Cell decision-making through the lens of Bayesian learning

Cell decision-making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker-Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision-making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters

Deep Learning Techniques in Estimating Ankle Joint Power Using Wearable IMUs

Estimating ankle joint power can be used to identify gait abnormalities, which is usually achieved by employing a complicated biomechanical model using heavy equipment settings. This paper demonstrates deep learning approaches to estimate ankle joint power from two Inertial Measurement Unit (IMU) sensors attached at foot and shank. The purpose of this study was to investigate deep learning models in estimating ankle joint power in practical scenarios, in terms of variance in walking speeds, reduced number of extracted features and inter-subject model adaption. IMU data was collected from nine healthy participants during five walking trials at different speeds on a force-plate-instrumented treadmill while an optical motion tracker was used as ground truth. Three state-of-the-art deep neural architectures, namely Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and, fusion of CNN and LSTM (CNN-LSTM), were developed, trained, and evaluated in predicting ankle joint power by extracting few simple, meaningful features. The proposed architectures were found efficient and promising with higher estimation accuracies (correlation coefficient, R > 0.92 and adjusted R-squared value > 83%) and lower errors (mean squared error <; 0.06, and mean absolute error <; 0.13) in inter-participant evaluations. Performance evaluations among the three deep regressors showed that LSTM performed comparatively better. Lower standard deviations in mean squared error (0.029) and adjusted R-squared value (5.5%) proved the proposed model's efficiency for all participants

Mathematical modelling of collective cell decision-making in complex environments

Cellular decision-making help cells to infer functionally different phenotypes in response to microenvironmental cues and noise present in the system and the environment, with or without genetic change. In Cellular Biology, there exists a list of open questions such as, how individual cell decisions influence the dynamics at the population level (an organization of indistinguishable cells) and at the tissue level (a group of nearly identical cells and their corresponding extracellular matrix which simultaneously accomplish a set of biological operations)? As collective cell migration originates from local cellular orientation decisions, can one generate a mathematical model for collective cell migration phenomena without elusive undiscovered biophysical/biochemical mechanisms and further predict the pattern formations which originates inside the collective cell migration? how optimal microenvironmental sensing is related to differentiated tissue at the spatial scale ? How cell sensing radius and total entropy production (which precisely helps us to understand the operating regimes where cells can take decisions about their future fate) is correlated, and how can one understand the limits of sensing radius at robust tissue development ? To partially tackle these sets of questions, the LEUP (Least microEnvironmental Uncertainty Principle) hypothesis has been applied to different biological scenaros. At first, the LEUP has been enforced to understand the spatio-temporal behavior of a tissue exhibiting phenotypic plasticity (it is a prototype of cell decision-making). Here, two cases have been rigorously studied i.e., migration/resting and migration/proliferation plasticity which underlie the epithelial-mesenchymal transition (EMT) and the Go-or-Grow dichotomy. On the one hand, for the Go-or-Rest plasticity, a bistable switching mechanism between a diffusive (fluid) and an epithelial (solid) tissue phase has been observed from an analogous mean-field approximation which further depends on the sensitivity of the phenotypes to the microenvironment. However, on the other hand, for the Go-or-Grow plasticity, the possibility of Turing pattern formation is inspected for the “solid” tissue phase and its relation to the parameters of the LEUP-driven cell decisions. Later, LEUP hypothesis has been suggested in the area of collective cell migration such that it can provide a tool for a generative mathematical model of collective migration without precise knowledge about the mechanistic details, where the famous Vicsek model is a special case. In this generative model of collective cell migration, the origin of pattern formation inside collective cell migration has been investigated. Moreover, this hypothesis helps to construct a mathematical model for the collective behavior of spherical \textit{Serratia marcescens} bacteria, where the basic understanding of migration mechanisms remain unknown. Furthermore, LEUP has been applied to understand tissue robustness, which in turn shows the way how progenitor cell fate decisions are associated with environmental sensing. The regulation of environmental sensing drives the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue, which are verified later with the experimental data. LEUP driven stochastic thermodynamic formalism also shows that the thermodynamic robustness of differentiated tissues depends on cell metabolism, cell sensing properties and the limits of the cell sensing radius, which further ensures the robustness of differentiated tissue spatial order. Finally, all important results of the thesis have been encapsulated and the extension of the LEUP has been discussed.:Contents Statement of authorship vii Abstract ix I. Introduction to cell decision-making 1 1. What is cell decision-making ? 3 1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2. Examplesofcelldecision-making. . . . . . . . . . . . . . . . . . . . . . 4 1.2.1. PhenotypicPlasticity . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2. Cellularmigration:orientationdecisions . . . . . . . . . . . . . 5 1.2.3. Celldifferentiation . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3. Challengesandopenquestions . . . . . . . . . . . . . . . . . . . . . . 7 1.4. Solutionstrategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.5. Structureofthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 II. Least microEnvironmental Uncertainty Principle (LEUP) 11 2. Least microEnvironmental Uncertainty Principle (LEUP) 13 2.1. HypothesisbehindLEUP . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2. Mathematicalformulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1. CellasBayesiandecisionmaker . . . . . . . . . . . . . . . . . . 14 2.2.2. VariationalprincipleforLEUP . . . . . . . . . . . . . . . . . . . . 16 III. LEUP in biological problems 17 3. Phenotypic plasticity : dynamics at the level of tissue from individual cell decisions 19 3.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2. Individualbasedmodel(IBM) . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3. Mean-fieldapproximation . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1. Phenotypicswitchingdynamics . . . . . . . . . . . . . . . . . . 26 3.3.2. Cellmigrationdynamics . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.3. Superpositionofphenotypicswitchingdynamicsandcellmi- gration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4. Spatio-temporaldynamicsofcellmigration/proliferationplasticity . . 28 3.4.1. CaseI:Largeinteractionradius . . . . . . . . . . . . . . . . . . 29 3.4.2. CaseII:Finiteinteractionradius . . . . . . . . . . . . . . . . . . 30 3.4.3. Phenotypicswitchingdynamicsintheabsenceofmicroenvi- ronmentalsensing . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4. Cellular orientation decisions: origin of pattern formations in collective cell migrations 39 4.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.1.1. Self-propelledparticlemodelwithleupbaseddecision-making 41 4.1.2. Orderparametersandobservables . . . . . . . . . . . . . . . . 42 4.1.3. Statisticaltest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2. ComparisonwithVicsekmodel . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1. Patternsindifferentparameterregimes . . . . . . . . . . . . . 45 4.3. Application:thesphericalbacteriacase. . . . . . . . . . . . . . . . . . 47 4.4. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5. Cell differentiation and sensing: tissue robustness from optimal environ- mental sensing 53 5.1. LEUPbasedmathematicalmodelforcelldifferentiation . . . . . . . . 56 5.1.1. StatisticalresultsfromLEUP . . . . . . . . . . . . . . . . . . . . 59 5.2. RelationbetweenLEUPandcellsensing . . . . . . . . . . . . . . . . . 60 5.3. LEUPdrivenfluctuationtheorem: confirmsthethermodynamicro- bustnessofdifferentiatedtissues . . . . . . . . . . . . . . . . . . . . . 61 5.3.1. Application: differentiated photoreceptor mosaics are ther- modynamicallyrobust . . . . . . . . . . . . . . . . . . . . . . . . 65 5.4. Thelimitforcellsensingradius . . . . . . . . . . . . . . . . . . . . . . . 67 5.4.1. Application:Theaveragesensingradiusoftheavianconecell 69 5.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Discussions 75 7. Supplementary Material 91 8. Erklärung 11

Investigating effects of window length on 1D-CNN-LSTM and effectiveness of Heuristic features in solving sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer

Human Activity Recognition (HAR) using smartphone sensors can offer multiple applications in different spheres. Using deep learning classifiers such as Convolutional Neural Networks (CNN), Short-Term Long Memory (LSTM), or their hybrid showed promising improvement in HAR. However, using these deep learning networks requires segmenting the input data into multiple data windows of similar length. The length of the data windows can significantly affect HAR's performance. Therefore, the influence of the window lengths needs to be investigated to choose an optimal window length. Additionally, the orientation and placement of the smartphone sensor also present significant challenges to HAR. Many approaches have been proposed to solve the orientation and placement problems. In my study, I first evaluated the effects of window length on 1D-CNN-LSTM in HAR for six activities: Lying, Sitting, Walking, and Running at 3-METs (Metabolic Equivalent of Tasks), 5-METs and 7-METs. Subsequently, I evaluated the effectiveness of the heuristic features in HAR in solving sensor orientation and sensor placement problems for three smartphone locations: Pocket, Backpack and Hand. I performed this evaluation using 1D-CNN-LSTM by using the optimal window length found in the first part

Mathematical modelling of collective cell decision-making in complex environments

Cellular decision-making help cells to infer functionally different phenotypes in response to microenvironmental cues and noise present in the system and the environment, with or without genetic change. In Cellular Biology, there exists a list of open questions such as, how individual cell decisions influence the dynamics at the population level (an organization of indistinguishable cells) and at the tissue level (a group of nearly identical cells and their corresponding extracellular matrix which simultaneously accomplish a set of biological operations)? As collective cell migration originates from local cellular orientation decisions, can one generate a mathematical model for collective cell migration phenomena without elusive undiscovered biophysical/biochemical mechanisms and further predict the pattern formations which originates inside the collective cell migration? how optimal microenvironmental sensing is related to differentiated tissue at the spatial scale ? How cell sensing radius and total entropy production (which precisely helps us to understand the operating regimes where cells can take decisions about their future fate) is correlated, and how can one understand the limits of sensing radius at robust tissue development ? To partially tackle these sets of questions, the LEUP (Least microEnvironmental Uncertainty Principle) hypothesis has been applied to different biological scenaros. At first, the LEUP has been enforced to understand the spatio-temporal behavior of a tissue exhibiting phenotypic plasticity (it is a prototype of cell decision-making). Here, two cases have been rigorously studied i.e., migration/resting and migration/proliferation plasticity which underlie the epithelial-mesenchymal transition (EMT) and the Go-or-Grow dichotomy. On the one hand, for the Go-or-Rest plasticity, a bistable switching mechanism between a diffusive (fluid) and an epithelial (solid) tissue phase has been observed from an analogous mean-field approximation which further depends on the sensitivity of the phenotypes to the microenvironment. However, on the other hand, for the Go-or-Grow plasticity, the possibility of Turing pattern formation is inspected for the “solid” tissue phase and its relation to the parameters of the LEUP-driven cell decisions. Later, LEUP hypothesis has been suggested in the area of collective cell migration such that it can provide a tool for a generative mathematical model of collective migration without precise knowledge about the mechanistic details, where the famous Vicsek model is a special case. In this generative model of collective cell migration, the origin of pattern formation inside collective cell migration has been investigated. Moreover, this hypothesis helps to construct a mathematical model for the collective behavior of spherical \textit{Serratia marcescens} bacteria, where the basic understanding of migration mechanisms remain unknown. Furthermore, LEUP has been applied