With increasing demands for rehabilitation, the need for physical therapy robots is also increasing. This paper proposes the construction of a rehabilitation database inspired with medical cloud technologies. We discuss the possibility of establishing a new validation methodology by generating a database based on the data collected using rehabilitation equipment. In this research, data was obtained by rehabilitation equipment and statistical processing was applied to the data to investigate an example of a validation method. The experimental results suggest that improvement of tracking property of subjects is much larger than improvement of maneuverability.

The Range of Motion (ROM) is an important index of physical and occupational therapy, although getting quantitative results requires much time and effort. This paper proposes a method for measuring the ROM on a 2-dimensional plane by introducing a rehabilitation robot. One advantage is that quantitative measurement is easily done. This paper also proposes the concept of resistancemovementROM, which indicates the extent of movement under load and resistance. This method makes it possible to observe the distribution of strength within the ROM. In other words, the proposed method evaluates upper limb mobility and the ability to produce force. The feasibility of the method is evaluated through experimental results.

The growth of the internet brings up new keywords for the information technology, such as cloud computing and big data. The increasing data in the information society has a potential to offer new insight into the real world. On the other hand, the increasing demand for rehabilitation of aged people has resulted in increased demand for physical therapy robots. Therefore, this paper proposes the construction of rehabilitation database extending the concept of medical cloud technologies. We discuss the possibility of establishing a new validation methodology by generating database created using the data collected with rehabilitation equipment. Statistical processing was applied to the data collected using the rehabilitation equipment, for investigating an example of a validation method. The results show that a new knowledge for physical therapy can be extracted through a statistical evaluation with database compiled from quantitative sensor information.

A cortical neuron generates irregular spike sequences including highly variable intervals. This seems to suggest that the neuron receives highly fluctuating synaptic inputs. The simple leaky integrate-and-fire(L1F) model driven by fluctuating inputs exhibit high variability in their spike sequences. The variability of inter-spike intervals(IS1s) becomes larger as the input fluctuation becomes larger. But Hodgkin-Huxley(HH) model is found to exhibit inverse relationships. The IS1 variability becomes smaller as the input fluctuation becomes larger, although the effective strength of inputs is kept to be constant. In this paper, we report the strange responses of Hodgkin-Huxley model to highly fluctuating inputs.