D. Litvak

Bio: D. Litvak is an academic researcher from Tel Aviv University. The author has an hindex of 2, co-authored 2 publications receiving 318 citations.

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%.

Detection of falls at home using floor vibrations and sound

Abstract: Falls are very prevalent among the elderly especially in their home. Approximately one in every three adults 65 years old or older falls each year, 30% of those falls result in serious injuries and more than 70% of the event are at home. 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 this paper we present an innovative automatic system for detection of elderly people falls at home. The system is based on floor vibration and acoustic sensing, and uses pattern recognition algorithm to discriminate between human fall events and other events. The proposed solution is unique, inexpensive, and does not require the person to wear anything. Using the proposed system we can detect human falls with a sensitivity of 97.5% and specificity of 98.5%.

Fall Detection in Homes of Older Adults Using the Microsoft Kinect

Abstract: A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.

Evaluation of accelerometer-based fall detection algorithms on real-world falls.

Abstract: Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elders Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls Many different approaches have been explored to automatically detect a fall using inertial sensors Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project The aim of the present study is to bechmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world fall To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls We found that the SP average of the thirteen algorithms, was (mean +/- std) 830%+/- 303% (maximum value = 98%) The SE was considerably lower (SE = 570%+/- 273%, maximum value = 828%), much lower than the values obtained on simulated falls The number of false alarms generated by the algorithms during 1-day monitoring of there representative fallers ranged from 3 to 85 The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector

A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

Abstract: We propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain postprocessing. Information from ellipse fitting and a projection histogram along the axes of the ellipse is used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

A Microphone Array System for Automatic Fall Detection

Abstract: More than a third of elderly fall each year in the United States. It has been shown that the longer the lie on the floor, the poorer is the outcome of the medical intervention. To reduce delay of the medical intervention, we have developed an acoustic fall detection system (acoustic-FADE) that automatically detects a fall and reports it promptly to the caregiver. Acoustic-FADE consists of a circular microphone array that captures the sounds in a room. When a sound is detected, acoustic-FADE locates the source, enhances the signal, and classifies it as “fall” or “nonfall.” The sound source is located using the steered response power with phase transform technique, which has been shown to be robust under noisy environments and resilient to reverberation effects. Signal enhancement is performed by the beamforming technique based on the estimated sound source location. Height information is used to increase the specificity. The mel-frequency cepstral coefficient features computed from the enhanced signal are utilized in the classification process. We have evaluated the performance of acoustic-FADE using simulated fall and nonfall sounds performed by three stunt actors trained to behave like elderly under different environmental conditions. Using a dataset consisting of 120 falls and 120 nonfalls, the acoustic-FADE achieves 100% sensitivity at a specificity of 97%.