FingerTac -- An Interchangeable and Wearable Tactile Sensor for the Fingertips of Human and Robot Hands

Skill transfer from humans to robots is challenging. Presently, many researchers focus on capturing only position or joint angle data from humans to teach the robots. Even though this approach has yielded impressive results for grasping applications, reconstructing motion for object handling or fine manipulation from a human hand to a robot hand has been sparsely explored. Humans use tactile feedback to adjust their motion to various objects, but capturing and reproducing the applied forces is an open research question. In this paper we introduce a wearable fingertip tactile sensor, which captures the distributed 3-axis force vectors on the fingertip. The fingertip tactile sensor is interchangeable between the human hand and the robot hand, meaning that it can also be assembled to fit on a robot hand such as the Allegro hand. This paper presents the structural aspects of the sensor as well as the methodology and approach used to design, manufacture, and calibrate the sensor. The sensor is able to measure forces accurately with a mean absolute error of 0.21, 0.16, and 0.44 Newtons in X, Y, and Z directions, respectively

Hierarchical meso/macro-porous carbon fabricated from dual MgO templates for direct electron transfer enzymatic electrodes

We designed a three-dimensional (3D) hierarchical pore structure to improve the current production efficiency and stability of direct electron transfer-type biocathodes. The 3D hierarchical electrode structure was fabricated using a MgO-templated porous carbon framework produced from two MgO templates with sizes of 40 and 150 nm. The results revealed that the optimal pore composition for a bilirubin oxidase-catalysed oxygen reduction cathode was a mixture of 33% macropores and 67% mesopores (MgOC33). The macropores improve mass transfer inside the carbon material, and the mesopores improve the electron transfer efficiency of the enzyme by surrounding the enzyme with carbon

The Biosynthesis of Capuramycin-type Antibiotics: Identification of the A-102395 Biosynthetic Gene Cluster, Mechanism of Self-Resistence, and Formation of Uridine-5\u27-Carboxamide

A-500359s, A-503083s, and A-102395 are capuramycin-type nucleoside antibiotics that were discovered using a screen to identify inhibitors of bacterial translocase I, an essential enzyme in peptidoglycan cell wall biosynthesis. Like the parent capuramycin, A-500359s and A-503083s consist of three structural components: a uridine-5\u27-carboxamide (CarU), a rare unsaturated hexuronic acid, and an aminocaprolactam, the last of which is substituted by an unusual arylamine-containing polyamide in A-102395. The biosynthetic gene clusters for A-500359s and A-503083s have been reported, and two genes encoding a putative non-heme Fe(II)-dependent α-ketoglutarate:UMP dioxygenase and an l-Thr:uridine-5\u27-aldehyde transaldolase were uncovered, suggesting that C-C bond formation during assembly of the high carbon (C6) sugar backbone of CarU proceeds from the precursors UMP and l-Thr to form 5\u27-C-glycyluridine (C7) as a biosynthetic intermediate. Here, isotopic enrichment studies with the producer of A-503083s were used to indeed establish l-Thr as the direct source of the carboxamide of CarU. With this knowledge, the A-102395 gene cluster was subsequently cloned and characterized. A genetic system in the A-102395-producing strain was developed, permitting the inactivation of several genes, including those encoding the dioxygenase (cpr19) and transaldolase (cpr25), which abolished the production of A-102395, thus confirming their role in biosynthesis. Heterologous production of recombinant Cpr19 and CapK, the transaldolase homolog involved in A-503083 biosynthesis, confirmed their expected function. Finally, a phosphotransferase (Cpr17) conferring self-resistance was functionally characterized. The results provide the opportunity to use comparative genomics along with in vivo and in vitro approaches to probe the biosynthetic mechanism of these intriguing structures

人型多指ロボットハンドの分布型3軸触覚センサを用いた畳み込みニューラルネットワークによる物体操りと物体認識の実現

早大学位記番号:新8740早稲田大

Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection