3 research outputs found
Comparison of gait event detection from shanks and feet in single-task and multi-task walking of healthy older adults
Automatic and objective detection algorithms for gait events from MEMS Inertial Measurement Units data have been developed to overcome subjective inaccuracy in traditional visual observation. Their accuracy and sensitivity have been verified with healthy older adults, Parkinson's disease and spinal injured patients, using single-task gait exercises, where events are precise as the subject is focusing only on walking. Multi-task walking instead simulates a more realistic and challenging scenario where subjects perform secondary cognitive task while walking, so it is a better benchmark. In this paper, we test two algorithms based on shank and foot angular velocity data in single-task, dual-task and multi-task walking. Results show that both algorithms fail when the subject slows extremely down or pauses due to high cognitive and attentional load, and, in particular, the first stride detection error rate of the foot-based algorithm increases. Stride time is accurate with both algorithms regardless of walking types, but the shank-based algorithm leads to an overestimation on the proportion of swing phase in one gait cycle. Increasing the number of cognitive tasks also causes this error with both algorithms
Is endobronchial ultrasound-guided transbronchial needle aspiration with a stylet necessary for lymph node screening in lung cancer patients?
<div><p>During endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), a needle is commonly used with a stylet, although recently the stylet has been omitted. This prospective study aimed to compare the quality of specimens obtained by EBUS-TBNA performed with and without a stylet. Between November 2013 and November 2014, 131 patients with lung cancer underwent EBUS-TBNA, with a total of 148 mediastinal or hilar lymph nodes sampled both with and without an inner-stylet, yielding 296 cytological specimens. Specimens were scored cytologically using five parameters: background blood or clot, amount of cellular material, degree of cellular degeneration, degree of cellular trauma, and retention of appropriate architecture. The procedure with a stylet required significantly longer operation time than without a stylet (14.5±0.8 vs 12.7±1.1 min, P<0.001). Excellent specimens were obtained in 261/296 and 260/296 samples in the procedures with and without a stylet, respectively (P=0.9), while the remaining 35 and 36 samples, respectively, were adequate. The diagnosing and staging of lung cancer using EBUS-TBNA did not differ significantly between the groups. In conclusion, specimen collection by EBUS-TBNA without a stylet is easier and faster than the procedure using a stylet and absence of a stylet did not alter specimen quality or diagnostic accuracy.</p></div
Comparison of gait event detection from shanks and feet in single-task and multi-task walking of healthy older adults
Automatic and objective detection algorithms for gait events from MEMS Inertial Measurement Units data have been developed to overcome subjective inaccuracy in traditional visual observation. Their accuracy and sensitivity have been verified with healthy older adults, Parkinson's disease and spinal injured patients, using single-task gait exercises, where events are precise as the subject is focusing only on walking. Multi-task walking instead simulates a more realistic and challenging scenario where subjects perform secondary cognitive task while walking, so it is a better benchmark. In this paper, we test two algorithms based on shank and foot angular velocity data in single-task, dual-task and multi-task walking. Results show that both algorithms fail when the subject slows extremely down or pauses due to high cognitive and attentional load, and, in particular, the first stride detection error rate of the foot-based algorithm increases. Stride time is accurate with both algorithms regardless of walking types, but the shank-based algorithm leads to an overestimation on the proportion of swing phase in one gait cycle. Increasing the number of cognitive tasks also causes this error with both algorithms