{"id":"W4206727444","doi":"10.1088/2634-4386/ac4c38","title":"Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications","year":2022,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Horizon 2020 Framework Programme; Electronic Components and Systems for European Leadership; European Commission","keywords":"Neuromorphic engineering; Computer science; Activity recognition; Hyperparameter; Artificial intelligence; Edge computing; Enhanced Data Rates for GSM Evolution; Artificial neural network; Wearable computer; Machine learning; Classifier (UML); Edge device; Software deployment; Energy consumption; Human–computer interaction; Embedded system; Engineering; Software engineering","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":[0.0002191192,0.000280094,0.0002154495,0.0002080179,0.0001142501,0.0005609831,0.0006526333,0.0005108353,0.002051112],"category_scores_gemma":[0.0007011978,0.00008522354,0.0001725306,0.0001951433,0.0001985757,0.0004979845,0.0003648079,0.0003114215,0.0003794307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002796452,"about_ca_system_score_gemma":0.0002757343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008581174,"about_ca_topic_score_gemma":0.001082233,"domain_scores_codex":[0.9999,0.00002413793,0.000006387539,0.00002679296,0.00002562524,0.00001703277],"domain_scores_gemma":[0.9998699,0.00004370349,0.00001567501,0.00002023399,0.00003864238,0.00001171299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003387726,0.0002844662,0.002591198,0.0001955137,0.00009339314,0.0002495172,0.00007588798,0.3710614,0.1354792,0.01199177,0.002355071,0.4752837],"study_design_scores_gemma":[0.000007505457,0.0001172206,0.0007912802,0.00001798758,0.00001548483,0.00009408383,0.00001962854,0.9712947,0.02187443,0.004218439,0.001541442,0.000007694098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1737674,0.001163535,0.8095675,0.001002094,0.0001501594,0.00008852303,0.0001410966,0.001040703,0.01307898],"genre_scores_gemma":[0.9396216,0.0002619795,0.05657517,0.0001853664,0.00002754298,0.00003866627,0.00005063814,0.00003442153,0.003204648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002051112,"threshold_uncertainty_score":0.006861687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05506559468370577,"score_gpt":0.245333075403273,"score_spread":0.1902674807195673,"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."}}