Dancing with Plants Plants as Biosensors

for Human Body Movement?

Report of an Anthroposophic Experiment

Motivation

On September 16, 2020, we conducted 10 experiments

Investigation if vegetables (weekly danced with eurythmy) behave different from non-danced [1]

Eurythmy: art of making visible language and music effective regularities and relationships through human movement [2][3]

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Data Collection

Experiment 1 2 3 4 5 6 7 8 9 10

Plant beetroot beetroot tomatoes beetroot* lettuce (first) beetroot (first)

tomatoes (first)

beetroot beetroot tomatoes

Dance Planets EU Upper Sun Mercury Mercury control, CCUL

control, CCUL

control, CCUL

Planets EU Mercury Mercury

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Setup of Equipment

Two SpikerBoxes [4] for different

plants per plant bed

Two dual-lense recording

devices for same plant

One camera on dancer to track

his movement [5]

* Experiment 4 got discarded.

Movement Plants: Tracking of plant movement with Machine Learning. [6]

Movement Dancer: Design of tracking algorithm to record different arm positions. [7]

Plant Discharge: Extraction of MFCCs from the plant discharge recordings [8] splitting the electrical signal

into a spectrum of different frequencies. [9]

Data Processing

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Data Analysis (1): Correlation SpikerBoxes - Dancer

Method

Dancing movements were binary encoded

(active, non-active) [10][11]

Encoded dancer’s movement was correlated with the

electrical signal for each beetroot [12]

Results

Signals are low in strength but consistent across all

experiments

The average correlation between dancer and plant is

higher in the experimental group

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Spikerbox 1

Left Hand Right Hand

Experiment Corr Coefficient Corr Coefficient

1 0.6034*** 0.3950***

2 -0.1294 -0.1294

4 0.2997** -0.4673***

8 0.2392*** 0.2392***

9 -0.5496*** -0.3838***

10 -0.2163 -0.2846

0.0412 -0.1051

(Abs) Danced 0.3597 0.3320

(Abs) Control 0.2392 0.2392

MethodFiltering out majority of noise in data via frequency-decomposition [13][14]Correlation of voltage signal of two SpikerBoxes [4]with each other for different windows-sizes [15][16][17]

ResultsIdentification of an optimal window-length of 21 secondsDifferent plants of the same type behave similarly during the dance with regard to their electrical discharge

Environmental changes have an effect on vegetables(corr = .416)

Data Analysis (2): SpikerBoxes within Experiment

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MFCC 0 4 9 Average

Beetroot Corr Corr Corr Correlation

01 0.771*** 0.406* 0.052 0.410

02 0.720*** 0.208 0.312 0.414

07 0.739*** 0.194 0.196 0.376

08 0.046*** 0.731** 0.352 0.376

09 0.941*** 0.338 0.240 0.506

0.416

MFCC 1 3 4 Average

Experimental Corr Corr Corr Correlation

01 - Beetroot 0.771*** 0.345 0.406* 0.507

02 - Beetroot 0.720*** 0.076 0.208 0.335

03 - Tomatoes 0.262 0.130 0.094 0.162

08 - Beetroot 0.046 0.297 0.731** 0.358

09 - Beetroot 0.941*** 0.156 0.338 0.478

10 - Tomatoes 0.248 0.458 0.256 0.321

0.360

Data Analysis (3): Comparison for Control/Experimental Group

Experimental Group (Plants)

Results

Changes in the electrical potential of first danced beetroots are more similar than for regularly danced beetrootsSynchronization of SpikerBoxes for undanced plants higher than for plants in the control group

Control Group (Plants)

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MFCC 2 3 8 Average

Control Corr Corr Corr Correlation

05 - Lettuce 0.262 0.430 0.4980.397

06 - Beetroot 0.515*** 0.351 0.2480.371

07 - Tomatoes 0.652* 0.299 0.651*0.534

0.434

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LimitationsDischarge is not explainable with studies in Gait detection using electrostatic Fields [6]. The electric Field outside is negligible. Other sources for this electric behavior need to be researched.

External influences caused by vibrations, weather influences or movements in the soil could have been interfered with our study.

This exploratory research is rather indicative, and the number of experiments is small.

ConclusionData shows marginal effects on the behavior of plant signals when exposed to eurythmic dancing.

The dancer’s arm movement is a possible influence on the electrical discharge

Thank you for your kind interest.References[1] Spiegel P. 2010. Anthroposophy's etheric forces : exploring the relation between music and plant-growth. University of Cape Town.[2] Wikipedia.com (2020). Eurythmy from https://en.wikipedia.org/wiki/Eurythmy, opened October 10, 2020.[3] Intro To Eurythmy Número de Catalogo: 9780880100427 Codigo de barras: 9780880100427 Formato: BOOK Condición: Nuevo ¿Falta información? Por favor, contáctanos si falta algún detalle y dinos donde podríamos añadir esta información en nuestra descripción.[4] Marzullo, G. (2012). The SpikerBox: A Low Cost, Open-Source BioAmplifier for Increasing Public Participation in Neuroscience InquiryPLOS ONE, 7(3), 1-6.[5] Kale, Geetanjali Vinayak and Varsha Hemant Patil. "A Study of Vision based Human Motion Recognition and Analysis." IJACI 7.2 (2016): 75-92. Web. 26 Nov. 2020.[6] Lucas, Bruce & Kanade, Takeo. (1981). An Iterative Image Registration Technique with an Application to Stereo Vision (IJCAI). 81.[7] Howse, J., & Minichino, J. (2020). Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition. Packt Publishing.[8] Sigurðsson, Sigurður, K. B. Petersen and T. Lehn-Schiøler. “Mel Frequency Cepstral Coefficients: An Evaluation of Robustness of MP3 Encoded Music.” ISMIR (2006).[9] Tahon, M., Devillers, L.: Towards a small set of robust acoustic features for emotion recognition. IEEE/ACM Audio, Speech, Language Processing 24(1), 16–28 (2016). [10] Farrington DP, Loeber R. Some benefits of dichotomization in psychiatric and criminological research. Criminal Behavior and Mental Health 2000; 10:100–122.[11] Lewis JA. In defence of dichotomy. Pharmaceutical Statistics 2004; 3:77–79.[12] Li, M., Tian, S., Linlin, S., Chen, X.: Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method. Sensors 2018, 18, 1737.[13] S. Sriram, S. Nitin, K. M. M. Prabhu and M. J. Bastiaans, "Signal denoising techniques for partial discharge measurements," in IEEE Transactions on Dielectrics and Electrical Insulation, vol. 12, no. 6, pp. 1182-1191, Dec. 2005, doi: 10.1109/TDEI.2005.1561798.[14] Ljubiša Stanković, Danilo Mandić, Miloš Daković, Miloš Brajović, Time-frequency decomposition of multivariate multicomponent signals, Signal Processing, Volume 142, 2018, Pages 468-479, ISSN 0165-1684[15] Ding, M., Bressler, S., Yang, W. et al. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern 83, 35–45 (2000). https://doi.org/10.1007/s004229900137[16] Yufeng Yu, Yuelong Zhu, Shijin Li, Dingsheng Wan, "Time Series Outlier Detection Based on Sliding Window Prediction", Mathematical Problems in Engineering, vol. 2014, Article ID 879736, 14 pages, 2014.[17] Bakdash JZ and Marusich LR (2017) Repeated Measures Correlation. Front. Psychol. 8:456.

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