@book{
 title = {Intelligent control system for back pain therapy},
 type = {book},
 year = {2017},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 identifiers = {[object Object]},
 volume = {10339 LNAI},
 id = {afcba4cf-71d5-3bd2-a483-0cf98911a28f},
 created = {2018-03-02T09:25:13.985Z},
 file_attached = {false},
 profile_id = {93b02a20-88c2-31ac-b399-224e27b8cf85},
 group_id = {77ec4ef5-571b-3aa2-af2e-79d0f1c8040d},
 last_modified = {2018-03-02T09:25:14.082Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
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 abstract = {© Springer International Publishing AG 2017. Back pain is a pending subject in our society despite scientific advances. The Kazemi Back System (KBS) is a therapy machine that allows the patient to correctly perform manipulation exercises to heal or relieve pain. In this paper we describe and evaluate a CBR approach to suggest an stream of configuration values for the KBS machine based on previous sessions from the same patient or other similar patients. Its challenge is to capture the expertise knowledge of physiotherapists and reuse it for future therapies. The CBR system includes two complementary reuse processes and an explanation module. Within our experimental evaluation we discuss the problem of incompleteness and noise in the data and how to solve the cold start configuration for new patients.},
 bibtype = {book},
 author = {Recio-Garcia, J.A. and Díaz-Agudo, B. and Jorro-Aragoneses, J.L. and Kazemi, A.}
}

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