"Body-In-The-Loop": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost
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{"title"=>"\"Body-in-the-loop\": Optimizing device parameters using measures of instantaneous energetic cost", "type"=>"journal", "authors"=>[{"first_name"=>"Wyatt", "last_name"=>"Felt", "scopus_author_id"=>"55991229900"}, {"first_name"=>"Jessica C.", "last_name"=>"Selinger", "scopus_author_id"=>"56435887300"}, {"first_name"=>"J. Maxwell", "last_name"=>"Donelan", "scopus_author_id"=>"7003559506"}, {"first_name"=>"C. David", "last_name"=>"Remy", "scopus_author_id"=>"23971268600"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "sgr"=>"84942475349", "doi"=>"10.1371/journal.pone.0135342", "scopus"=>"2-s2.0-84942475349", "isbn"=>"5923786001", "pmid"=>"26288361", "pui"=>"606153877"}, "id"=>"58070a6a-ecca-3877-9640-877f67711546", "abstract"=>"This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body \"in-the-loop\") and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (Steady-State Cost Mapping, Instantaneous Cost Mapping, and Instantaneous Cost Gradient Search) with eight healthy human subjects. Steady-State Cost Mapping is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, Instantaneous Cost Mapping identifies a cubic polynomial more quickly. Instantaneous Cost Gradient Search uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects' preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using Instantaneous Cost Mapping, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous Cost Gradient Search extends favorably to multi-dimensional parameter spaces.", "link"=>"http://www.mendeley.com/research/bodyintheloop-optimizing-device-parameters-using-measures-instantaneous-energetic-cost", "reader_count"=>77, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>12, "Student > Ph. D. Student"=>33, "Other"=>6, "Student > Master"=>10, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>12, "Student > Ph. D. Student"=>33, "Other"=>6, "Student > Master"=>10, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>53, "Unspecified"=>4, "Nursing and Health Professions"=>2, "Medicine and Dentistry"=>6, "Agricultural and Biological Sciences"=>1, "Sports and Recreations"=>6, "Business, Management and Accounting"=>2, "Psychology"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>53}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Sports and Recreations"=>{"Sports and Recreations"=>6}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Uruguay"=>1, "United States"=>2}, "group_count"=>12}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2217355"], "description"=>"<p>Shown are means and standard deviations of the predicted energetic minima. We use the subjects’ preferred step frequency as ‘ground truth’ for comparison [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135342#pone.0135342.ref015\" target=\"_blank\">15</a>]. All three methods, on average, identify energetic minima near the subjects preferred cadence. The variance in the outcomes of the proposed Instantaneous Cost methods is only slightly higher than that of the established Steady-State Cost Mapping. There is no significant difference between the absolute error of the three methods.</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514656, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_the_Three_Methods_/1514656", "title"=>"Accuracy of the Three Methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217357"], "description"=>"<p>The lines indicate the prescribed step frequencies over the course of the experiment. The experiment begins with an incorrect guess of the optimal step frequency that is randomly selected to be either 20% above or below the subject’s preferred step frequency. After a warm-up period, the step frequency is perturbed above and below the guess. The measurements of oxygen consumption during this perturbation process are used to estimate the slope of the underlying relationship between energetic cost and step frequency. This slope, or gradient, is used to update the guess of the optimal step frequency. Though the performance of the algorithm varied between subjects, the algorithm was always able to approach the energetic minimum. The average total walking-time was 56.5 minutes and the mean relative error of the final iteration value to the subject’s preferred step frequency was −0.29% ± 4.77% (mean ± SD). The lightening of shades indicates the progress of time.</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514658, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.g005", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Convergence_of_the_Instantaneous_Cost_Gradient_Search_Method_/1514658", "title"=>"Convergence of the Instantaneous Cost Gradient Search Method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217358"], "description"=>"<p>Data are shown for Subject 1 and Subject 8 (poor and good performance of Instantaneous Cost methods respectively). This figure illustrates the third-order polynomial fit to the means (± SE) in the Steady-State Cost Mapping method, the third order polynomial response surface that best predicts the measures from the Instantaneous Cost Mapping, and the series of linear response surfaces used to update the parameter guess in the Instantaneous Cost Gradient Search (the lightening of shades indicates the sequence of the fits). The linear response surfaces of the Instantaneous Cost Gradient Search for Subject 1 show a much higher degree of variability than Subject 8. This led the algorithm to converge at a value somewhat below the subject’s preferred step frequency (worse than any other subject). Interestingly, the Instantaneous Cost Mapping for Subject 1 estimated a similarly low minimum. For subject 8, the linear response surfaces are more regular, leading the gradient search algorithm to outperform both of the mapping methods.</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514659, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_Response_Surface_Estimates_of_the_Three_Methods_/1514659", "title"=>"Examples of Response Surface Estimates of the Three Methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217359"], "description"=>"<p>The subject’s preferred cadence (step frequency) was evaluated while on a treadmill without a metronome. “Time constant” refers to the time constant of the metabolic response used in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135342#pone.0135342.e008\" target=\"_blank\">Eq (1)</a>. This was characterized with a step-change in energetic requirements during the first six minutes of the Instantaneous Cost Mapping trial.