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A novel method to detect human falls in depth videos is presented in this paper. A fast and robust shape sequence descriptor, namely the Silhouette Orientation Volume (SOV), is used to represent actions and classify falls. The SOV descriptor provides high classification accuracy even with a combination of simple associated models, such as Bag-of-Words and the Naïve Bayes classifier. Experiments on the public SDU-Fall dataset show that this new approach achieves up to 91.89% fall detection accuracy with a single-view depth camera. The classification rate is about 5% higher than the results reported in the literature. An overall accuracy of 89.63% was obtained for the six-class action recognition, which is about 25% higher than the state of the art. Moreover, a perfect silhouette-based action recognition rate of 100% is achieved on the Weizmann action dataset. [ABSTRACT FROM AUTHOR]

Elderly people, who are living alone, are at great risk if a fall event occurred. Thus, automatic fall detection systems are in demand. Some of the early automatic fall detection systems such as wearable devices has a high cost and may cause inconvenience to the daily lives of the elderly people. In this paper, an improved depth-based fall detection system is presented. Our approach uses shape based fall characterization and a Support Vector Machines (SVM) classifier to classify falls from other daily actions. Shape based fall characterization is carried out with Curvature Scale Space (CSS) features and Fisher Vector (FV) encoding. FV encoding is used because it has several advantages against the Bag-of-Words (BoW) model. FV representation is robust and performs well even with simple linear classifiers. Extensive experiments on SDUFall dataset, which contains five daily activities and intentional falls from 20 subjects, show that encoding CSS features with FV encoding and a SVM classifier can achieve an up to 88.83% fall detection accuracy with a single depth camera. This classification rate is 2% more accurate than the compared approach. Moreover, an overall 64.67% accuracy is obtained for 6-class action recognition, which is about 10% more accurate than the compared approach. [ABSTRACT FROM AUTHOR]

Today, depending on the increasing population and technological developments, household appliances have significantly increased. This situation causes an ever-increasing energy demand, especially in modern societies. However, efficient energy use is also critical due to limited energy resources. With the Non-intrusive load monitoring methods, the usage of home appliances can be controlled, and behaviours can be adjusted for the energy saving of users. The control of home appliances is only possible with the effective detection of these appliances. In this study, a new hybrid model including a sliding window approach, long short-term memory (LSTM) network, and classifier is proposed to detect house appliances. Unlike traditional feature extraction, the proposed model uses time-series features to determine device features. For this purpose, each time series is divided into windows that do not overlap with the sliding window approach. The mean, standard deviation, median, and multiscale entropy values are calculated from each window. These values are combined and applied to the LSTM network for deep feature extraction. It is applied to k nearest neighbor (k-NN), Ensemble Learning (EBT), and support vector machine (SVM) classifiers to detect residential appliances from deep features retrieved from the LSTM network. The proposed model's effectiveness has been tested with the high-resolution profiles of the residential appliances dataset. In experimental studies, residential appliances were detected in LSTM based k-NN, EBT, and SVM classifiers with an accuracy of 98.25%, 97.81%, and 97.38%, respectively. [ABSTRACT FROM AUTHOR]

Recently, there has been an incremental interest on Environmental Sound Classification (ESC), which is an important topic of the non-speech audio classification task. A novel approach, which is based on deep Convolutional Neural Networks (CNN), is proposed in this study. The proposed approach covers a bunch of stages such as pre-processing, deep learning based feature extraction, feature concatenation, feature reduction and classification, respectively. In the first stage, the input sound signals are denoised and are converted into sound images by using the Sort Time Fourier Transform (STFT) method. After sound images are formed, pre-trained CNN models are used for deep feature extraction. In this stage, VGG16, VGG19 and DenseNet201 models are considered. The feature extraction is performed in a pyramidal fashion which makes the dimension of the feature vector quite large. For both dimension reduction and the determination of the most efficient features, a feature selection mechanism is considered after feature concatenation stage. In the last stage of the proposed method, a Support Vector Machines (SVM) classifier is used. The efficiency of the proposed method is calculated on various ESC datasets such as ESC 10, ESC 50 and UrbanSound8K, respectively. The experimental works show that the proposed method produced 94.8%, 81.4% and 78.14% accuracy scores for ESC-10, ESC-50 and UrbanSound8K datasets. The obtained results are also compared with the state-of-the art methods achievements. [ABSTRACT FROM AUTHOR]