KinSpace: Passive Obstacle Detection via Kinect

Falls are a significant problem for the elderly living independently in the home. Many falls occur due to household objects left in open spaces. We present KinSpace, a passive obstacle detection system for the home. KinSpace employs the use of a Kinect sensor to learn the open space of an environment through observation of resident walking patterns. It then monitors the open space for obstacles that are potential tripping hazards and notifies the residents accordingly. KinSpace uses real-time depth data and human-in-the-loop feedback to adjust its understanding of the open space of an environment. We present a 5,000-frame deployment dataset spanning multiple homes and classes of objects. We present results showing the effectiveness of our underlying technical solutions in identifying open spaces and obstacles. The results for both lab testing and a deployment in an actual home show roughly 80% accuracy for both open space detection and obstacle detection even in the presence of many real-world issues. Consequently, this new technology shows great potential to reduce the risk of falls in the home due to environmental hazards.

[1]  Azriel Rosenfeld,et al.  Connectivity in Digital Pictures , 1970, JACM.

[2]  S. Brereton Life , 1876, The Indian medical gazette.

[3]  L. Rubenstein,et al.  The epidemiology of falls and syncope. , 2002, Clinics in geriatric medicine.

[4]  David Obdrzalek,et al.  Research and Education in Robotics - EUROBOT 2011 - International Conference, Prague, Czech Republic, June 15-17, 2011. Proceedings , 2011, Eurobot Conference.

[5]  Gang Zhou,et al.  Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[6]  A. Jech,et al.  Preventing falls in the elderly. , 1992, Geriatric nursing.

[7]  Zhigang Zhu,et al.  KinDectect: Kinect Detecting Objects , 2012, ICCHP.

[8]  Wolfgang L. Zagler,et al.  Computers Helping People with Special Needs, 12th International Conference, ICCHP 2010, Vienna, Austria, July 14-16, 2010, Proceedings, Part II , 2010, ICCHP.

[9]  Dong Xuan,et al.  Mobile phone-based pervasive fall detection , 2010, Personal and Ubiquitous Computing.

[10]  Patrick Boissy,et al.  A smart sensor based on rules and its evaluation in daily routines , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[11]  S. Ebrahim,et al.  Falls by elderly people at home: prevalence and associated factors. , 1988, Age and ageing.

[12]  A. Schultz,et al.  Age effects on strategies used to avoid obstacles , 1994 .

[13]  N. Noury,et al.  Monitoring behavior in home using a smart fall sensor and position sensors , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[14]  Harald Reiterer,et al.  NAVI - A Proof-of-Concept of a Mobile Navigational Aid for Visually Impaired Based on the Microsoft Kinect , 2011, INTERACT.

[15]  John A. Stankovic,et al.  Context-aware wireless sensor networks for assisted living and residential monitoring , 2008, IEEE Network.

[16]  Marco Winckler,et al.  Human-Computer Interaction - INTERACT 2011 - 13th IFIP TC 13 International Conference, Lisbon, Portugal, September 5-9, 2011, Proceedings, Part III , 2011, INTERACT.

[17]  John A. Stankovic,et al.  Kinsight: Localizing and Tracking Household Objects Using Depth-Camera Sensors , 2012, 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems.

[18]  Oliver Bittel,et al.  Obstacle and Game Element Detection with the 3D-Sensor Kinect , 2011, Eurobot Conference.

[19]  C. Becker,et al.  Cost of falls in old age: a systematic review , 2010, Osteoporosis International.

[20]  Tao Yuan,et al.  A wearable pre-impact fall detector using feature selection and Support Vector Machine , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[21]  M. Dellepiane,et al.  A Low-Cost Time-Critical Obstacle Avoidance System for the Visually Impaired , 2011 .

[22]  Ruzena Bajcsy,et al.  USING SMART SENSORS AND A CAMERA PHONE TO DETECT AND VERIFY THE FALL OF ELDERLY PERSONS , 2005 .

[23]  Nathan Silberman,et al.  Indoor scene segmentation using a structured light sensor , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[24]  M. Prado,et al.  Preliminary evaluation of a full-time falling monitor for the elderly , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.