{"id":"W2601691888","doi":"10.1007/978-3-319-56148-6_22","title":"Investigation of the Feasibility of Strain Gages as Pressure Sensors for Force Myography","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Electrical impedance myography; Strain gauge; Wearable computer; Computer science; Gesture; Pressure sensor; Artificial intelligence; Gesture recognition; Simulation; Embedded system; Electrical engineering; Mechanical engineering; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001429136,0.0004948711,0.0002576097,0.0004752122,0.0001902344,0.0006977524,0.000735152,0.001043255,0.003046622],"category_scores_gemma":[0.003366656,0.0004224422,0.0002505415,0.0002457417,0.0005811221,0.001083881,0.0005063494,0.0003892104,0.0006781811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001312458,"about_ca_system_score_gemma":0.0002779536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003832699,"about_ca_topic_score_gemma":0.000421045,"domain_scores_codex":[0.9992478,0.0002918769,0.00002618518,0.0001451082,0.0002349286,0.00005412486],"domain_scores_gemma":[0.9978842,0.001670698,0.00005527551,0.0001047293,0.0002333682,0.00005166528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002851997,0.0004274826,0.007774135,0.0004066768,0.00005048759,0.0006437646,0.0003341977,0.001167893,0.8215393,0.002370887,0.0004718746,0.1619613],"study_design_scores_gemma":[0.0002940185,0.0161394,0.06058051,0.0002901962,0.0003445045,0.005626575,0.0009044609,0.04407517,0.8500779,0.002822773,0.01871862,0.0001258355],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8327401,0.01682686,0.1349699,0.0005573739,0.0003071314,0.0003183812,0.0002544794,0.0002148186,0.01381095],"genre_scores_gemma":[0.9164961,0.004765738,0.07025273,0.0001322911,0.00007594714,0.00009028292,0.0001783826,0.00005201985,0.007956533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003046622,"threshold_uncertainty_score":0.01019198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03021641326833361,"score_gpt":0.2537297141970851,"score_spread":0.2235133009287515,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}