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Benjamin J. Fregly

Bio: Benjamin J. Fregly is an academic researcher from Rice University. The author has contributed to research in topics: Contact force & Gait (human). The author has an hindex of 48, co-authored 150 publications receiving 8061 citations. Previous affiliations of Benjamin J. Fregly include Veterans Health Administration & University of Melbourne.


Papers
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Journal ArticleDOI
TL;DR: An annual “Grand Challenge Competition to Predict In Vivo Knee Loads” based on a series of comprehensive publicly available in vivo data sets for evaluating musculoskeletal model predictions of contact and muscle forces in the knee is introduced.

490 citations

Journal ArticleDOI
TL;DR: In vivo data collected from a single subject with an instrumented knee implant support the hypothesis that the knee adduction torque is highly correlated with medial compartment contact force and medial to total force ratio during gait.

485 citations

Journal ArticleDOI
TL;DR: In this article, a coarse-grained parallelization of the particle swarm optimization (PSO) algorithm is presented. But the performance of the parallel PSO algorithm is limited by the synchronization requirement of each iteration.
Abstract: Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.

369 citations

01 Dec 2004
TL;DR: The results suggest that (1) parallel PSO exhibits excellent parallel performance under load‐balanced conditions, (2) an asynchronous implementation would be valuable for real‐life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.
Abstract: : Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the Particle Swarm Optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima - large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that 1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, 2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and 3) larger population sizes should be considered when multiple processors are available.

330 citations

Journal ArticleDOI
TL;DR: In vivo gait data collected from a subject with a force‐measuring knee implant was used to assess whether KAM decreases accurately predict corresponding decreases in medial contact force and future studies that evaluate the effectiveness of gait modifications for offloading the medial compartment of the knee should consider the combined effect of these two knee moments.

323 citations


Cited by
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Journal ArticleDOI

6,278 citations

Journal ArticleDOI
TL;DR: OpenSim is developed, a freely available, open-source software system that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments.
Abstract: Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.

3,621 citations

Journal ArticleDOI
TL;DR: In this paper, a review of wearable sensors and systems that are relevant to the field of rehabilitation is presented, focusing on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders.
Abstract: The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

1,826 citations

Journal ArticleDOI
TL;DR: For STA to be compensated for effectively, it is here suggested that either its subject-specific pattern is assessed by ad hoc exercises or it is characterized from a large series of measurements on different subject populations.

1,030 citations

Journal ArticleDOI
TL;DR: In this study, the individual contributions of the ankle plantar flexors to the body segment energetics are examined using a musculoskeletal model and optimization framework to generate a forward dynamics simulation of normal walking at 1.5 m/s.

1,025 citations