{"id":{"repo_id":"uic","oai_identifier":"oai:figshare.com:article/31451620"},"canonical_url":"https://search.dev.ndltd.org/etd/uic/oai:figshare.com:article/31451620","repository":{"repo_id":"uic","name":"University of Illinois - Chicago","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"EMG-Based Human-in-the-Loop Bayesian Optimization to Assist Hip-Centric Activities","abstract":"This dissertation presents a practical method for personalizing hip exoskeleton assistance using surface EMG-based human-in-the-loop optimization, cutting tuning time from hours to minutes while preserving assistance quality. We show that processed EMG provides a reliable objective for rapid personalization, enabling convergence within typical clinical sessions. The research progresses from simulation studies revealing fundamental controller-hardware gaps to experimental validation across three activities. In leg swinging (n=8), EMG-based optimization reduces muscle activity by 15-17\\% with <15 seconds of steady data per trial. In squatting (n=4), the method completes tuning in 4 minutes 40 seconds and yields 21\\% lower metabolic cost with 17\\% lower EMG. In walking (n=11), a multi-objective formulation balancing EMG and user preference identifies personalized controllers in 11-12 minutes, reducing metabolic cost by 14.9\\% while improving perceived exertion by 25-45\\%. Three technical innovations enable this speed: (i) a signal-enhancement pipeline combining Hankel decomposition, Bayesian regularization, and optimized smoothing that improves composite EMG quality metrics by 108\\%; (ii) machine-learning-guided initialization from anthropometric measurements that reduces convergence time by 26.5\\% and improves final performance by 9.98\\%; and (iii) heteroscedastic Gaussian process surrogates with Expected Hypervolume Improvement that capture input-dependent noise, improving predictive accuracy by 23-31%. Supporting investigations establish practical design principles. Systematic evaluation across 12 participants performing 30 conditions each reveals that simple amplitude summation provides the most reliable EMG-metabolic correlation (r=0.762), challenging assumptions about complex feature necessity. Simulation studies comparing 12 controller architectures demonstrate that phase-adaptive impedance achieves 55.5\\% mechanical power reduction with minimal parameters, while Bezier profiles reach 62.9\\% reduction at higher implementation cost. These results establish EMG-based HIL as a clinically feasible approach to exoskeleton personalization, validated across 23 healthy adults. The framework employs compact controller parameterizations (4-8 parameters) suitable for real-time optimization, with transparent cost functions and traceable convergence. Limitations include healthy-adult validation, electrode placement sensitivity, and restricted activity scope. Extensions should address clinical populations, online adaptation to fatigue, and broader task coverage.","abstract_html":"This dissertation presents a practical method for personalizing hip exoskeleton assistance using surface EMG-based human-in-the-loop optimization, cutting tuning time from hours to minutes while preserving assistance quality. We show that processed EMG provides a reliable objective for rapid personalization, enabling convergence within typical clinical sessions. The research progresses from simulation studies revealing fundamental controller-hardware gaps to experimental validation across three activities. In leg swinging (n=8), EMG-based optimization reduces muscle activity by 15-17\\% with &lt;15 seconds of steady data per trial. In squatting (n=4), the method completes tuning in 4 minutes 40 seconds and yields 21\\% lower metabolic cost with 17\\% lower EMG. In walking (n=11), a multi-objective formulation balancing EMG and user preference identifies personalized controllers in 11-12 minutes, reducing metabolic cost by 14.9\\% while improving perceived exertion by 25-45\\%. Three technical innovations enable this speed: (i) a signal-enhancement pipeline combining Hankel decomposition, Bayesian regularization, and optimized smoothing that improves composite EMG quality metrics by 108\\%; (ii) machine-learning-guided initialization from anthropometric measurements that reduces convergence time by 26.5\\% and improves final performance by 9.98\\%; and (iii) heteroscedastic Gaussian process surrogates with Expected Hypervolume Improvement that capture input-dependent noise, improving predictive accuracy by 23-31%. Supporting investigations establish practical design principles. Systematic evaluation across 12 participants performing 30 conditions each reveals that simple amplitude summation provides the most reliable EMG-metabolic correlation (r=0.762), challenging assumptions about complex feature necessity. Simulation studies comparing 12 controller architectures demonstrate that phase-adaptive impedance achieves 55.5\\% mechanical power reduction with minimal parameters, while Bezier profiles reach 62.9\\% reduction at higher implementation cost. These results establish EMG-based HIL as a clinically feasible approach to exoskeleton personalization, validated across 23 healthy adults. The framework employs compact controller parameterizations (4-8 parameters) suitable for real-time optimization, with transparent cost functions and traceable convergence. Limitations include healthy-adult validation, electrode placement sensitivity, and restricted activity scope. Extensions should address clinical populations, online adaptation to fatigue, and broader task coverage.","abstract_has_math":false,"creators":["Salvador Echeveste (23292001)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-01T00:00:00Z","date_published":"2025-12-01T00:00:00Z","updated_at":"2026-07-27T21:34:28Z","subjects":["Engineering","Robotics"],"languages":[],"rights":["In Copyright","Open Access after 2028-01-01"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.25417/uic.31451620.v1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Salvador Echeveste (23292001)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12-01T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/EMG-Based_Human-in-the-Loop_Bayesian_Optimization_to_Assist_Hip-Centric_Activities/31451620"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering","Robotics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright","Open Access after 2028-01-01"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25417/uic.31451620.