Browsing by Subject "Wearables"

Now showing 1 - 4 of 4

Embargo
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
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
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.