Browsing by Subject "Inertial sensors"
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Item Open Access A novel heuristic fall-detection algorithm based on double thresholding, fuzzy logic, and wearable motion sensor data(Institute of Electrical and Electronics Engineers, 2023-05-25) Barshan, Billur; Turan, M. S.We present a novel heuristic fall-detection algorithm based on combining double thresholding of two simple features with fuzzy logic techniques. We extract the features from the acceleration and gyroscopic data recorded from a waist-worn motion sensor unit. We compare the proposed algorithm to 15 state-of-the-art heuristic fall-detection algorithms in terms of five performance metrics and runtime on a vast benchmarking fall data set that is publicly available. The data set comprises recordings from 2880 short experiments (1600 fall and 1280 non-fall trials) with 16 participants. The proposed algorithm exhibits superior average accuracy (98.45%), sensitivity (98.31%), and F-measure (98.59%) performance metrics with a runtime that allows real-time operation. Besides proposing a novel heuristic fall-detection algorithm, this work has comparative value in that it provides a fair comparison on the relative performances of a considerably large number of existing heuristic algorithms with the proposed one, based on the same data set. The results of this research are encouraging in the development of fall-detection systems that can function in the real world for reliable and rapid fall detection.Item Open Access Activity recognition invariant to position and orientation of wearable motion sensor units(2019-04) Yurtman, ArasWe 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.Item 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.Item Open Access Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals(Elsevier Ireland Ltd., 2014) Yurtman, A.; Barshan, B.We develop an autonomous system to detect and evaluate physical therapy exercises using wearable motion sensors. We propose the multi-template multi-match dynamic time warping (MTMM-DTW) algorithm as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session. While allowing some distortion (warping) in time, the algorithm provides a quantitative measure of similarity between an exercise execution and previously recorded templates, based on DTW distance. It can detect and classify the exercise types, and count and evaluate the exercises as correctly/incorrectly performed, identifying the error type, if any. To evaluate the algorithm's performance, we record a data set consisting of one reference template and 10 test executions of three execution types of eight exercises performed by five subjects. We thus record a total of 120 and 1200 exercise executions in the reference and test sets, respectively. The test sequences also contain idle time intervals. The accuracy of the proposed algorithm is 93.46% for exercise classification only and 88.65% for simultaneous exercise and execution type classification. The algorithm misses 8.58% of the exercise executions and demonstrates a false alarm rate of 4.91%, caused by some idle time intervals being incorrectly recognized as exercise executions. To test the robustness of the system to unknown exercises, we employ leave-one-exercise-out cross validation. This results in a false alarm rate lower than 1%, demonstrating the robustness of the system to unknown movements. The proposed system can be used for assessing the effectiveness of a physical therapy session and for providing feedback to the patient. © 2014 Elsevier Ireland Ltd.Item Open Access Classifying daily and sports activities invariantly to the positioning of wearable motion sensor units(IEEE, 2020) Barshan, Billur; Yurtman, ArasWe propose techniques that achieve invariance to the positioning of wearable motion sensor units on the body for the recognition of daily and sports activities. Using two sequence sets based on the sensory data allows each unit to be placed at any position on a given rigid body part. As the unit is shifted from its ideal position with larger displacements, the activity recognition accuracy of the system that uses these sequence sets degrades slowly, whereas that of the reference system (which is not designed to achieve position invariance) drops very fast. Thus, we observe a tradeoff between the flexibility in sensor unit positioning and the classification accuracy. The reduction in the accuracy is at acceptable levels, considering the convenience and flexibility provided to the user in the placement of the units. We compare the proposed approach with an existing technique to achieve position invariance and combine the former with our earlier methodology to achieve orientation invariance. We evaluate our proposed methodology on a publicly available data set of daily and sports activities acquired by wearable motion sensor units. The proposed representations can be integrated into the preprocessing stage of existing wearable systems without significant effort.Item Open Access Classifying human leg motions with uniaxial piezoelectric gyroscopes(2009) Tunçel O.; Altun, K.; Barshan, B.This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost. © 2009 by the authors.Item Open Access Comparative study on classifying human activities with miniature inertial and magnetic sensors(Elsevier, 2010) Altun, K.; Barshan, B.; Tunçel, O.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), a rule-based algorithm (RBA) or decision tree, 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). