A Smart Sensor to Detect the Falls of the Elderly.

Slides:



Advertisements
Similar presentations
Technology Enabled High-Touch Care Majd Alwan, Ph.D. Medical Automation Research Center University of Virginia Improving healthcare quality and efficiency.
Advertisements

DS-01 Disaster Risk Reduction and Early Warning Definition
V-1 Part V: Collaborative Signal Processing Akbar Sayeed.
Robot Sensor Networks. Introduction For the current sensor network the topography and stability of the environment is uncertain and of course time is.
Technology Solutions. There are a variety of technologies—old and new—that have been developed to warn drivers and operators when workers on foot are.
Shweta Jain 1. Motivation ProMOTE Introduction Architectural Choices Pro-MOTE system Architecture Experimentation Conclusion and Future Work Acknowledgement.
Sponsored by the U.S. Department of Defense © 2005 by Carnegie Mellon University 1 Pittsburgh, PA Dennis Smith, David Carney and Ed Morris DEAS.
Detecting Computer Intrusions Using Behavioral Biometrics Ahmed Awad E. A, and Issa Traore University of Victoria PST’05 Oct 13,2005.
AuRA: Principles and Practice in Review
A Cloud-Assisted Design for Autonomous Driving Swarun Kumar Shyamnath Gollakota and Dina Katabi.
Perception and Communications for Vulnerable Road Users safety Pierre Merdrignac Supervisors: Fawzi Nashashibi, Evangeline Pollard, Oyunchimeg Shagdar.
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
A Robotic Wheelchair for Crowded Public Environments Choi Jung-Yi EE887 Special Topics in Robotics Paper Review E. Prassler, J. Scholz, and.
Overview of Computer Vision CS491E/791E. What is Computer Vision? Deals with the development of the theoretical and algorithmic basis by which useful.
Detecting and Tracking Moving Objects for Video Surveillance Isaac Cohen and Gerard Medioni University of Southern California.
Model Building and Simulation Chapter 43 Research Methodologies.
Simultaneous Localization and Map Building System for Prototype Mars Rover CECS 398 Capstone Design I October 24, 2001.
seminar on Intrusion detection system
Jacinto C. Nascimento, Member, IEEE, and Jorge S. Marques
Motion detector ​ Bikesh Shrestha ​ Ari Rajamäki.
Computational Thinking Related Efforts. CS Principles – Big Ideas  Computing is a creative human activity that engenders innovation and promotes exploration.
PIR MOTION SENSOR Mohammed Muhid Ahmed S Checked By: Uzair Aakhoon.
Lift Me Up - CS4222 Group 9. Elderly Falls – How big is the problem?  About one third of the elder population over the age of 65 falls each year, and.
Architectural Design.
VIRTUAL PROTOTYPING of ROBOTS DYNAMICS E. Tarabanov.
Presented by: Chaitanya K. Sambhara Paper by: Maarten Ditzel, Caspar Lageweg, Johan Janssen, Arne Theil TNO Defence, Security and Safety, The Hague, The.
Intrusion Detection for Grid and Cloud Computing Author Kleber Vieira, Alexandre Schulter, Carlos Becker Westphall, and Carla Merkle Westphall Federal.
Overview of the Database Development Process
Mobile Distributed 3D Sensing Sandia National Laboratories Intelligent Sensors and Robotics POC: Chris Lewis
“Microbotics”. Introduction INSPIRED by the biology of a bee and the insect’s hive behavior... we aim to push advances in miniature robotics and the design.
SMUCSE 8394 BTS – Devices II Sensors Detection, Surveillance, Protection.
No: 1 CEMSIS 1 WP3 - Use of pre-developed products Key issues N. Thuy EDF R&D.
An approach to Intelligent Information Fusion in Sensor Saturated Urban Environments Charalampos Doulaverakis Centre for Research and Technology Hellas.
Fault Diagnosis System for Wireless Sensor Networks Praharshana Perera Supervisors: Luciana Moreira Sá de Souza Christian Decker.
