Browsing by Subject "Wearable sensors"

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A new CNN-LSTM architecture for activity recognition employing wearable motion sensor data: enabling diverse feature extraction
(Elsevier, 2023-06-28) Koşar, Enes; Barshan, Billur
Extracting representative features to recognize human activities through the use of wearables is an area of on-going research. While hand-crafted features and machine learning (ML) techniques have been sufficiently well investigated in the past, the use of deep learning (DL) techniques is the current trend. Specifically, Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), and hybrid models have been investigated. We propose a novel hybrid network architecture to recognize human activities through the use of wearable motion sensors and DL techniques. The LSTM and the 2D CNN branches of the model that run in parallel receive the raw signals and their spectrograms, respectively. We concatenate the features extracted at each branch and use them for activity recognition. We compare the classification performance of the proposed network with three single and three hybrid commonly used network architectures: 1D CNN, 2D CNN, LSTM, standard 1D CNN-LSTM, 1D CNN-LSTM proposed by Ordóñez and Roggen, and an alternative 1D CNN-LSTM model. We tune the hyper-parameters of six of the models using Bayesian optimization and test the models on two publicly available datasets. The comparison between the seven networks is based on four performance metrics and complexity measures. Because of the stochastic nature of DL algorithms, we provide the average values and standard deviations of the performance metrics over ten repetitions of each experiment. The proposed 2D CNN-LSTM architecture achieves the highest average 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. This improvement is a consequence of the proposed model enabling the extraction of a broader range of complementary features that comprehensively represent human activities. We evaluate the complexities of the networks in terms of the total number of parameters, model size, training/testing time, and the number of floating point operations (FLOPs). We also compare the results of the proposed network with those of recent related work that use the same datasets.
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.
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
Classification of fall directions via wearable motion sensors
(Academic Press, 2022-06-15) Turan, M. Ş.; Barshan, Billur
Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of academic work on this class of systems, not much effort has been devoted to the investigation of effective and robust algorithms and like-for-like comparison of state-of-the-art algorithms using a sufficiently large dataset. In this article, fall-direction classification algorithms are presented and compared on an extensive dataset, comprising a total of 1600 fall trials. Eight machine learning classifiers are implemented for fall-direction classification into four basic directions (forward, backward, right, and left). These are, namely, Bayesian decision making (BDM), least squares method (LSM), k-nearest neighbor classifier (k-NN), artificial neural networks (ANNs), support vector machines (SVMs), decision-tree classifier (DTC), random forest (RF), and adaptive boosting or AdaBoost (AB). BDM achieves perfect classification, followed by k-NN, SVM, and RF. Data acquired from only a single motion sensor unit, worn at the waist of the subject, are processed for experimental verification. Four of the classifiers (BDM, LSM, k-NN, and ANN) are modified to handle the presence of data from an unknown class and evaluated on the same dataset. In this robustness analysis, ANN and k-NN yield accuracies above 96.2%. The results obtained in this study are promising in developing real-world fall-classification systems as they enable fast and reliable classification of fall directions.
Open Access
Fall detection and classification using wearable motion sensors
(2017-08) Turan, Mustafa Şahin
Effective fall-detection systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is wearable sensor based fall-detection systems. While there is a vast amount of academic work on this class of systems, the literature still lacks effective and robust algorithms and comparative evaluation of state-of-the-art algorithms on a common basis, using an extensive dataset. In this thesis, falldetection and fall direction classification systems that use a motion sensor unit, worn at the waist of the subject, are presented. A comparison of a variety of falldetection algorithms on an extensive dataset, comprising a total of 2880 trials, is undertaken. A novel heuristic fall-detection algorithm (fuzzy-augmented double thresholding: FADoTh) using two simple features is proposed and compared to 15 state-of-the-art heuristic fall-detection algorithms, among which it displays the highest average accuracy (98:45%), sensitivity, and F-measure values. A learner version of the same algorithm (k-NN classifier-augmented tree: kAT) is developed and compared to eight machine learning (ML) classifiers based on the same dataset: Bayesian decision making (BDM), least squares method (LSM), k-nearest neighbor classifier (k-NN), artificial neural networks (ANN), support vector machines (SVM), decision tree classifier (DTC), random forest (RF), and adaptive boosting (AdaBoost). The kAT algorithm yields an average accuracy of 98:85% and performs on par with BDM, k-NN, ANN, SVM, DTC, RF, and AdaBoost, whereas LSM produces inferior results. Finally, the same eight ML classifiers are implemented for fall direction classification into four basic directions (forward, backward, right, and left) and evaluated on a reduced version of the same dataset consisting of only fall trials. BDM achieves perfect classification, followed by k-NN, SVM, and RF. BDM, LSM, k-NN, and ANN are modified to work in the presence of data from an unknown class and evaluated on the reduced dataset. In this robustness analysis, ANN and k-NN yield accuracies above 96:2%. The results obtained in this study are promising in developing real-world fall-detection systems.