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.Item Open Access Detection and evaluation of physical therapy exercises by dynamic time warping using wearable motion sensor units(Springer, 2014) Yurtman, Aras; Barshan, BillurWe develop an autonomous system that detects and evaluates physical therapy exercises using wearable motion sensors. We propose an algorithm that detects all the occurrences of one or more template signals (representing exercise movements) in a long signal acquired during a physical therapy session. In matching the signals, the algorithm allows some distortion in time, based on dynamic time warping (DTW). The algorithm classifies the executions in one of the exercises and evaluates them as correct/incorrect, giving the error type if there is any. It also provides a quantitative measure of similarity between each matched execution and its template. To evaluate the performance of the algorithm in physical therapy, a dataset consisting of one template execution and ten test executions of each of the three execution types of eight exercises performed by five subjects is recorded, having a total of 120 and 1,200 exercise executions in the training and test sets, respectively, as well as many idle time intervals in the test signals. The proposed algorithm detects 1,125 executions in the whole test set. 8.58 % of the 1,200 executions are missed and 4.91 % of the idle time intervals are incorrectly detected as executions. The accuracy is 93.46 % only for exercise classification and 88.65 % for simultaneous exercise and execution type classification. The proposed system may be used for both estimating the intensity of the physical therapy session and evaluating the executions to provide feedback to the patient and the specialist.Item Open Access Deterministic and stochastic error modeling of inertial sensors and magnetometers(2012) Seçer, GörkemThis thesis focuses on the deterministic and stochastic modeling and model parameter estimation of two commonly employed inertial measurement units. Each unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. In the first part of the thesis, deterministic modeling and calibration of the units are performed, based on real test data acquired from a flight motion simulator. The deterministic modeling and identification of accelerometers is performed based on a traditional model. A novel technique is proposed for the deterministic modeling of the gyroscopes, relaxing the test bed requirement and enabling their in-use calibration. This is followed by the presentation of a new sensor measurement model for magnetometers that improves the calibration error by modeling the orientation-dependent magnetic disturbances in a gimbaled angular position control machine. Model-based Levenberg-Marquardt and modelfree evolutionary optimization algorithms are adopted to estimate the calibration parameters of sensors. In the second part of the thesis, stochastic error modeling of the two inertial sensor units is addressed. Maximum likelihood estimation is employed for estimating the parameters of the different noise components of the sensors, after the dominant noise components are identified. Evolutionary and gradient-based optimization algorithms are implemented to maximize the likelihood function, namely particle swarm optimization and gradient-ascent optimization. The performance of the proposed algorithm is verified through experiments and the results are compared to the classical Allan variance technique. The results obtained with the proposed approach have higher accuracy and require a smaller sample data size, resulting in calibration experiments of shorter duration. Finally, the two sensor units are compared in terms of repeatability, present measurement noise, and unaided navigation performance.Item Open Access Fizik tedavi egzersizlerinin giyilebilir hareket algılayıcıları işaretlerinden dinamik zaman bükmesiyle sezimi ve değerlendirilmesi(IEEE, 2014-04) Yurtman, Aras; Barshan, BillurGiyilebilir hareket algılayıcılarından kaydedilen sinyalleri işleyerek fizik tedavi egzersizlerini algılamak ve değerlendirmek için özerk bir sistem geliştirilmiştir. Bir fizik tedavi seansındaki bir ya da birden fazla egzersiz tipini algılamak için, temeli dinamik zaman bükmesi (DZB) benzeşmezlik ölçütüne dayanan bir algoritma geliştirilmiştir. Algoritma, egzersizlerin doğru ya da yanlış yapıldığını değerlendirmekte ve varsa hata türünü saptamaktadır. Algoritmanın başarımını degerlendirmek için, beş katılımcı tarafından yapılan sekiz egzersiz hareketinin üç yürütüm türü için birer şablon ve 10’ar sınama yürütümünden oluşan bir veri kümesi kaydedilmiştir. Dolayısıyla, eğitim ve sınama kümelerinde sırasıyla 120 ve 1,200 egzersiz yürütümü bulunmaktadır. Sınama kümesi, boş zaman dilimleri de içermektedir. Öne sürülen algoritma, sınama kümesindeki 1,200 yürütümün % 8.58’ini kaçırmakta ve boş zaman dilimlerinin % 4.91’ini yanlış sezim olarak değerlendirerek toplam 1,125 yürütüm algılamaktadır. Doğruluk, sadece egzersiz sınıflandırması ele alındığında ˘ % 93.46, hem egzersiz hem de yürütüm türü sınıflandırması içinse % 88.65’tir. Sistemin bilinmeyen egzersizlere karşı davranışını sınamak için, algoritma, her egzersiz için, o egzersizin şablonları dışarıda bırakılarak çalıştırılmış ve 1,200 egzersizin sadece 10’u yanlış sezilmiştir. Bu sonuç, sistemin bilinmeyen hareketlere karşı gürbüz olduğunu göstermektedir. Öne sürülen sistem, hem bir fizik tedavi seansının yoğunluğunu kestirmek, hem de hastaya ve fizik tedavi uzmanına geribildirim vermek amacıyla egzersiz hareketlerini değerlendirmek için kullanılabilir.Item Open Access Improvements in deterministic error modeling and calibration of inertial sensors and magnetometers(Elsevier B.V., 2016) Secer, G.; Barshan, B.We consider the deterministic modeling, calibration, and model parameter estimation of two commonly employed inertial measurement units based on real test data acquired from a flight motion simulator. Each unit comprises three tri-axial devices: an