{"id":"W2337546824","doi":"10.1109/percomw.2016.7457169","title":"From smart to deep: Robust activity recognition on smartwatches using deep learning","year":2016,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":175,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Smartwatch; Activity recognition; Computer science; Wearable computer; Deep learning; Artificial intelligence; Machine learning; Context (archaeology); Wearable technology; Pipeline (software); Human–computer interaction; Embedded system","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":[0.0002758829,0.0006866195,0.0004757334,0.0003201276,0.0001502629,0.0005110581,0.0008309207,0.0004688475,0.00268135],"category_scores_gemma":[0.001210345,0.0002729568,0.0004364706,0.0004192542,0.0002673477,0.001218183,0.001028424,0.0008631019,0.001215959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004323997,"about_ca_system_score_gemma":0.0005008563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003732212,"about_ca_topic_score_gemma":0.007080776,"domain_scores_codex":[0.9997901,0.00003885828,0.000008997095,0.00008395455,0.00004319311,0.0000349375],"domain_scores_gemma":[0.9997655,0.00008875194,0.00002422024,0.00005797471,0.0000409712,0.00002264322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005571308,0.000366087,0.007973467,0.0002516219,0.0001787407,0.0002664135,0.0002028082,0.26736,0.04385921,0.0042204,0.009579556,0.6651846],"study_design_scores_gemma":[0.00001097936,0.00008968284,0.003002281,0.00001834785,0.00001312182,0.00004323218,0.00004183918,0.980258,0.01024325,0.004721079,0.001545721,0.00001250005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1887518,0.0006281497,0.7954708,0.0004995836,0.0001316886,0.00009984671,0.001259273,0.008976689,0.004182109],"genre_scores_gemma":[0.8563996,0.0002997064,0.1366799,0.0002887392,0.00002752809,0.0001253332,0.001793058,0.0001793756,0.00420679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003732212,"threshold_uncertainty_score":0.008969963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07025401121407453,"score_gpt":0.306682747891044,"score_spread":0.2364287366769695,"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."}}