押しスイッチの高さと身体肢位との関係

　In occupational therapy, we are often involved in designing housing reforms that are comfortable for impaired people. Moreover, occupational therapists should evaluate the disability of each subject to determine a suitable height for the switch operation for the impaired. However, due to some circumstances the switch is not always in the appropriate location. It is necessary to clarify the height of the switch so that it can be easily operated while in various postures. Therefore, we focused on the pushing motion of the wall switch placed at heights of 60cm, 80cm, and 100cm, and aimed to find a suitable height for the switch in the standing and sitting positions by measuring the load on the legs, the muscle activity and the force on the switch. As a result, the lowest weight ratio was found at 100cm in the sitting position during the load shift on the left/right foot before the switch operation. The lowest percentage of maximum voluntary contraction (%MVC) was found at 100cm from the deltoid muscle (Del.), right and left erector spinae (Rt-ES, Lt-ES) in the standing position. Rt-ES and Lt-ES had the same tendency in the sitting position. The smallest force applied on the switch was found at 100cm in the standing position (p＜0.05) and the sitting position had the same tendency. There was no significant difference or relevance in the relationship between the time to start the muscle activity and the time to start the switch operation. For both sitting and standing positions, the muscle activity and the shift ratio of the foot load were small when the switch was at the height of 100cm, and the force applied to the switch was also small. Therefore, the appropriate height for the switch is at 100cm in both sitting and standing positions. 作業療法では障害者が使いやすい住宅の改修に関わることが多い。そして障害の状態に 応じた操作スイッチの高さの設定は作業療法士が対象者個人の状態を評価し設定しなけれ ばならない。しかし、スイッチの位置は適切な位置にあるとは限らず、各姿勢で容易に操 作できるスイッチの高さを明らかにする必要がある。そこで我々は、壁面にあるスイッチ を押す動作に着目し、スイッチの高さを60cm、80cm、100cmに設定し、下肢荷重、筋活動 （橈側手根屈筋（FCR）、三角筋（Del.）、左右脊柱起立筋（Rt-ES, Lt-ES））、スイッチにかか る力を測定し、座位と立位における適切なスイッチ高さを示すことを目的とした。その結 果、スイッチ操作開始までの左右足の移動で座位と立位で100cmが最も低い体重比であっ た（p＜0.05）。筋活動（％MVC）は立位ではDel., Rt-ES, Lt-ESにおいて100cmが最も少な かった（p＜0.05）。座位でもRt-ESとLt-ESに同じ傾向がみられた。スイッチに係る力は立 位では100cmが最も小さな力だった（p＜0.05）。座位でも同じ傾向がみられた。筋活動開始 時間とスイッチ操作開始時間の関係は有意差と交互作用は認められなかった。高さ100cm のスイッチの位置は座位でも立位でも足部荷重の変化が少なく、脊柱への筋の負担も少な かった。そして小さな力でスイッチを押せる高さであった。さらにスイッチ操作開始まで の時間に対しては、姿勢とスイッチの高さに影響は認められなかった。これらのことから 座位でも立位でも対応できる適切なスイッチの高さは100cmであることが示唆された。[原著

押しスイッチの高さと身体肢位との関係

取得学位 : 博士（保健学）, 学位授与番号 : 医博甲第2195号 , 学位授与年月日 : 平成23年3月22日, 学位授与大学 : 金沢大学, 審査結果の報告日 : 平成23年2月10

骨格筋アセチルコリンレセプター・クラスターに対する免疫および薬理学的因子の影響

取得学位 : 博士（医学）, 学位授与番号 : 医博甲第1035号, 学位授与年月日：平成4年3月31日,学位授与年：199

Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability

Consistent map building in petrochemical complexes for frefghter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of frefghter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle flters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The diference between the original FastSLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was signifcant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refnery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability