Investigation of the Surrounding Environment′s Influence on Gait Sensing Using a Plant as a Sensor

Abstract

Some animals and plants function as bioantennas in that changes in their surrounding environment produce variations in their bioelectric potentials. While the bioelectric potential is affected by living activities of the plant, it has been observed that the bioelectric potential can be reduced using plants. Thus, the influence of the life activity of a plant on the reception signal must be accounted for when a plant is used as a sensor. In this study, we produced an environmental change near a foliage plant growing in an indoor environment and examined the directivity of the plant's sensing ability. The sensitivity of the plant was a roughly circular area centered on the location of the plant. We also investigated the influence of the number of leaves on the plant on its sensing ability and found that it decreased with a reduction in the number of leaves. In addition, we monitored the effect of a person walking on the spot near the plant on the bioelectric potential of the plant. Six subjects stepped on the spot 50 cm from a rubber tree and we measured the variation in the bioelectric potential of the tree produced by this stepping motion. The results confirmed that stepping motion produces a measurable response in the bioelectric potential of a plant and that this response varies in synchrony with the subject's stepping rate. Moreover, by conducting principal component analysis using the peak value of the spectrum characteristics of the measured bioelectric potential, cumulative proportion was found to reach nearly 97% at low-frequency components up to the fifth peak.

Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

“…14 In MSPM, key information pertaining to the data is mapped into the lowdimensional space by the data dimension reduction, and the original high-dimensional data feature information is obtained, after which comprehensive statistics are established for the low-dimensional data to realize online monitoring. At present, MSPM methods include principal component analysis (PCA), 15,16 canonical correlation analysis (CCA), 17,18 independent component analysis (ICA), 19,20 Fisher discriminant analysis (FDA), 21,22 and partial least squares (PLS). 23,24 These methods usually assume that process variables are linearly related and obey the Gaussian distribution.…”