To study how to design different grades of individualized wheelchairs according to users' needs, a personalized wheelchair design method based on AHP and Kano model is proposed. The AHP model determines the relative importance of characteristics of customers' demands. The subfunctions of manual wheelchairs and their attributes are given. The weight coefficients are calculated. 20 experts (10 are the members of the research team, 5 are doctors, and 5 are wheelchair designers) are involved in the above two parts of the work. Kano model represents the types of user requirements. 30 participants' (wheelchair users) needs are divided into 5 categories: M, O, E, I, and R. According to the types of user needs and the weight of each subfunction, three manual wheelchair models are built. Traditional design method usually cannot satisfy the requirements of users and product structure, so this paper makes a contribution to solve this problem. The method can be used to design individualized wheelchairs which may improve the product quality and customers' satisfaction. Meanwhile it also can reduce the design time, thereby reducing the design cost.

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The process of intelligent interaction through brain machine interface requires quick and accurate extraction of Electroencephalogram (EEG) signals. However, the accuracy of signal classification varies with the signal extraction location. Is there a universal rule to follow to determine the optimal extraction location? This paper investigates the possibility of a universal rule to determine optimal extraction location through Welch, Support Vector Machine and Euclidean distance algorithms. The motor imagery EEG signals of 40 subjects were extracted and the classification correct rates of brain electrode signals in different positions were analyzed using Welch and Support Vector Machine algorithms. Then the electrodes were sorted according to the correct rate, and finally three pairs of electrodes with the highest correct rate were obtained. For comparison, this paper