simultaneous activity recognition cues
(Institute of Physics Publishing, 2012-01-11) Altun, K.; Barshan, B.
We consider the human localization problem using body-worn inertial/magnetic sensor units. Inertial sensors are characterized by a drift error caused by the integration of their rate output to obtain position information. Because of this drift, the position and orientation data obtained from inertial sensors are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user can provide information about his position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing localization simultaneously with activity recognition, we detect the activity context switches and use the corresponding position information as position updates in a localization filter. The localization filter also involves a smoother that combines the two estimates obtained by running the zero-velocity update algorithm both forward and backward in time. We performed experiments with eight subjects in indoor and outdoor environments involving walking, turning and standing activities. Using a spatial error criterion, we show that the position errors can be decreased by about 85% on the average. We also present the results of two 3D experiments performed in realistic indoor environments and demonstrate that it is possible to achieve over 90% error reduction in position by performing localization simultaneously with activity recognition.
Open Access
Sensor-activity relevance in human activity recognition with wearable motion sensors and mutual information criterion
(Springer, 2014) Dobrucalı Oğuzhan; Barshan, Billur
Selecting a suitable sensor configuration is an important aspect of recognizing human activities with wearable motion sensors. This problem encompasses selecting the number and type of the sensors, configuring them on the human body, and identifying the most informative sensor axes. In earlier work, researchers have used customized sensor configurations and compared their activity recognition rates with those of others. However, the results of these comparisons are dependent on the feature sets and the classifiers employed. In this study, we propose a novel approach that utilizes the time-domain distributions of the raw sensor measurements. We determine the most informative sensor types (among accelerometers, gyroscopes, and magnetometers), sensor locations (among torso, arms, and legs), and measurement axes (among three perpendicular coordinate axes at each sensor) based on the mutual information criterion.
Open Access
SiameseHAR: siamese-based model for human activity classification with FMCW radars
(Springer, 2023-06-03) Ege, Mert; Morgül, Ömer
Human Activity Recognition (HAR) is an attractive task in academic researchers. Furthermore, HAR is used in many areas such as security, sports activities, health, and entertainment. Frequency Modulated Continuous Wave (FMCW) radar data is a suitable option to classify human activities since it operates more robustly than a camera in difficult weather conditions such as fog and rain. Additionally, FMCW radars cost less than cameras. However, FMCW radars are less popular than camera-based HAR systems. This is mainly because the accuracy performance of FMCW radar data is lower than that of the camera when classifying human activation This article proposes the SiameseHAR model for the classification of human movement with FMCW radar data. In this model, we use LSTM and GRU blocks in parallel. In addition, we feed radar data operating at different frequencies (10 GHz, 24 GHz, 77 GHz) to the SiameseHAR model in parallel with the Siamese architecture. Therefore, the weights of the paths that use different radar data as inputs are tied. As far as we know, it is the first time that the multi-input Siamese architecture has been used for human activity classification. The SiameseHAR model we proposed is superior to most of the state-of-the-art models.