D
David Howard
Researcher at University of Salford
Publications - 115
Citations - 3294
David Howard is an academic researcher from University of Salford. The author has contributed to research in topics: Gait (human) & Functional electrical stimulation. The author has an hindex of 26, co-authored 106 publications receiving 2884 citations. Previous affiliations of David Howard include University of Manchester & RMIT University.
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A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data
TL;DR: The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequency-based features when classifying dynamic activities performed by healthy subjects.
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Inertial sensor-based knee flexion/extension angle estimation
Glen Cooper,Ian Sheret,Louise McMillian,Konstantinos Siliverdis,Ning Sha,Diana Hodgins,Laurence Kenney,David Howard +7 more
TL;DR: A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described, which uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge and does not make use of the earth's magnetic field.
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Whole body inverse dynamics over a complete gait cycle based only on measured kinematics
TL;DR: The model gave reasonably good estimates of sagittal plane ground forces and moment; however, the estimates in the other planes were less good, which the authors believe is largely due to their small magnitudes in comparison to the sagittal forces and Moment.
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Foot kinematics during walking measured using bone and surface mounted markers.
Christopher J. Nester,Richard Jones,Anmin Liu,David Howard,Arne Lundberg,Anton Arndt,P Lundgren,Alex Stacoff,Peter Wolf +8 more
TL;DR: It is unlikely that one rigid body foot model and marker attachment approach is always preferable over another, as differences between the data from the skin and plate protocols were consistently smaller than differences between either protocol and the kinematic data for each bone comprising the segment.
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Predicting lower limb joint kinematics using wearable motion sensors
TL;DR: A wearable system based only on footwear mounted sensors and a simpler sensor set providing only acceleration data shows potential, and predictions were generally stable when sensor data was lost, it remains to be seen whether the generalised regression networks algorithm is robust for other activities such as stair climbing.