42,227 research outputs found
Multi-sensor based ambient assisted living system
Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 76-84.An important goal of Ambient Assisted Living (AAL) research is to contribute
to the quality of life of the elderly and handicapped people and help them to
maintain an independent lifestyle with the use of sensors, signal processing and
the available telecommunications infrastructure. From this perspective, detection
of unusual human activities such as falling person detection has practical applications.
In this thesis, a low-cost AAL system using vibration and passive infrared
(PIR) sensors is proposed for falling person detection, human footstep detection,
human motion detection, unusual inactivity detection, and indoor flooding
detection applications. For the vibration sensor signal processing, various frequency
analysis methods which consist of the discrete Fourier transform (DFT),
mel-frequency cepstral coefficients (MFCC), discrete wavelet transform (DWT)
with different filter-banks, dual-tree complex wavelet transform (DT-CWT), and
single-tree complex wavelet transform (ST-CWT) are compared to each other to
obtain the best possible classification result in our dataset. Adaptive-threshold
based Markov model (MM) classifier is preferred for the human footstep detection.
Vibration sensor based falling person detection system employs Euclidean
distance and support vector machine (SVM) classifiers and these classifiers are
compared to each other. PIR sensors are also used for falling person detection
and this system employs two PIR sensors. To achieve the most reliable system, a
multi-sensor based falling person detection system which employs one vibration
and two PIR sensors is developed. PIR sensor based system has also the capability
of detecting uncontrolled flames and this system is integrated to the overall
system. The proposed AAL system works in real-time on a standard personal
computer or chipKIT Uno32 microprocessors without computers. A network is
setup for the communication of the Uno32 boards which are connected to different
sensors. The main processor gives final decisions and emergency alarms
are transmitted to outside of the smart home using the auto-dial alarm system via telephone lines. The resulting AAL system is a low-cost and privacy-friendly
system thanks to the types of sensors used.Yazar, AhmetM.S
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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