著者

Toru Yasuda Sirinda Jaotawipart Hironobu Kuruma

出版者

The Japanese Association of Rehabilitation Medicine

雑誌

Progress in Rehabilitation Medicine (ISSN:24321354)

巻号頁・発行日

vol.8, pp.20230022, 2023 (Released:2023-07-22)

参考文献数

33

被引用文献数

1

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Objectives: This study used magnetic resonance imaging (MRI) to investigate the effects of thoracic spine self-mobilization on patients with low back pain (LBP) and lumbar hypermobility.Methods: Twenty-four patients (15 men, 9 women) with LBP were randomly allocated to a thoracic spine self-mobilization group or sham group. The thoracic spine self-mobilization group performed thoracic spine active flexion and extension activities using two tennis balls fixed with athletic tape. Outcome measures were collected pre-intervention and after 4 weeks and included the Visual Analog Scale (VAS) for pain, the Oswestry Disability Index, lumbar rotation angle measured using MRI taken in the lateral position with 45° of trunk rotation, thoracolumbar rotation range of motion (ROM) in the sitting position, and stiffness of the erector spinae muscles. The effects of the intervention were analyzed using two-way repeated-measures analysis of variance (ANOVA), followed by multiple comparisons. The significance level was set at 5%.Results: The results of the two-way repeated measures ANOVA indicated that the main effect of the group was significant (P<0.05) for VAS, the sum of the lumbar rotation angle, and the thoracolumbar rotation ROM. A significant group-by-time interaction was found for the sum of lumbar rotation angles. The results of the multiple comparison tests for VAS, sum of the lumbar rotation angle from L1 to S1, and thoracolumbar rotation ROM were significantly different after 4 weeks.Conclusions: This study revealed a decrease in lumbar segmentation after thoracic spine mobilization. Thoracic spine mobilization may be effective in patients with LBP and hypermobility.

著者

Naoto SATO Hironobu KURUMA Yuichiroh NAKAGAWA Hideto OGAWA

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E103-D, no.2, pp.363-378, 2020-02-01

被引用文献数

7

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As one type of machine-learning model, a “decision-tree ensemble model” (DTEM) is represented by a set of decision trees. A DTEM is mainly known to be valid for structured data; however, like other machine-learning models, it is difficult to train so that it returns the correct output value (called “prediction value”) for any input value (called “attribute value”). Accordingly, when a DTEM is used in regard to a system that requires reliability, it is important to comprehensively detect attribute values that lead to malfunctions of a system (failures) during development and take appropriate countermeasures. One conceivable solution is to install an input filter that controls the input to the DTEM and to use separate software to process attribute values that may lead to failures. To develop the input filter, it is necessary to specify the filtering condition for the attribute value that leads to the malfunction of the system. In consideration of that necessity, we propose a method for formally verifying a DTEM and, according to the result of the verification, if an attribute value leading to a failure is found, extracting the range in which such an attribute value exists. The proposed method can comprehensively extract the range in which the attribute value leading to the failure exists; therefore, by creating an input filter based on that range, it is possible to prevent the failure. To demonstrate the feasibility of the proposed method, we performed a case study using a dataset of house prices. Through the case study, we also evaluated its scalability and it is shown that the number and depth of decision trees are important factors that determines the applicability of the proposed method.