TY - GEN
T1 - Towards a real time kinect signature based human activity assessment at home
AU - Blumrosen, Gaddi
AU - Miron, Yael
AU - Plotnik, Meir
AU - Intrator, Nathan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/15
Y1 - 2015/10/15
N2 - Tracking Human activity at home plays a growing factor in fields of security, and of bio-medicine. Microsoft Kinect is a non-wearable sensor that aggregate depth images with traditional optical video frames to estimate individuals' joints' location for kinematic analysis. When the subject of interest is out of Kinect coverage, or not in line of sight, the joints' estimations are distorted, which reduce the estimation accuracy, and can lead, in a scenario of multiple subjects, to erroneous estimations' assignment. In this work we derive features from Kinect joints and form a Kinect Signature (KS). This signature is used to identify different patients, differentiate them from others, exclude artifacts and derive the tracking quality. The suggested technology has the potential to assess human kinematics at home, reduce the cost of the patient traveling to the hospital, and improve the medical treatment follow-up.
AB - Tracking Human activity at home plays a growing factor in fields of security, and of bio-medicine. Microsoft Kinect is a non-wearable sensor that aggregate depth images with traditional optical video frames to estimate individuals' joints' location for kinematic analysis. When the subject of interest is out of Kinect coverage, or not in line of sight, the joints' estimations are distorted, which reduce the estimation accuracy, and can lead, in a scenario of multiple subjects, to erroneous estimations' assignment. In this work we derive features from Kinect joints and form a Kinect Signature (KS). This signature is used to identify different patients, differentiate them from others, exclude artifacts and derive the tracking quality. The suggested technology has the potential to assess human kinematics at home, reduce the cost of the patient traveling to the hospital, and improve the medical treatment follow-up.
KW - Gait analsyis
KW - Kinect
KW - Motion tracking
KW - Parkinson Diseases
KW - Sensor Network
UR - http://www.scopus.com/inward/record.url?scp=84961626128&partnerID=8YFLogxK
U2 - 10.1109/BSN.2015.7299359
DO - 10.1109/BSN.2015.7299359
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AN - SCOPUS:84961626128
T3 - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
BT - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
Y2 - 9 June 2015 through 12 June 2015
ER -