to understand tissue robustness, which in turn shows the way how progenitor cell fate decisions are associated with environmental sensing. The regulation of environmental sensing drives the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue, which are verified later with the experimental data. LEUP driven stochastic thermodynamic formalism also shows that the thermodynamic robustness of differentiated tissues depends on cell metabolism, cell sensing properties and the limits of the cell sensing radius, which further ensures the robustness of differentiated tissue spatial order. Finally, all important results of the thesis have been encapsulated and the extension of the LEUP has been discussed.:Contents Statement of authorship vii Abstract ix I. Introduction to cell decision-making 1 1. What is cell decision-making ? 3 1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2. Examplesofcelldecision-making. . . . . . . . . . . . . . . . . . . . . . 4 1.2.1. PhenotypicPlasticity . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2. Cellularmigration:orientationdecisions . . . . . . . . . . . . . 5 1.2.3. Celldifferentiation . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3. Challengesandopenquestions . . . . . . . . . . . . . . . . . . . . . . 7 1.4. Solutionstrategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.5. Structureofthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 II. Least microEnvironmental Uncertainty Principle (LEUP) 11 2. Least microEnvironmental Uncertainty Principle (LEUP) 13 2.1. HypothesisbehindLEUP . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2. Mathematicalformulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1. CellasBayesiandecisionmaker . . . . . . . . . . . . . . . . . . 14 2.2.2. VariationalprincipleforLEUP . . . . . . . . . . . . . . . . . . . . 16 III. LEUP in biological problems 17 3. Phenotypic plasticity : dynamics at the level of tissue from individual cell decisions 19 3.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2. Individualbasedmodel(IBM) . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3. Mean-fieldapproximation . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1. Phenotypicswitchingdynamics . . . . . . . . . . . . . . . . . . 26 3.3.2. Cellmigrationdynamics . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.3. Superpositionofphenotypicswitchingdynamicsandcellmi- gration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4. Spatio-temporaldynamicsofcellmigration/proliferationplasticity . . 28 3.4.1. CaseI:Largeinteractionradius . . . . . . . . . . . . . . . . . . 29 3.4.2. CaseII:Finiteinteractionradius . . . . . . . . . . . . . . . . . . 30 3.4.3. Phenotypicswitchingdynamicsintheabsenceofmicroenvi- ronmentalsensing . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4. Cellular orientation decisions: origin of pattern formations in collective cell migrations 39 4.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.1.1. Self-propelledparticlemodelwithleupbaseddecision-making 41 4.1.2. Orderparametersandobservables . . . . . . . . . . . . . . . . 42 4.1.3. Statisticaltest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2. ComparisonwithVicsekmodel . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1. Patternsindifferentparameterregimes . . . . . . . . . . . . . 45 4.3. Application:thesphericalbacteriacase. . . . . . . . . . . . . . . . . . 47 4.4. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5. Cell differentiation and sensing: tissue robustness from optimal environ- mental sensing 53 5.1. LEUPbasedmathematicalmodelforcelldifferentiation . . . . . . . . 56 5.1.1. StatisticalresultsfromLEUP . . . . . . . . . . . . . . . . . . . . 59 5.2. RelationbetweenLEUPandcellsensing . . . . . . . . . . . . . . . . . 60 5.3. LEUPdrivenfluctuationtheorem: confirmsthethermodynamicro- bustnessofdifferentiatedtissues . . . . . . . . . . . . . . . . . . . . . 61 5.3.1. Application: differentiated photoreceptor mosaics are ther- modynamicallyrobust . . . . . . . . . . . . . . . . . . . . . . . . 65 5.4. Thelimitforcellsensingradius . . . . . . . . . . . . . . . . . . . . . . . 67 5.4.1. Application:Theaveragesensingradiusoftheavianconecell 69 5.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Discussions 75 7. Supplementary Material 91 8. Erklärung 11