</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514660, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Subject_Specific_Data_/1514660", "title"=>"Subject-Specific Data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217360"], "description"=>"<p>Listed are estimates of the energetic minima resulting from the different algorithms. Values are in percent-error with respect to the subject’s preferred step frequency. SSCM refers to a Steady-State Cost Mapping with a best-fit third-order polynomial. ICM refers to the minima of the Instantaneous Cost Mapping. ICGS refers to terminal values of the Instantaneous Cost Gradient Search.</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514661, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.t002", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimization_Results_/1514661", "title"=>"Optimization Results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217361"], "description"=>"<p>SSCM refers to the traditional method of Steady-State Cost Mapping. Our methods rely on the estimation of Instantaneous Cost. ICM refers to the Instantaneous Cost Mapping and ICGS to the Instantaneous Cost Gradient Search.</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514662, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.t003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Qualitative_Comparison_of_the_Three_Methods_/1514662", "title"=>"Qualitative Comparison of the Three Methods.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217362"], "description"=>"<div><p>This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body “in-the-loop”) and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (<i>Steady-State Cost Mapping</i>, <i>Instantaneous Cost Mapping</i>, and <i>Instantaneous Cost Gradient Search</i>) with eight healthy human subjects. <i>Steady-State Cost Mapping</i> is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, <i>Instantaneous Cost Mapping</i> identifies a cubic polynomial more quickly. <i>Instantaneous Cost Gradient Search</i> uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects’ preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using <i>Instantaneous Cost Mapping</i>, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous Cost Gradient Search extends favorably to multi-dimensional parameter spaces.</p></div>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514663, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342", "stats"=>{"downloads"=>5, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Body_In_The_Loop_Optimizing_Device_Parameters_Using_Measures_of_Instantaneous_Energetic_Cost_/1514663", "title"=>"\"Body-In-The-Loop\": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217351"], "description"=>"<p>The relationship <i>x</i>(<i>p</i>) between a controller parameter <i>p</i> (in this work, step-frequency) and the physiological objective <i>x</i> (in this work, metabolic energetic cost) was replicated by a response surface </p><p></p><p></p><p></p><p><mi>x</mi><mo>‾</mo></p><p><mo stretchy=\"true\">(</mo><mi>p</mi><mo>,</mo><mi>λ</mi><mo stretchy=\"true\">)</mo></p><p></p><p></p><p></p> defined by a set of shape parameters <i><b>λ</b></i>. The shape parameters were identified to minimize the error between predicted respiratory response <p></p><p></p><p></p><p><mi>y</mi><mo>‾</mo></p><p></p><p></p><p></p> (based on a model of the measurement dynamics [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135342#pone.0135342.ref020\" target=\"_blank\">20</a>]) and actual measures <p></p><p></p><p></p><p><mi>y</mi><mo>^</mo></p><p></p><p></p><p></p>. This dynamic estimation process enabled us to use non-steady-state breath-by-breath measures to approximate the relationship between the control parameter <i>p</i> and energetic cost <i>x</i>. Optimization was performed with respect to the response surface, <p></p><p></p><p></p><p><mi>x</mi><mo>‾</mo></p><p><mo stretchy=\"true\">(</mo><mi>p</mi><mo>,</mo><mi>λ</mi><mo stretchy=\"true\">)</mo></p><p></p><p></p><p></p>, that approximates the energy-parameter relationship.<p></p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514652, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.g001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphical_Representation_of_the_Response_Surface_Identification_/1514652", "title"=>"Graphical Representation of the Response Surface Identification.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217352"], "description"=>"<p><i>Steady-State Cost Mapping</i> uses a cubic polynomial fit to averages of steady-state metabolic measurements taken in random order at discrete step frequencies between -25% and 25% of the subject’s preferred step frequency. <i>Instantaneous Cost Mapping</i> uses data collected from continuously changing the commanded step frequency over the same range. The predicted minimum of this method is obtained from a cubic polynomial response surface fit that best predicts the respiratory measurements. The <i>Instantaneous Cost Gradient Search</i> uses an estimate of the slope of the energy-parameter surface to update the guess of the optimal parameter setting. The slope estimate comes from measurements taken at discrete step frequencies a small distance away from the current guess. The predicted minimum is the final iteration value. (Gray lines indicate data that are not used in the identification.)</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514653, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.g002", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_Methods_/1514653", "title"=>"Overview of Methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-19 03:50:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2217353"], "description"=>"<p>“Body-in-the-Loop” optimization refers to the use of real-time measures of a physiological value function to drive the online selection of parameter values. We evaluated this concept with the example of treadmill walking to the beat of a metronome. In a study with eight healthy subjects, we used step-frequency as a proxy for the controller parameters of a computer controlled assistive device. Our physiological objective was the minimization of metabolic energetic cost. Three different computer algorithms were implemented that prescribed step-frequency via the metronome and that evaluated measures of oxygen consumption.</p>", "links"=>[], "tags"=>["Optimizing Device Parameters", "parameter value", "method", "account measurement dynamics", "step frequency", "response surface identification", "Instantaneous Cost Gradient", "Instantaneous Cost Mapping"], "article_id"=>1514654, "categories"=>["Biological Sciences"], "users"=>["Wyatt Felt", "Jessica C. Selinger", "J. Maxwell Donelan", "C. David Remy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135342.g003", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_Evaluation_/1514654", "title"=>"Experimental Evaluation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-19 03:50:59"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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