This paper proposes a gating algorithm that employs the successively compensated norm ratio SΦ_N^+g to separate a submerged pulse wave from a body movement artifact without the need to designate an artifact pulse segment that is measured over two wavelengths (denoted by IR, R) of SpO₂. This algorithm uses predetermined rules to classify IR,R signals into a pulse wave and an artifact. One disadvantage of using an inverse matrix to separate the pulse signal is that it can generate spikes and oscillations at the ill-condition for the separation matrix. It is thus important to apply a relaxation algorithm at the ill-condition. The proposed gating algorithm opens and closes the gate to automatically separate the pulse segment and the artifact segment according to pre-determined rules. It therefore enables more flexible Φ_N^true tracking. The proposed gated successively compensated norm ratio SΦ_N^+g results in improved separation of the pulse wave (H = 0.0382) without the need for precise Φ_N adjustment than the separation obtained using Φ_N (H = 0.2781). This algorithm is also effective when there are other body movement artifacts present. This algorithm can not only be applied to two-wave pulse photometry, but it can be also be extended to multi-wave pulse photometry.

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This paper describes a human forearm motion discrimination method by the concise neural network for myoelectric hand control. In the conventional studies, the neural network is often used to estimate the motion intention by the myoelectric potential and realize the high discrimination precision. However, the feature extraction processing of the myoelectric potential and the neural network structure tends to become complicated. Therefore, they are not suitable due to the calculation amount increase because the myoelectric hand system is difficult to be equipped with a high-speed CPU. The purpose of this study is to obtain the high discrimination precision without complicating the neural network structure. This study proposes the optimization method of "input data" to Neural Network and "data for learning". Therefore, the total system will be concise and inexpensive. In addition, this study proposes the robust relearning method against the myoelectric potential change by the muscle fatigue. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

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Machines and equipment to improve the healthy daily life of elderly people are becoming increasingly important. Resistance training equipment is especially effective for muscle atrophy prevention, not only in healthy adult people, but also in elderly people and even disabled individuals. Reportedly, training for elderly people is safe, and it ameliorates cardiovascular disease risk factors. Based on a new concept to strengthen disused muscles and to enable whole-body exercise in seniors, we developed a device using power rehabilitation technology. This study developed a new rowing machine that elderly people can use for safe and effective rehabilitation exercise.

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In late years, the patient load of chronic respiratory failure disease, such as a Obstructive Pulmonary Disease (COPD) or Sleep Apnea Syndrome (SAS) increaseschronic obstructive pulmonary disease (COPD) and sleep apnea syndrome (SAS), is increasing. For the patient, in whom such condition is comparatively serious, non-invasive positive pressure ventilation (NPPV) may be prescribed. Since this disposal method uses a nasal mask, a patient is treated non-invasively. Although, It is reported that skin obstacles arise, such as a pressure necrosis and compression gangrene, by the reason the mask pressed the face.
Consequentially, it was created that the finite element model of the face portion based on a human body slice picture, and the stress which acts on the contact area of a nasal mask was calculated. And, It turned out that a nasal mask suitable for the form of an individual face is required, in order to reduce a skin obstacle.

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