Mathematical modelling of collective cell decision-making in complex environments

Cellular decision-making help cells to infer functionally different phenotypes in response to microenvironmental cues and noise present in the system and the environment, with or without genetic change. In Cellular Biology, there exists a list of open questions such as, how individual cell decisions influence the dynamics at the population level (an organization of indistinguishable cells) and at the tissue level (a group of nearly identical cells and their corresponding extracellular matrix which simultaneously accomplish a set of biological operations)? As collective cell migration originates from local cellular orientation decisions, can one generate a mathematical model for collective cell migration phenomena without elusive undiscovered biophysical/biochemical mechanisms and further predict the pattern formations which originates inside the collective cell migration? how optimal microenvironmental sensing is related to differentiated tissue at the spatial scale ? How cell sensing radius and total entropy production (which precisely helps us to understand the operating regimes where cells can take decisions about their future fate) is correlated, and how can one understand the limits of sensing radius at robust tissue development ? To partially tackle these sets of questions, the LEUP (Least microEnvironmental Uncertainty Principle) hypothesis has been applied to different biological scenaros. At first, the LEUP has been enforced to understand the spatio-temporal behavior of a tissue exhibiting phenotypic plasticity (it is a prototype of cell decision-making). Here, two cases have been rigorously studied i.e., migration/resting and migration/proliferation plasticity which underlie the epithelial-mesenchymal transition (EMT) and the Go-or-Grow dichotomy. On the one hand, for the Go-or-Rest plasticity, a bistable switching mechanism between a diffusive (fluid) and an epithelial (solid) tissue phase has been observed from an analogous mean-field approximation which further depends on the sensitivity of the phenotypes to the microenvironment. However, on the other hand, for the Go-or-Grow plasticity, the possibility of Turing pattern formation is inspected for the “solid” tissue phase and its relation to the parameters of the LEUP-driven cell decisions. Later, LEUP hypothesis has been suggested in the area of collective cell migration such that it can provide a tool for a generative mathematical model of collective migration without precise knowledge about the mechanistic details, where the famous Vicsek model is a special case. In this generative model of collective cell migration, the origin of pattern formation inside collective cell migration has been investigated. Moreover, this hypothesis helps to construct a mathematical model for the collective behavior of spherical \textit{Serratia marcescens} bacteria, where the basic understanding of migration mechanisms remain unknown. Furthermore, LEUP has been applied to understand tissue robustness, which in turn shows the way how progenitor cell fate decisions are associated with environmental sensing. The regulation of environmental sensing drives the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue, which are verified later with the experimental data. LEUP driven stochastic thermodynamic formalism also shows that the thermodynamic robustness of differentiated tissues depends on cell metabolism, cell sensing properties and the limits of the cell sensing radius, which further ensures the robustness of differentiated tissue spatial order. Finally, all important results of the thesis have been encapsulated and the extension of the LEUP has been discussed.:Contents Statement of authorship vii Abstract ix I. Introduction to cell decision-making 1 1. What is cell decision-making ? 3 1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2. Examplesofcelldecision-making. . . . . . . . . . . . . . . . . . . . . . 4 1.2.1. PhenotypicPlasticity . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2. Cellularmigration:orientationdecisions . . . . . . . . . . . . . 5 1.2.3. Celldifferentiation . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3. Challengesandopenquestions . . . . . . . . . . . . . . . . . . . . . . 7 1.4. Solutionstrategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.5. Structureofthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 II. Least microEnvironmental Uncertainty Principle (LEUP) 11 2. Least microEnvironmental Uncertainty Principle (LEUP) 13 2.1. HypothesisbehindLEUP . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2. Mathematicalformulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1. CellasBayesiandecisionmaker . . . . . . . . . . . . . . . . . . 14 2.2.2. VariationalprincipleforLEUP . . . . . . . . . . . . . . . . . . . . 16 III. LEUP in biological problems 17 3. Phenotypic plasticity : dynamics at the level of tissue from individual cell decisions 19 3.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2. Individualbasedmodel(IBM) . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3. Mean-fieldapproximation . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1. Phenotypicswitchingdynamics . . . . . . . . . . . . . . . . . . 26 3.3.2. Cellmigrationdynamics . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.3. Superpositionofphenotypicswitchingdynamicsandcellmi- gration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4. Spatio-temporaldynamicsofcellmigration/proliferationplasticity . . 28 3.4.1. CaseI:Largeinteractionradius . . . . . . . . . . . . . . . . . . 29 3.4.2. CaseII:Finiteinteractionradius . . . . . . . . . . . . . . . . . . 30 3.4.3. Phenotypicswitchingdynamicsintheabsenceofmicroenvi- ronmentalsensing . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4. Cellular orientation decisions: origin of pattern formations in collective cell migrations 39 4.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.1.1. Self-propelledparticlemodelwithleupbaseddecision-making 41 4.1.2. Orderparametersandobservables . . . . . . . . . . . . . . . . 42 4.1.3. Statisticaltest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2. ComparisonwithVicsekmodel . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1. Patternsindifferentparameterregimes . . . . . . . . . . . . . 45 4.3. Application:thesphericalbacteriacase. . . . . . . . . . . . . . . . . . 47 4.4. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5. Cell differentiation and sensing: tissue robustness from optimal environ- mental sensing 53 5.1. LEUPbasedmathematicalmodelforcelldifferentiation . . . . . . . . 56 5.1.1. StatisticalresultsfromLEUP . . . . . . . . . . . . . . . . . . . . 59 5.2. RelationbetweenLEUPandcellsensing . . . . . . . . . . . . . . . . . 60 5.3. LEUPdrivenfluctuationtheorem: confirmsthethermodynamicro- bustnessofdifferentiatedtissues . . . . . . . . . . . . . . . . . . . . . 61 5.3.1. Application: differentiated photoreceptor mosaics are ther- modynamicallyrobust . . . . . . . . . . . . . . . . . . . . . . . . 65 5.4. Thelimitforcellsensingradius . . . . . . . . . . . . . . . . . . . . . . . 67 5.4.1. Application:Theaveragesensingradiusoftheavianconecell 69 5.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Discussions 75 7. Supplementary Material 91 8. Erklärung 11

