苍白
发表于 2025-3-23 11:05:37
Tridimensional Motion Analysis of Trunk Versus Pelvis Movement in Hip Abductor Lurchulation of the pelvis and thorax were measured by a combination of a light emission diode-position sensor detector (LED-PSD) optical system and gyrosensors. These data were analyzed with the authors’ original programs. One hundred and sixty-one patients (545 tests) consisting of 94 normal controls (
驾驶
发表于 2025-3-23 14:54:37
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漫不经心
发表于 2025-3-23 20:31:05
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Inscrutable
发表于 2025-3-23 22:56:55
Gait Analysis of Unilateral Total Hip Replacement: Quantitative Analysis of the Vertical Floor Reactrthrosis of the hip joint. Ten Charnley’s low friction arthroplasties and 21 Omnifit systems were implanted from 1984 to 1990, and the mean follow-up period was 5.0 years. Gait studies were conducted on a walkway to measure the floor reaction force at free speed without the use of canes or crutches.
不理会
发表于 2025-3-24 05:18:53
—Overview— Clinical Gait Analysis in Hip Patientsl accelerometry and reaction force measurement. The vertical reaction force directly represents the kinematic acceleration of vertical body movement. The abnormal trunk movement of hip patients was investigated with a TV motion analysis system. Patients with lateral trunk bending and shifting showed
Esalate
发表于 2025-3-24 10:11:51
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CANE
发表于 2025-3-24 12:34:33
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哪有黄油
发表于 2025-3-24 15:24:42
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Accrue
发表于 2025-3-24 21:17:48
Kazuhiko Sakamoto,Yoshinobu Hara,Akira Shimazu,Kenji Hirohashiep Neural Network (DNN). However, because it takes a long time to sample DNN’s output for calculating its distribution, it is difficult to apply it to edge computing where resources are limited. Thus, this research proposes a method of reducing a sampling time required for MC Dropout in edge computi
装入胶囊
发表于 2025-3-25 00:08:59
Takatoshi Ide,Yasuhiro Yamamoto,Shigeru Tatsugiics is to capture the relationship between the covariates and the event time distribution. In this paper, we propose a novel network-based approach to survival analysis, called DPWTE, that uses a neural network to learn the distribution of the event times. DPWTE makes an assumption that (individual)