A Study on Standard for Safety Requirements for Care Robots.

Recently, care robots are being developed that incorporate robotics into assistive products that focus on daily care for the physically disabled or elderly with reduced physical function. However, although care robots can reduce the physical burden of human intervention, they can also be dangerous depending on their situational awareness. This study describes a standardization that defines safety requirements for care robots and includes verification methods to test their safety requirements. As an example of the application of this standard, a standard for the safety and performance method of a feeding robot is shown. This standardization study is expected to contribute to the spread of care robots in the future.

Opening the GATE: systems thinking from the global assistive technology alliance.

Purpose: This paper describes international actions to collaborate in the assistive technology (AT) arena and provides an update of programmes supporting AT globally. Methods: The World Health Organisation (WHO) identifies the severe global uneven distribution of resources, expertise and extensive unmet need for AT, as well the optimistic substantial capability for innovations and developments in appropriate and sustainable AT design, development and delivery. Systems thinking and market shaping are identified as means to address these challenges and leverage the ingenuity and expertise of AT stakeholders. Results: This paper is a 'call to action', showcasing emerging AT networks as exemplars of a distributed, but integrated mechanism for addressing AT needs globally, and describing the Global Alliance of Assistive Technology Organisations (GAATO) as a vehicle to facilitate this global networking. Conclusion: Partners in this Global Alliance aim to advance the field of assistive technology by promoting shared research, policy advocacy, educating people and organisations within and outside the field, teaching, training and knowledge transfer by pulling together broad-based membership organisations.Implications for RehabilitationCollegial, cross discipline and multi-stakeholder collaborations support assistive technology research and practice.Knowledge exchange within and across countries and regions is mutually beneficial.Self-organising assistive technology communities are emerging and supported by global movements such as WHO GATE and GAATO.

Evaluation and Verification of A Novel Wrist Rehabilitation Robot employing Safety-related Mechanism.

In this paper, we propose a wrist rehabilitation robot employing a novel actuation mechanism composed of electromagnetic clutch, brake, and motor and a safe-related mechanism. The actuation mechanism of the robot can perform both passive and active rehabilitation by the same mechanism. A torque sensor is also coupled to the actuation mechanism so as to measure the wrist joint moment in real-time. User can align his/her wrist joint to the center of rotation with the indication device. Moreover EMG is measured in real-time to evaluate effectiveness of active rehabilitation and to detect an emergency situation such as muscle spasm while doing active rehabilitation. In experiments, the effectiveness of active rehabilitation is evaluated and verified by 3D motion capture data and EMG data measured in real-time. The feasibility of the indication device is evaluated in the wrist joint alignment by verifying X-ray image of wrist and robot. Finally, we performed a usability test with five experts working in medical field, and the test result showed that the proposed robot can be applied to wrist rehabilitation.

A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.

Biomechanical assessment of electric lifting chair for persons with disability.

Electric lifting chair is a typical assistive product to aid for standing up and sitting down for persons with disability, and it is particularly useful for the elderly persons whose muscular system is weakened by degenerative joint disease. This paper describes a biomechanical assessment of electric lifting chair with hip-up function. In experiments we measured 3D motion data and electromyogram (EMG) on the femoral muscle when subject performs the standing motion on the predetermined seat height. The experimental results show that 15 degree of the hip-up angle is adequate for the hip-up function, and that the higher seat position is more effective to assist for standing up motion.

A supervised feature projection for real-time multifunction myoelectric hand control.

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMG pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMG signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron (MLP) classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time control system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the myoelectric hand control, are completed within 97 msec.