accelerometer, a gyroscope, and a magnetometer. We perform the deterministic error modeling and calibration of accelerometers based on an improved measurement model, and the technique we propose for gyroscopes lowers costs by eliminating the need for additional sensors and relaxing the test bed requirement. We present an extended measurement model for magnetometers that reduces calibration errors by modeling orientation-dependent hard-iron errors in a gimbaled angular position-control machine. While we employ the model-based Levenberg-Marquardt optimization algorithm for the parameter estimation of accelerometers and magnetometers, we use a model-free evolutionary optimization algorithm (particle swarm optimization) for estimating the calibration parameters of gyroscopes. Errors are considerably reduced as a result of proper modeling and calibration. © 2016 Elsevier B.V.Item Open Access Investigating inter-subject and inter-activity variations in activity recognition using wearable motion sensors(Oxford University Press, 2016) Barshan, B.; Yurtman, A.This work investigates inter-subject and inter-activity variability of a given activity dataset and provides some new definitions to quantify such variability. The definitions are sufficiently general and can be applied to a broad class of datasets that involve time sequences or features acquired using wearable sensors. The study is motivated by contradictory statements in the literature on the need for user-specific training in activity recognition. We employ our publicly available dataset that contains 19 daily and sports activities acquired from eight participants who wear five motion sensor units each. We pre-process recorded activity time sequences in three different ways and employ absolute, Euclidean and dynamic time warping distance measures to quantify the similarity of the recorded signal patterns. We define and calculate the average inter-subject and inter-activity distances with various methods based on the raw and pre-processed time-domain data as well as on the raw and pre-processed feature vectors. These definitions allow us to identify the subject who performs the activities in the most representative way and pinpoint the activities that show more variation among the subjects. We observe that the type of pre-processing used affects the results of the comparisons but that the different distance measures do not alter the comparison results as much. We check the consistency of our analysis and results by highlighting some of our activity recognition rates based on an exhaustive set of sensor unit, sensor type and subject combinations. We expect the results to be useful for dynamic sensor unit/type selection, for deciding whether to perform user-specific training and for designing more effective classifiers in activity recognition.Item Open Access Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals(2011) Ayrulu-Erdem, B.; Barshan, B.We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction. © 2011 by the authors; licensee MDPI, Basel, Switzerland.Item Open Access Novel noniterative orientation estimation for wearable motion sensor units acquiring accelerometer, gyroscope, and magnetometer measurements(IEEE, 2020) Yurtman, Aras; Barshan, BillurWe propose a novel noniterative orientation estimation method based on the physical and geometrical properties of the acceleration, angular rate, and magnetic field vectors to estimate the orientation of motion sensor units. The proposed algorithm aims that the vertical (up) axis of the earth coordinate frame is as close as possible to the measured acceleration vector and that the north axis of the earth makes an angle with the detected magnetic field vector as close as possible to the estimated value of the magnetic dip angle. We obtain the sensor unit orientation based on the rotational quaternion transformation between the earth and the sensor unit frames. We evaluate the proposed method by incorporating it into an activity recognition scheme for daily and sports activities, which requires accurately estimated sensor unit orientations to achieve invariance to the orientations at which the units are worn on the body. Using four different classifiers on a publicly available data set, the proposed methodology achieves an average activity recognition accuracy higher than the state-of-the-art methods, as well as being computationally efficient enough to be executed in real time.Item Open Access Position invariance for wearables: interchangeability and single-unit usage via machine learning(IEEE, 2021) Yurtman, Aras; Barshan, Billur; Redif, S.We propose a new methodology to attain invariance to the positioning of body-worn motion-sensor units for recognizing everyday and sports activities. We first consider random interchangeability of the sensor units so that the user does not need to distinguish between them before wearing. To this end, we propose to use the compact singular value decomposition (SVD) that significantly reduces the accuracy degradation caused by random interchanging of the units. Secondly, we employ three variants of a generalized classifier that requires wearing only a single sensor unit on any one of the body parts to classify the activities. We combine both approaches with our previously developed methods to achieve invariance to both position and orientation, which ultimately allows the user significant flexibility in sensor-unit placement (position and orientation). We assess the performance of our proposed approach on a publicly available activity dataset recorded by body-worn motion-sensor units. Experimental results suggest that there is a tolerable reduction in accuracy, which is justified by the significant flexibility and convenience offered to users when placing the units.