Demo Data from SIGIR 2026 / reviewers in the wild / expert
Emily Lynn Bishop
dblp:49/8368
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Wearable and physiological sensing · 77% Accessibility and assistive technology · 12% Human-robot interaction · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › gait analysis
walking speed estimation |
0.1 | 1 | 2010 | Walking speed estimation using shank-mounted accelerometers · ICRA 2010 |
Wearable and physiological sensing
wearable sensing |
0.1 | 1 | 2010 | Walking speed estimation using shank-mounted accelerometers · ICRA 2010 |
Accessibility and assistive technology
assistive technology |
0.0 | 1 | 2010 | Walking speed estimation using shank-mounted accelerometers · ICRA 2010 |
Human-robot interaction › physical human-robot interaction
prosthetic control |
0.0 | 1 | 2010 | Walking speed estimation using shank-mounted accelerometers · ICRA 2010 |
Methods — techniques the papers use, named apart from their topics
inverted pendulum model · 0.1direct integration · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Walking speed estimation using shank-mounted accelerometersabstractWe studied the feasibility of estimating walking speed using two shank-mounted accelerometers. Our approach took advantage of the inverted pendulum-like behavior of the stance leg during walking to identify a new method for dividing up walking into individual stride cycles and estimating the initial conditions for the direct integration of the accelerometer signals. To test its accuracy, we compared speed to known values during treadmill walking. The speed estimation method worked well across treadmill speeds yielding a root mean square speed estimation error of only 8%. This accuracy is comparable to that achieved from shank-mounted inertial measurement unit, providing a robust and low-cost alternative in using accelerometer for walking speed estimation. Shank-mounted accelerometer may be of great benefit for estimating speed in walking for the embedded control of knee-mounted devices such as prostheses and energy harvesters. Emily Lynn Bishop, Qingguo Li |
ICRA | 1 |