{"doi":"10.1109/iembs.2008.4649369","title":"A computationally light classification method for mobile wellness platforms","abstract":null,"journal":"2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","year":2008,"id":685573,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1791142,"name":"Jani Mantyjarvi","orcid":null,"position":1,"is_corresponding":false},{"id":1791143,"name":"Heidi Simila","orcid":null,"position":2,"is_corresponding":false},{"id":1791144,"name":"Juha Parkka","orcid":null,"position":3,"is_corresponding":false},{"id":1791145,"name":"Miikka Ermes","orcid":null,"position":4,"is_corresponding":false},{"id":1791140,"name":"Ville Kononen","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A computationally light classification method for mobile wellness platforms","abstract":"The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162872","pmcid":null,"openalex_id":null,"authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":null,"license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx5/4636107/4649055/04649369.pdf?arnumber=4649369","host_type":"publisher"}],"fields_of_study":[],"mesh_terms":["Humans","Diagnosis, Computer-Assisted","Monitoring, Ambulatory","Motor Activity","Algorithms","Decision Support Systems, Clinical","Pattern Recognition, Automated","Health Promotion"],"keywords":[],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-18T17:00:40.926500Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}