Building Effective Machine Learning Models for Ankle Joint Power Estimation During Walking Using FMG Sensors

Ankle joint power is usually determined by a complex process that involves heavy equipment and complex biomechanical models. Instead of using heavy equipment, we proposed effective machine learning (ML) and deep learning (DL) models to estimate the ankle joint power using force myography (FMG) sensors. In this study, FMG signals were collected from nine young, healthy participants. The task was to walk on a special treadmill for five different velocities with a respective duration of 1 min. FMG signals were collected from an FMG strap that consists of 8 force resisting sensor (FSR) sensors. The strap was positioned around the lower leg. The ground truth value for ankle joint power was determined with the help of a complex biomechanical model. At first, the predictors' value was preprocessed using a rolling mean filter. Following, three sets of features were formed where the first set includes raw FMG signals, and the other two sets contained time-domain and frequency-domain features extracted using the first set. Cat Boost Regressor (CBR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) were trained and tested using these three features sets. The results presented in this study showed a correlation coefficient of R = 0.91 ± 0.07 for intrasubject testing and were found acceptable when compared to other similar studies. The CNN on raw features and the LSTM on time-domain features outperformed the other variations. Aside from that, a performance gap between the slowest and fastest walking distance was observed. The results from this study showed that it was possible to achieve an acceptable correlation coefficient in the prediction of ankle joint power using FMG sensors with an appropriate combination of feature set and ML model.ISSN:1011-521

Unsupervised, Semi-Supervised Interactive Force Estimations During pHRI via Generated Synthetic Force Myography Signals

Recognizing applied hand forces using force myography (FMG) biosignals requires adequate training data to facilitate physical human-robot interactions (pHRI). But in practice, data is often scarce, and labels are usually unavailable or time consuming to generate. Synthesizing FMG biosignals can be a viable solution. Therefore, in this paper, we propose for the first time a dual-phased algorithm based on semi-supervised adversarial learning utilizing fewer labeled real FMG data with generated unlabeled synthetic FMG data. We conducted a pilot study to test this algorithm in estimating applied forces during interactions with a Kuka robot in 1D-X, Y, Z directions. Initially, an unsupervised FMG-based deep convolutional generative adversarial network (FMG-DCGAN) model was employed to generate real-like synthetic FMG data. A variety of transformation functions were used to observe domain randomization for increasing data variability and for representing authentic physiological, environmental changes. Cosine similarity score and generated-to-input-data ratio were used as decision criteria minimizing the reality gap between real and synthetic data and helped avoid risks associated with wrong predictions. Finally, the FMG-DCGAN model was pretrained to generate pseudo-labels for unlabeled real and synthetic data, further retrained using all labeled and pseudo-labeled data and was termed as the self-trained FMG-DCGAN model. Lastly, this model was evaluated on unseen real test data and achieved accuracies of 85%>R-2 > 77% in force estimation compared to the corresponding supervised baseline model (89%>R-2 > 78%). Therefore, the proposed method can be more practical for use in FMG-based HRI, rehabilitation, and prosthetic control for daily, repetitive usage even with few labeled data.ISSN:2169-353