This paper proposes a novel sitting assistance robot, which considers the posture tolerance of its user. The standing and sitting motion are different essentially because the standing is lifting motion against gravitational force and sitting is posture coordination to sitting position according to the gravity. Therefore, the robot should lead the patient’s posture within a stable range during sitting and the required performance is different from standing assistance. However, in previous studies, conventional assistive robots used the sitting motion which is “reverse” motion of standing. Furthermore, these robots helped patients by using a fixed motion reference pathway in spite of their original intention, and as the results, these robots failed to assist by confliction between their intended motion and reference path. Therefore, we propose a novel sitting assistance robot, which allows its user to move their body within a prescribed degree of posture tolerance during the process of moving from a standing to a sitting position. Our key findings cover two fundamental research topics. One is the investigation into posture tolerance during a sitting motion. The other topic is a novel assistance control algorithm that considers the investigated posture tolerance by combining position control and force control. A prototype assistive robot, based on the proposed idea was fabricated to help patients sitting down safely according to their original intention.

This paper proposes a novel standing assistance scheme that guides a subject to adopt a suitable posture using intended movements. In much previous research, an assistive robot requires the patient to understand the standing reference beforehand and generally, the subject learns the standing motion used by the robot. However, for practical use, a patient should be able to use an assistive robot without special prior knowledge. Nursing specialists also guide their patients with an intended movement. For example, when they incline a patient's upper body before lifting it up, they guide the patient's body with a special moving pattern. Moving in this way is based on their experience and expertise and, in fact, the patient moves according to the motion intended by the nursing specialist. In this paper, we investigate what type of motion guides a patient, and we develop a standing assistance scheme that guides the patient to stand in the intended way. The effectiveness of the proposed method is verified by experiments with our prototype.

Various assistive machines have been developed to prevent falling accidents of the elderly. In order to achieve advanced support using robot technology, it is important to acquire data or real-time state estimation of user's various motions. However, a lot of expensive and sophisticated sensors utilized to estimate user's state accurately are difficult to use in general households or institutions. In this article, we propose a method to estimate the user's state utilizing a few inexpensive and simple sensors. We focused on CoG (Center of Gravity) to estimate user's state, but when utilizing less sensors than required to calculate the human link model parameters, the position of CoG is underspecified. Then we considered the range of value of unknown parameters to calculate candidates of CoG. The range of CoG candidates can become narrow enough to estimate human state in real-time by properly selecting and placing the sensors. Therefore, the evaluation of CoG candidates allows us to determine where and which sensors to set when designing assistive robots. We firstly selected some sensors which can be generally found on assistive machines, and we created sets of measurements using the number of unknown parameters. From the result of the experiment using a motion capture system, we confirmed that the range of the candidates was considerably narrow when using some of the created measurement sets. We validated the proposed method to estimate user's CoG candidates by actually placing the sensors according to the designed measurement sets and confirmed that the CoG candidates corresponded to those obtained using the motion capture system.

This study proposes a pattern based standing assistance on a robotic walker with a standing and sitting assistance function. In our previous works, we have developed a standing assistance system. Our assistive system consists of a powered walker and a standing assistance manipulator, and is small enough to be used in a typical narrow room such as a bathroom. Our system focuses on domestic use for elderly people who need nursing in their day-to-day lives. Usually, the body conditions of elderly users are different individually and required standing assistance schemes are also different. However, it is difficult to design the suitable standing assistance scheme according to the symptom of elderly users individually at real time because of its cost. Therefore, the proposed walker has several standing assistance patterns which are designed for the user of the different symptom and selects the suitable one for its user. Our idea is practicality for implementation with low cost. The performance of our proposed system was verified through experiments using our prototype with elderly and handicapped subjects.

In this paper, we propose a method to assess an extent of anomaly state of human using a walker-type support system. The elderly and the handicapped people use the walker-type support system to keep their balance and support their weight. Although the walker-type support system is easy to move based on the applied force of the user, several accidents such as falling and colliding with the obstacle have been reported. The anomaly state that causes a severe injury of the user should be detected before accident and the walker-type support system should prevent such accidents. In this paper, we focus on assessing the extent of the anomaly state of the user based on the statistical analysis of the applied force of the user. This research models the applied force of the user in real time by using the Gaussian Mixture Model (GMM), and we determine each parameter of GMM statistically. In addition, we assess the extent of the anomaly state of the user by using the Hellinger score, which can compare the data set of the normal state with that of anomaly state. The proposed method is applied to developed walker-type support system with simple force sensor, and we conduct the experiments in the several walking states and the environmental conditions.