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This dissertation presents a practical method for personalizing hip exoskeleton assistance using surface EMG-based human-in-the-loop optimization, cutting tuning time from hours to minutes while preserving assistance quality. We show that processed EMG provides a reliable objective for rapid personalization, enabling convergence within typical clinical sessions. The research progresses from simulation studies revealing fundamental controller-hardware gaps to experimental validation across three activities. In leg swinging (n=8), EMG-based optimization reduces muscle activity by 15-17\\% with <15 seconds of steady data per trial. In squatting (n=4), the method completes tuning in 4 minutes 40 seconds and yields 21\\% lower metabolic cost with 17\\% lower EMG. In walking (n=11), a multi-objective formulation balancing EMG and user preference identifies personalized controllers in 11-12 minutes, reducing metabolic cost by 14.9\\% while improving perceived exertion by 25-45\\%. Three technical innovations enable this speed: (i) a signal-enhancement pipeline combining Hankel decomposition, Bayesian regularization, and optimized smoothing that improves composite EMG quality metrics by 108\\%; (ii) machine-learning-guided initialization from anthropometric measurements that reduces convergence time by 26.5\\% and improves final performance by 9.98\\%; and (iii) heteroscedastic Gaussian process surrogates with Expected Hypervolume Improvement that capture input-dependent noise, improving predictive accuracy by 23-31%. Supporting investigations establish practical design principles. Systematic evaluation across 12 participants performing 30 conditions each reveals that simple amplitude summation provides the most reliable EMG-metabolic correlation (r=0.762), challenging assumptions about complex feature necessity. Simulation studies comparing 12 controller architectures demonstrate that phase-adaptive impedance achieves 55.5\\% mechanical power reduction with minimal parameters, while Bezier profiles reach 62.9\\% reduction at higher implementation cost. These results establish EMG-based HIL as a clinically feasible approach to exoskeleton personalization, validated across 23 healthy adults. The framework employs compact controller parameterizations (4-8 parameters) suitable for real-time optimization, with transparent cost functions and traceable convergence. Limitations include healthy-adult validation, electrode placement sensitivity, and restricted activity scope. Extensions should address clinical populations, online adaptation to fatigue, and broader task coverage."]},{"key":"dc:title","label":"Title","values":["EMG-Based Human-in-the-Loop Bayesian Optimization to Assist Hip-Centric Activities"]}]}],"canonical_facts":{"dc:creator":["Salvador Echeveste (23292001)"],"dc:date":["2025-12-01T00:00:00Z"],"dc:description":["This dissertation presents a practical method for personalizing hip exoskeleton assistance using surface EMG-based human-in-the-loop optimization, cutting tuning time from hours to minutes while preserving assistance quality. We show that processed EMG provides a reliable objective for rapid personalization, enabling convergence within typical clinical sessions. The research progresses from simulation studies revealing fundamental controller-hardware gaps to experimental validation across three activities. In leg swinging (n=8), EMG-based optimization reduces muscle activity by 15-17\\% with <15 seconds of steady data per trial. In squatting (n=4), the method completes tuning in 4 minutes 40 seconds and yields 21\\% lower metabolic cost with 17\\% lower EMG. In walking (n=11), a multi-objective formulation balancing EMG and user preference identifies personalized controllers in 11-12 minutes, reducing metabolic cost by 14.9\\% while improving perceived exertion by 25-45\\%. Three technical innovations enable this speed: (i) a signal-enhancement pipeline combining Hankel decomposition, Bayesian regularization, and optimized smoothing that improves composite EMG quality metrics by 108\\%; (ii) machine-learning-guided initialization from anthropometric measurements that reduces convergence time by 26.5\\% and improves final performance by 9.98\\%; and (iii) heteroscedastic Gaussian process surrogates with Expected Hypervolume Improvement that capture input-dependent noise, improving predictive accuracy by 23-31%. Supporting investigations establish practical design principles. Systematic evaluation across 12 participants performing 30 conditions each reveals that simple amplitude summation provides the most reliable EMG-metabolic correlation (r=0.762), challenging assumptions about complex feature necessity. Simulation studies comparing 12 controller architectures demonstrate that phase-adaptive impedance achieves 55.5\\% mechanical power reduction with minimal parameters, while Bezier profiles reach 62.9\\% reduction at higher implementation cost. These results establish EMG-based HIL as a clinically feasible approach to exoskeleton personalization, validated across 23 healthy adults. The framework employs compact controller parameterizations (4-8 parameters) suitable for real-time optimization, with transparent cost functions and traceable convergence. Limitations include healthy-adult validation, electrode placement sensitivity, and restricted activity scope. Extensions should address clinical populations, online adaptation to fatigue, and broader task coverage."],"dc:identifier":["10.25417/uic.31451620.v1"],"dc:relation":["https://figshare.com/articles/thesis/EMG-Based_Human-in-the-Loop_Bayesian_Optimization_to_Assist_Hip-Centric_Activities/31451620"],"dc:rights":["In Copyright","Open Access after 2028-01-01"],"dc:subject":["Engineering","Robotics"],"dc:title":["EMG-Based Human-in-the-Loop Bayesian Optimization to Assist Hip-Centric Activities"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T21:34:28Z"}