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10.3390/s17061342 http://scihub22266oqcxt.onion/10.3390/s17061342 C5492489!5492489!28598394     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sensors+(Basel) 2017 ; 17 (6): ä Nephropedia Template TP {{tp|p=28598394|t=2017. Recognizing Bedside Events Using Thermal and Ultrasonic Readings |pdf=|usr=}}{{28598394}} gab.com Text Recognizing Bedside Events Using Thermal and Ultrasonic Readings JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=28598394 #FOAvip Falls in homes of the elderly, in residential care facilities and in hospitals commonly occur in close proximity to the bed. Most approaches for recognizing falls use cameras, which challenge privacy, or sensor devices attached to the bed or the body to recognize bedside events and bedside falls. We use data collected from a ceiling mounted 80 × 60 thermal array combined with an ultrasonic sensor device. This approach makes it possible to monitor activity while preserving privacy in a non-intrusive manner. We evaluate three different approaches towards recognizing location and posture of an individual. Bedside events are recognized using a 10-second floating image rule/filter-based approach, recognizing bedside falls with 98.62% accuracy. Bed-entry and exit events are recognized with 98.66% and 96.73% accuracy, respectively. Twit Text FOAVip Recognizing Bedside Events Using Thermal and Ultrasonic Readings ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=28598394 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28598394 #FOAvip English Wikipedia <ref>{{Cite pmid|28598394}}</ref> Recognizing Bedside Events Using Thermal and Ultrasonic Readings #MMPMID28598394 Asbjørn D; Jim T Sensors (Basel) 2017[Jun]; 17 (6): ä PMID28598394show ga Falls in homes of the elderly, in residential care facilities and in hospitals commonly occur in close proximity to the bed. Most approaches for recognizing falls use cameras, which challenge privacy, or sensor devices attached to the bed or the body to recognize bedside events and bedside falls. We use data collected from a ceiling mounted 80 × 60 thermal array combined with an ultrasonic sensor device. This approach makes it possible to monitor activity while preserving privacy in a non-intrusive manner. We evaluate three different approaches towards recognizing location and posture of an individual. Bedside events are recognized using a 10-second floating image rule/filter-based approach, recognizing bedside falls with 98.62% accuracy. Bed-entry and exit events are recognized with 98.66% and 96.73% accuracy, respectively. äRecognizing Bedside Events Using Thermal and Ultrasonic Readings (epmc).pdf DeepDyve Pubget Overpricing