- 著者
- 齋藤 弘匡 宮崎 祐太 杉 正明
- 出版者
- 一般社団法人 日本作業療法士協会
- 雑誌
- 作業療法 (ISSN:02894920)
- 巻号頁・発行日
- vol.42, no.5, pp.595-603, 2023-10-15 (Released:2023-10-15)
- 参考文献数
- 48
本研究の目的は訪問リハビリテーション利用者を対象に,被介護者の抑うつ状態と介護者の介護負担感の関連を明らかにすることである.被介護者の基本情報,抑うつ度,認知機能,ADL自立度,生活空間の広がり,介護者の基本情報を調査し,介護負担感との関連を検討した.重回帰分析により,被介護者の抑うつ度の高さ,排便管理の自立度の低さが介護負担感の重要な因子として抽出され,介護負担感軽減のための介入には,被介護者の抑うつ状態を考慮する重要性が示唆された.
- 著者
- 柳川 由紀子 越後 富夫 宮崎 祐太 武村 紀子 八木 康史
- 出版者
- 一般社団法人 人工知能学会
- 雑誌
- 人工知能学会論文誌 (ISSN:13460714)
- 巻号頁・発行日
- vol.33, no.6, pp.C-I33_1-12, 2018-11-01 (Released:2018-11-01)
- 参考文献数
- 51
Tracking precisely of abnormalities in the gastrointestinal tract is useful for preparing sample image sequences on educational training for medical diagnose on endoscopy. While the gastrointestinal wall deforms continuously in an unpredictable manner, however, abnormalities without distinctive features make it difficult to track over continuous frames. To address this problem, the proposed method employs Convolutional neural networks (CNN) for tracking lesion area. Conventionally, CNN for tracking requires a large amount of sample data for preliminary learning. The state-of-arts tracking methods using CNN are premised on preliminary learning on data similar to target images given a large number of correct answer labels. On the other hand, the proposed method are not required preliminary learning using similar data. The image components in the marked region at the starting frame is similar to components at the only same position, but different between them depending on the degree of overlapped area. Furthermore, in the successive frame, the components in the previous region is similar to them in the identified area. Therefore, similarity can be learned in the previous frame, called it as an intra-frame training. This paper describes the method for tracking an abnormal region by using CNN based on training overlap rates between the abnormal region and local scanning one with the same size on the starting intra-frame. Furthermore, network parameters are transformed from training the similar regions on the continuous frame additionally. We demonstrate the efficiency of the proposed approach using eight common types of gastrointestinal abnormality.