An ear-worn sensor for the detection of gait impairment after abdominal surgery.
Surgery to the trunk often results in a change of gait, most pronounced during walking. This change is usually transient, often as a result of wound pain, and returns to normal as the patient recovers. Quantifying and monitoring gait impairment therefore represents a novel means of functional postoperative home recovery follow-up. Until now, this type of assessment could only be made in a gait lab, which is both expensive and labor intensive to administer on a large scale. The objective of this work is to validate the use of an ear-worn activity recognition (e-AR) sensor for quantification of gait impairment after abdominal wall and perianal surgery. The e-AR sensor was used on 2 comparative simulated data sets (N = 32) of truncal impairment to observe walking patterns. The sensor was also used to observe the walking patterns of preoperative and postoperative surgical patients who had undergone abdominal wall (n = 5) and perianal surgery (n = 5). Methods for multiresolution feature extraction, selection, and classification are investigated using the raw ear-sensor data. Results show that the method demonstrates a good separation between impaired and nonimpaired classes for both simulated and real patient data sets. This indicates that the e-AR sensor may be used as a tool for the pervasive assessment of postoperative gait impairment, as part of functional recovery monitoring, in patients at their own homes.
Imperial College London, London, UK.
Louis
L
Atallah
Omer
O
Aziz
Edward
E
Gray
Benny
B
Lo
Guang-Zhong
GZ
Yang
D034861:Abdominal Wall / Q000601:surgery; D000465:Algorithms; D001003:Anal Canal / Q000601:surgery; D003198:Computer Simulation; D004423:Ear; D005684:Gait / Q000502:physiology*; D006801:Humans; D051346:Mobility Limitation; D008962:Models, Theoretical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D011184:Postoperative Period; D020127:Recovery of Function / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D016138:Walking / Q000145:classification / Q000502:physiology*; D059015:Wireless Technology / Q000295:instrumentation*