Author
David Howard
Other affiliations: University of Manchester, RMIT University
Bio: 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.
Papers published on a yearly basis
Papers
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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.
Abstract: Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, various classification schemes and feature extraction methods have been used to identify different activities from a range of different datasets. In this paper, we present a comparison of 14 methods to extract classification features from accelerometer signals. These are based on the wavelet transform and other well-known time- and frequency-domain signal characteristics. To allow an objective comparison between the different features, we used two datasets of activities collected from 20 subjects. The first set comprised three commonly used activities, namely, level walking, stair ascent, and stair descent, and the second a total of eight activities. Furthermore, we compared the classification accuracy for each feature set across different combinations of three different accelerometer placements. The classification analysis has been performed with robust subject-based cross-validation methods using a nearest-neighbor classifier. 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. Overall, the best feature sets achieved over 95% intersubject classification accuracy.
528 citations
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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.
251 citations
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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.
226 citations
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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.
225 citations
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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.
148 citations
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01 Jan 2016
TL;DR: Biomechanics and motor control of human movement is downloaded so that people can enjoy a good book with a cup of tea in the afternoon instead of juggling with some malicious virus inside their laptop.
Abstract: Thank you very much for downloading biomechanics and motor control of human movement. Maybe you have knowledge that, people have search hundreds times for their favorite books like this biomechanics and motor control of human movement, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious virus inside their laptop.
1,689 citations
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TL;DR: The emergence of `ambient-assisted living’ (AAL) tools for older adults based on ambient intelligence paradigm is summarized and the state-of-the-art AAL technologies, tools, and techniques are summarized.
Abstract: In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.
1,000 citations
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TL;DR: The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography, which are expected to play an increasingly important role in clinical applications.
Abstract: Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.
926 citations
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TL;DR: This review summarizes the latest advances in this emerging field of "bio-integrated" technologies in a comprehensive manner that connects fundamental developments in chemistry, material science, and engineering with sensing technologies that have the potential for widespread deployment and societal benefit in human health care.
Abstract: Bio-integrated wearable systems can measure a broad range of biophysical, biochemical, and environmental signals to provide critical insights into overall health status and to quantify human performance. Recent advances in material science, chemical analysis techniques, device designs, and assembly methods form the foundations for a uniquely differentiated type of wearable technology, characterized by noninvasive, intimate integration with the soft, curved, time-dynamic surfaces of the body. This review summarizes the latest advances in this emerging field of “bio-integrated” technologies in a comprehensive manner that connects fundamental developments in chemistry, material science, and engineering with sensing technologies that have the potential for widespread deployment and societal benefit in human health care. An introduction to the chemistries and materials for the active components of these systems contextualizes essential design considerations for sensors and associated platforms that appear in f...
727 citations
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TL;DR: A set of new methods for joint angle calculation based on inertial measurement data in the context of human motion analysis are presented, including methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field.
Abstract: This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.
632 citations