Open Access
Investigating the Performance of Wearable Motion Sensors on recognizing falls and daily activities via machine learning
(Academic Press, 2022-06-30) Kavuncuoğlu, E.; Uzunhisarcıklı, E.; Barshan, Billur; Özdemir, A.T.
With sensor-based wearable technologies, high precision monitoring and recognition of human physical activities in real time is becoming more critical to support the daily living requirements of the elderly. The use of sensor technologies, including accelerometers (A), gyroscopes (G), and magnetometers (M) is mostly encountered in work focused on assistive technology, ambient intelligence, context-aware systems, gait and motion analysis, sports science, and fall detection. The classification performance of four sensor type combinations is investigated through the use of four machine learning algorithms: support vector machines (SVMs), Manhattan k-nearest neighbor classifier (M.k-NN), subspace linear discriminant analysis (SLDA), and ensemble bagged decision tree (EBDT). In this context, a large dataset containing 2520 tests performed by 14 volunteers containing 16 activities of daily living (ADLs) and 20 falls was employed. In binary (fall vs. ADL) and multi-class activity (36 activities) recognition, the highest classification accuracy rate was obtained by the SVM (99.96%) and M.k-NN (95.27%) classifiers, respectively, with the AM sensor type combination in both cases. We also made our dataset publicly available to lay the groundwork for new research.
Open Access
Investigation of sensor placement for accurate fall detection
(Springer, 2017) Ntanasis, P.; Pippa, E.; Özdemir, A. T.; Barshan, Billur; Megalooikonomou, V.
Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.
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
Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units
(Oxford University Press, 2014-11) Barshan, B.; Yüksek, M. C.
This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered. A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times. © 2013 © The British Computer Society 2013. All rights reserved.
Open Access
Thermal drawing of low-dimensional material-integrated triboelectric fibers for healthcare applications
(2024-07) Sadeque, Md Sazid Bin
The potential of flexible wearable devices and sensors to revolutionize healthcare lies in their ability to facilitate real-time monitoring. However, many of these wearable sensors are extensive energy consumers, and the requirement of bulky energy storage devices significantly hampers their acceptability. Currently available sensing devices mostly employ film-based devices, which lack breathability, reducing their applicability in widespread healthcare applications. Triboelectric nanogenerators (TENGs) are environmentally sustainable devices that convert mechanical and biomechanical energy into electrical output through the synergetic processes of triboelectrification and electrostatic induction. These devices effectively harvest low-frequency mechanical and biomechanical energy and enable self-powered sensing. TENG performance can be enhanced by incorporating low dimensional materials with high specific surface area into flexible ferroelectric polymers. Ferroelectric polyvinylidene fluoride (PVDF) and its copolymers are particularly advantageous due to their high dielectric constant and abundant highly electronegative fluorine ions. Various low dimensional materials can interact with the polar groups of PVDF and reorient them to conform to electroactive phases. Moreover, they can also form micro-capacitors and modulate the surface properties of nanocomposite. In this thesis, we aim to prepare a triboelectric nanogenerator integrated textile fiber with self-energy generating ability and breathability as textiles. We employed the thermal drawing process as a fabrication platform for preparing continuous triboelectric fibers. Graphene nanoplatelet (GNP) and Molybdenum disulfide (MoS2) are added to the PVDF matrix to improve triboelectric properties. β phases of thermally drawn nanocomposite fibers demonstrate significant improvement and were increased to 37.6%, 39.5%, and 43.3% for 1, 3, 5% GNP integration. For the case of MoS2, β phase increases to 47.5% for 3 wt%MoS2; however, β phase decreases beyond 3 wt%. The nanocomposite TENG fibers demonstrate improved triboelectric properties. The fibers show superior sensitivity, flexibility and durability, enabling their applications in critical healthcare applications.