**5. Conclusion**

*Prosthesis*

**Table 3.**

*configuration).*

**22**

**Figure 9.**

*Influence of box configuration information on classification rate due to error (±1.0: Corresponding to 15 cm for* 

**Selected feature Coefficient p value Coefficient p value**

17, rdWL −2.15 0.087 −0.96 0.420 *The meaning of the symbols: r, the ratio of WL; the value of WL; t, total sum for the whole period of the reaching motion; d, segmentation delimited by every time step (0.1 s). The ID number and type of features are expressed as follows. rdWL(1–112): the ratio of WL in the segmentation delimited by every time step (0.1 s); vdWL(113–144): WL in the segmentation delimited by every time step (0.1 s); rtWL(145–172): the ratio of the total sum of WL until a specified elapsed time; vtWL(173–180): the total sum of WL until a specified elapsed time; BP(181): the box pose and* 

*Feature selected using AIC, coefficients of the logistic regression equation, p value (dataset: EMG and box* 

*BL(182): the box position; p value represents statistical significance of coefficient.*

**π4 versus π6 π5 versus π6**

*position, and 45° for pose). (a) Subject A, (b) subject B, and (c) subject C.*

In this research, we employed multinomial logistic regression to realize both information integration of two signal sources: images and around-shoulder EMG, and the target-reaching-position identification for 12 box configuration (pose 4 × position 3).

A high classification rate was achieved using both information sources. It was found that the box configuration information contributes to the classification of the depth of the reaching motion. Moreover, since the timing at which the classification rate exceeds 70% greatly differs from each subject, it is considered that the optimal classification timing might be individual dependent. Furthermore, the classification rate decreased when the error level was increased in all subjects.

In the experiment, we only changed the box position in the depth direction relative to the subject. Lateral changes of the box position relative to the subject shall be investigated, in the near future. Moreover, the effect of the box configuration information calculated from the real images captured by the active camera should be studied and compared with the results of this study. Since the error caused by the image acquisition and processing, as well as the real computational cost shall affect the information integration. Finally, the system should be finally validated with the data from amputee subjects.