PRESENTED BY: SAURAV SINGH.  INTRODUCTION  WINS SYSTEM ARCHITECTURE  WINS NODE ARCHITECTURE  WINS MICRO SENSORS  DISTRIBUTED SENSOR AT BORDER  WINS.
Sluzek 142/MAPLD Development of a Reconfigurable Sensor Network for Intrusion Detection Andrzej Sluzek & Palaniappan Annamalai Intelligent Systems.
Environment for Information Security n Distributed computing n Decentralization of IS function n Outsourcing.
Optimized M2M interworking with mobile networks Group Name: oneM2M REQ Source: Takanori Iwai, NEC, Meeting Date: Agenda.
© Jalal Kawash 2010 Introduction Peeking into Computer Science 1.
Effective Requirements Management – an overview Kristian Persson Field Product Manager, Telelogic Asia/Pacific.
I A I Infrared Security System and Method US Patent 7,738,008 June How Does It Work? June 2010 I A I = Infrared Applications Inc.
Tufts University School Of Engineering Tufts Wireless Laboratory TWL Direction Almir Davis 09/28/20091.
Team # 4 Jonathan Usher Ronny Polansky Sunil Patel.
JUNIOR RASHID SHAFEER CS 7 – 7509 YCET CONTENTS o EVOLUTION o OVERVIEW o PROMISES AND POTENTIAL o REALITY o ANALYSIS o CONCLUSION o REFERENCES.
Trends in Embedded Computing The Ubiquitous Computing through Sensor Swarms.
MACHINE VISION Machine Vision System Components ENT 273 Ms. HEMA C.R. Lecture 1.
1 Distribution Statement “A” (Approved for Public Release, Distribution Unlimited)5/15/2012 Advanced Radio Frequency Mapping (RadioMap) Dr. John Chapin.
PSEUDO-RELEVANCE FEEDBACK FOR MULTIMEDIA RETRIEVAL Seo Seok Jun.
Robust Object Tracking by Hierarchical Association of Detection Responses Present by fakewen.
An Architecture to Support Context-Aware Applications
Object Lesson: Discovering and Learning to Recognize Objects Object Lesson: Discovering and Learning to Recognize Objects – Paul Fitzpatrick – MIT CSAIL.
Adaptive Tracking in Distributed Wireless Sensor Networks Lizhi Yang, Chuan Feng, Jerzy W. Rozenblit, Haiyan Qiao The University of Arizona Electrical.
Intrusion Detection System
A Blackboard-Based Learning Intrusion Detection System: A New Approach
1 Architecture and Behavioral Model for Future Cognitive Heterogeneous Networks Advisor: Wei-Yeh Chen Student: Long-Chong Hung G. Chen, Y. Zhang, M. Song,
TELECARE Barry Fearon & Maddy Hill Community Commissioning Health and Community Services Hertfordshire County Council.
BORDER SECURITY USING WIRELESS INTEGRATED NETWORK SENSORS (WINS) By B.S.Indrani (07841A0406) Aurora’s Technological and Research Institute.
Border Security Using Wireless Integrated Network Sensors
SECURITY SYSTEM USING PIR. OVERVIEW  Introduction of Embedde system  Aim of the project  Current scenario  Limitations of Current scenario  Futurescope.
Presented by: Kumar Magi. ( 2MM07EC016 ). Contents Introduction Definition Sensor & Its Evolution Sensor Principle Multi Sensor Fusion & Integration Application.
CRESST ONR/NETC Meetings, July July, 2003 ONR Advanced Distributed Learning Bill Kaiser UCLA/SEAS Wireless Networked Sensors for Assessment.
Flame & Smoke Detection System Flame & Smoke Vision Detection is an intelligent vision-based analytics system which can timely detect suspicious fire or.
An E-Textiles. Virginia Tech e-Textiles Group Design of an e-textile computer architecture – Networking – Fault tolerance – Power aware – Programming.
Motion Sensors By Elva S. Agbon ICS 30 B MOTION SENSORS A sensor specifically designed to detect a gentle or sharp up and down or side to side motion.
TOUCHLESS TOUCHSCREEN USER INTERFACE
TOUCHLESS TOUCH SCREEN USER INTERFACE
EYE TRACKING TECHNOLOGY
Intelligent Transportation System
CONTENTS Introduction What is a biosensor ? Types Of Wearable Biosensors Applications Advantages Disadvantages Conclusion References Need of wearable.
Sensor Networks – Motes, Smart Spaces, and Beyond
Presentation transcript:

A Smart Sensor to Detect the Falls of the Elderly

2 introduction  Falls are a major health hazard for the elderly and a major obstacle to independent living  The estimated incidence of falls for both institutionalized and independent persons aged over 75 is at least 30 percent per year

3 introduction  The SIMBAD( Smart Inactivity Monitor using Array-Based Detectors) system ultimately aims to enhance the quality of life of the elderly, afford them a greater sense of security,and facilitate independent living

4 Justification for their approach  the current and emerging technologies have key limitations:  Simple sensors, such as single- or dual- element PIR (passive infrared) sensors, provide fairly crude data that’s difficult to interpret  Wearable devices such as wrist communicators and motion detectors have potential but rely on a person’s ability and willingness to wear them

5 Justification for their approach  Cameras might appear intrusive and require considerable human resources to monitor activity.  Machine interpretation of camera images is complex and might be difficult in this application area

6 Justification for their approach  IRISYS (InfraRed Integrated Systems) thermal imaging sensors can help overcome these limitations.  The sensor is wall mounted, and users don’t have to wear a device  this solution’s cost-effectiveness, because the low-level data lacks detail, the system will seem less intrusive to users.

7 Justification for their approach

8 SIMBAD’ s technical development  The IRISYS sensor can reliably locate and track a thermal target in the sensor’s field of view, providing size, location, and velocity information.

9 SIMBAD’ s technical development  SIMBAD considers two distinct characteristics of observed behavior:  First, it analyzes target motion to detect falls’ characteristic dynamics  Second, it monitors target inactivity and compares it with a map of acceptable periods of inactivity in different locations in the field of view.

10 SIMBAD’ s technical development  the prototype system architecture, which has five major components  Tracker  The tracker identifies and tracks an elliptical target using data from the IRISYS sensor  The tracker provides real-time estimates of target position, velocity, shape, and size.

11 SIMBAD’ s technical development  Fall detector  This subsystem employs a neural network to classify falls using vertical-velocity estimates derived either directly from IRISYS sensor data or from the tracker  Subtle-motion detector  This relatively simple signal-based mechanism identifies small movements in the sensor’s field of view

12 SIMBAD’ s technical development  Because such movements generate insufficient responses to activate the tracker  Inactivity monitor  This uses output from the tracker and subtle- motion detector to monitor periods of inactivity in the sensor’s field of view  Once a target is no longer visible, this subsystem monitors two distinct types of inactivity in the neighborhood of the last known position

13 SIMBAD’ s technical development  Coarse-scale inactivity identifies the period of time since the tracker last tracked the object.  Fine-scale inactivity identifies the period of time since the system detected subtle motion in some neighborhood of the object’s last known position.

14 SIMBAD’ s technical development  High-level reasoner  This subsystem performs the reasoning required to monitor the output of the fall detector, inactivity monitor, and subtle-motion detector and to generate alarm signals if required.  The system generates two classes of alarm— those triggered by excessive periods of inactivity (according to the risk map) and those triggered by the detection of a fall.

15 SIMBAD’ s technical development

16 conclusion  To refine SIMBAD and extend its capabilities, they’re  Improving the fall detection algorithms, which, might involve developing a more elaborate representation of a fall’s dynamics  Creating algorithms to track, locate, multiple individuals in a multiroom environment

17 conclusion  Developing a sensor subsystem that lets a group of sensors monitor the activity of one or more individuals throughout a building’s living spaces and discriminate between real and false alerts  Integrating the sensor in a host telecare system  Conducting further field trials to assess SIMBAD’s usefulness in supporting the elderly living in the community