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.