Showing papers by "Yaniv Zigel published in 2009"

A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls

Abstract: Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: ldquoRescue Randy.rdquo The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.

System for automatic fall detection for elderly people

Abstract: Apparatus for detection of human falls, comprises: an acceleration detector, for detecting vibration events, typically placed on a floor, a microphone, located in association with the acceleration detector for detection of corresponding sound events, and a classification unit to classify concurrent events from the microphone and the acceleration detector, thereby to determine whether a human fall is indicated. If the event appears to be a human fall, then an alarm is raised.

Dimension reduction approaches for SVM based speaker age estimation.

Abstract: This paper presents two novel dimension reduction approaches applied on the gaussian mixture model (GMM) supervectors to improve age estimation speed and accuracy. The GMM supervector embodies many speech characteristics irrelevant to age estimation and like noise, they are harmful to the system’s generalization ability. In addition, the support vectors machine (SVM) testing computation grows with the vector’s dimension, especially when using complex kernels. The first approach presented is the weighted-pairwise principal components analysis (WPPCA) that reduces the vector dimension by minimizing the redundant variability. The second approach is based on anchor-models, using a novel anchors selection method. Experiments showed that dimension reduction makes the testing process 5 times faster and using the WPPCA approach, it is also 5% more accurate.

Feature selection for room volume identification from room impulse response

Abstract: The room impulse response (RIR) can be used to calculate many room acoustical parameters, such as the reverberation time (RT). However, estimating the room volume, another important room parameter, from the RIR is typically a more difficult task requiring extraction of other features from the RIR. Most of the existing fully-blind methods for estimating the room volume from the RIR do not combine features from different feature sets. This can be one reason to the fact that these methods are sensitive to differences in source-to-receiver distance and wall reflection coefficients. We propose a new approach in which hypothetical-volume room models are trained with room volume features from different feature sets. Estimation is performed by identifying the hypothesis with maximum-likelihood (ML) using background model normalization. The different feature sets are compared using equal error rate (EER) of hypothesis verification. A combination of features from the different feature sets is selected so that minimum EER is achieved. Using the selected features, we achieve average detection rate of 98.8% with a standard deviation (STD) of 1.5% for eight rooms with different volumes, source-to-receiver distances, and wall reflection coefficients.

A new method for atrial electrical activity analysis from surface ECG signals using an energy ratio measure

Abstract: A new method called separation using maximum energy ratio (SUMER) is introduced. Using 12 lead ECG signals, SUMER tries to separate the atrial activity from the ventricular activity. Relying on the assumption that the atrial activity can be reconstructed from a linear combination of 12 lead ECG signals, SUMER looks for the combination that will give us the best representation of the atrial activity. A cost function that is the energy ratio between different segments in the ECG signal is created. Forcing the linear combination to find the maximum possible cost function gives us the desired combination. The method presents better separation performances than the ICA method, and provides tools for the next phase of P wave detection.

Age verification using a hybrid speech processing approach.

Abstract: The human speech production system is a multi-level system. On the upper level, it starts with information that one wants to transmit. It ends on the lower level with the materialization of the information into a speech signal. Most of the recent work conducted in age estimation is focused on the lower-acoustic level. In this research the upper lexical level information is utilized for age-group verification and it is shown that one's vocabulary reflects one's age. Several age-group verification systems that are based on automatic transcripts are proposed. In addition, a hybrid approach is introduced, an approach that combines the word-based system and an acoustic-based system. Experiments were conducted on a four age-groups verification task using the Fisher corpora, where an average equal error rate (EER) of 28.7% was achieved using the lexical-based approach and 28.0% using an acoustic approach. By merging these two approaches the verification error was reduced to 24.1%.

Information bottleneck based age verification.

Abstract: Word N-gram models can be used for word-based age-group verification. In this paper the Agglomerative Information Bottleneck (AIB) approach is used to tackle one of the most fundamental drawbacks of word N-gram models: its abundant amount of irrelevant information. It is demonstrated that irrelevant information can be omitted by joining words to form word-clusters; this provides a mechanism to transform any sequence of words to a sequence of word-cluster labels. Consequently, word N-gram models are converted to wordcluster N-gram models which are more compact. Age verification experiments were conducted on the Fisher corpora. Their goal was to verify the age-group of the speaker of an unknown speech segment. In these experiments an Ngram model was compressed to a fifth of its original size without reducing the verification performance. In addition, a verification accuracy improvement is demonstrated by disposing irrelevant information.