Author name cluster

Isamu Kajitani

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4 papers

2 author rows

IROS Conference 2018 Conference Paper

Evaluating Robotic Devices of Non-Wearable Transferring Aids Using Whole-Body Robotic Simulator of the Elderly

Yoshio Matsumoto
Kunihiro Ogata
Isamu Kajitani
Keiko Homma
Yujin Wakita

This paper describes the development of a whole-body robotic simulator of an elderly person for evaluating robotics devices for nursing care. To improve the quality of life of the elderly persons, physical assistance such as transfer, movement, and bathroom assistance is important. It is also important to reduce the workload of caregivers in an aging society. In recent years, assistive robotic devices for nursing care have been developed and commercialized for such purposes. However, such devices have not become popular in the care facilities yet. One of the reasons is that it is still difficult to evaluate the effects of the devices on the care receivers and caregivers. In particular, it is necessary to quantitatively evaluate the effect of the devices on the human body from the viewpoint of safety and comfort. We have developed a whole-body robotic system to simulate the pose and motion of the elderly persons. The purpose of this system is to realize quantitative physical evaluation of robotics devices for nursing care of the human body. The experimental results of the preliminary evaluation of assistive robotic devices are also presented.

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ICRA Conference 2013 Conference Paper

Humanoid robot as an evaluator of assistive devices

Kanako Miura
Eiichi Yoshida
Yoshiyuki Kobayashi
Yui Endo 0001
Fumio Kanehiro
Keiko Homma
Isamu Kajitani
Yoshio Matsumoto

This paper presents a basic study on feasibility of usage of humanoid robots as an evaluator of assistive devices, by taking advantage of its anthropomorphic shape. In this new application humanoid are expected to help evaluation through quantitative measures, which is difficult with human subjects, and also to reduce the burden coming from ethical concerns with costly tests by human subjects. Taking a passive supportive wear “Smart Suit Lite” designed to relieve the load at lower back as an example, we have conducted pilot experiments by using the humanoid robot HRP-4C. The motion to be performed by the humanoid is obtained through retargeting technique from measured human lifting motion. The supportive effect is first estimated by simulation taking into account the mechanism of the supportive device. The experimentation of humanoid hardware brought us encouraging results on the basic feasibility of this application, as we observed a clear decrease of the torque for lifting when wearing the device as expected by the simulation.

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AAAI Conference 1999 Conference Paper

An Evolvable Hardware Chip and Its Application as a Multi-Function Prosthetic Hand Controller

Isamu Kajitani
Tsutomu Hoshino
University of Tsukuba; Nobuki Kajihara
Adaptive Devices NEC Laboratory
RWCP; Masaya Iwata
Tetsuya Higuchi
Electrotechnical Laboratory

Thispaperdescribesthe applicationof geneticalgorithmsto the biomedicalengineering problem of a multi-function myoelectricprosthetic hand controller. This is achievedby an innovativeLSI chip (EHW chip), i. e. , a VLSIimplementation of Evolvable Hardware(EHW), whichcan adapt its owncircuit structure to its environment autonomouslyand quickly by using genetic algorithms. Usually, a long training period (almost onemonth)is required beforemulti-functionmyoelectric prosthetic handscan be controlled, however, the EHW chip controller developed here can reducethis period andit has beendesignedfor easy implementationwithin a prosthetic hand. Thereare plans to commercialize the prosthetic hand with the EHW chip, and the medical departmentof Hokkaido Universityhas already decidedto adoptthis for clinical treatment.

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IJCAI Conference 1997 Conference Paper

Evolvable Hardware for Generalized Neural Networks

Masahiro Murakawa
Shuji Yoshizawa
Isamu Kajitani
Tetsuya Higuchi

This paper describes an evolvable hardware (EHW) system for generalized neural network learning. We have developed an ASIC VLSI chip, which is a building block to configure a scalable neural network hardware system. In our system, both the topology and the hidden layer node functions of a neural network mapped on the chips are dynamically changed using a genetic algorithm. Thus, the most desirable network topology and choice of node function (e. g. Gaussian or sigmoid) for a given application can be determined adaptively. This approach is particularly suited to applications requiring ability to cope with time-varying problems and real-time timing constraints. The chip consists of 15 Digital Signal Processors (DSPs), whose functions and interconnections are reconfigured dynamically according to the chromosomes of the genetic algorithm. Incorporation of local learning hardware increases the learning speed significantly. Simulation results on adaptive equalization in digital mobile communication are also given. Our system is two orders of magnitude faster than a Sun SS20 on the corresponding problem.

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