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Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.

Activity trackers are promising tools to increase the motivation to be physically active, thus strengthening users’ physical constitution and potentially preventing cardiovascular diseases. To establish behavioral changes with such beneficial consequences, prolonged continuous use of trackers seems to be necessary. However, the usage behavior of many tracker users is characterized by interruptions or complete discontinuation after only a few months. Which factors determine individual usage trajectories is still unclear. This research sheds light on user diversity to investigate how inter-individual differences are related to reasons for usage interruptions and permanence of abandonment. Results based on a survey of N = 159 former users revealed that usage motives regarding self-determination theory, domain-specific personality traits (affinity for technology interaction), and interaction variables (dependency effect, trust in activity tracker measurement) were related to specific reasons for usage interruptions. Moreover, highly autonomous usage motivation and high trust were linked to more fragile abandonment decisions.

The edge-connectivity matrix of a weighted graph is the matrix whose off-diagonal v-w entry is the weight of a minimum edge cut separating vertices vand w. Its computation is a classical topic of combinatorial optimization since at least the seminal work of Gomory and Hu. In this article, we investigate spectral properties of these matrices. In particular, we provide tight bounds on the smallest eigenvalue and the energy. Moreover, we study the eigenvector structure and show in which cases eigenvectors can be easily obtained from matrix entries. These results in turn rely on a new characterization of those nonnegative matrices that can actually occur as edge-connectivity matrices.

The recognition of human gestures in real-time using body attached sensors has brought attention in recent years due to the manifold potential applications. The implemented sensors must be flexible, easy to adapt to bodily parts, highly sensitive and biocompatible. In this work a novel strain sensor is investigated which is based on a conductive thermoplastic polyurethane (CTPU) blended with a biocompatible soft thermoplastic polyurethane polymer (STPU). The sensor shows a high capability to transfer the mechanical deformation into an electrical response with high sensitivity. An investigation of the sensing properties using different mixture percentage of STPU/CTPU starting from 10/0 to 0/10 is carried out. The sensing behavior is characterized based on several mechanical and electrical properties e.g. stretchability, sensitivity, linearity, repeatability and hysteresis. The composition STPU/CTPU 7/3 showed a better performance compared to other studied compositions, where the elastic zone estimated was ∼13 (linearity = 0.98 within elastic zone) and 39 of sensitivity. The mentioned sensor's response demonstrates high repeatability and low hysteresis effect. Finally, the selected sensor composition was attached to a glove where the movement of a finger was successfully tracked.

Hand sign recognition is gaining importance in human-human and in human-machine communication and interaction. Electrical Impedance Tomography (EIT) is thereby very interesting as it provides information on impedance changes in depth of the Section of the arm, which infers muscle contractions. This paper introduces an EIT imaging system for hand sign recognition and monitoring having a low complexity and including an electronic interface with 8 electrodes placed on the forearm, a Gauss-Newton image reconstruction algorithm, a robust CNN based hand sign classification and a virtual hand model for visualization. A database has been collected for EIT measurements in pole mode taken by eight subjects performing the American sign language numbers from 0 to 9. The overall imaging system is validated using a water tank system, where conductive objects can be changed in properties and positions. The correspondence between the reconstructed images and the expected muscle behavior for the hand signs is investigated. A robust Convolutional Neural Network (CNN) classification algorithm was implemented and optimized by implementing an Adam optimizer and conducting a dedicated study to avoid overfitting. The results obtained by CNN are compared to the results by a Support Vector Machine (SVM), and a Softmax classifier. They show a classification accuracy of 95.94%, 75.61%, and 62.9% respectively. In term of subject dependency, the system using the CNN model shows a higher performance, as the accuracy decreases only by 0.72% while increasing the number of subjects from one to eight. Finally, for visualization, a 3D virtual hand model is designed and controlled based on detected hand signs.

Electrical impedance tomography (EIT) is an imaging method for characterizing the inner conductivity distribution of an object based on the measured boundary voltages resulting from the injection of an AC signal, followed by an image reconstruction procedure. An algorithm tries to solve an ill-posed inverse problem making it challenging to reconstruct an accurate image. To overcome this, we propose a genetic algorithm (GA) for the image reconstruction with a non-blind search method considering prior knowledge about the possible conductivity distribution in the initial search space. To validate the algorithm, experiments have been conducted in a water tank. The algorithm’s performance was evaluated regarding image quality and processing time, being able to minimize the corresponding quality function to 0.0505 with 100 generations using the non-blind search and the uniform crossover/random mutation. Compared to traditional methods, the GA achieves significantly better image quality. It has been implemented as an image reconstruction algorithm for gesture recognition. EIT measurements have been conducted with six persons performing American sign numbers (0–9) resulting in 1800 reconstructed images. They were classified by a previously developed convolutional neural network (CNN), reaching a 92 % accuracy, which is a very good achievement in the case of multiple subjects.

Electrical Impedance Tomography (EIT) for gesture recognition is on the rise in the bio-medical field. It is an imaging technique based on a set of electrodes which are placed on the forearm of the individual. The inner conductivity distribution inside the forearm will be calculated based on measured boundary voltages resulting of the injected excitation signal. In this work, the Genetic Algorithm, using the Non-Blind Search has been developed as a solution for the EIT Inverse Problem. Therefore, several measurements have been conducted for American sign numbers (0-9) for gesture recognition applications. The reconstructed images were used in a Convolutional Neural Network (CNN) classification algorithm for gesture recognition. The overall accuracy achieved for image recognition was 93%.

New bionic technologies and robots are becoming increasingly common in workspaces and private spheres. It is thus crucial to understand concerns regarding their use in social and legal terms and the qualities they should possess to be accepted as ‘co-workers’. Previous research in these areas used the Stereotype Content Model to investigate, for example, attributions of Warmth and Competence towards people who use bionic prostheses, cyborgs, and robots. In the present study, we propose to differentiate the Warmth dimension into the dimensions of Sociability and Morality to gain deeper insight into how people with or without bionic prostheses are perceived. In addition, we extend our research to the perception of robots. Since legal aspects need to be considered if robots are expected to be ‘co-workers’, for the first time, we also evaluated current perceptions of robots in terms of legal aspects. We conducted two studies: In Study 1, participants rated visual stimuli of individuals with or without disabilities and low- or high-tech prostheses, and robots of different levels of Anthropomorphism in terms of perceived Competence, Sociability, and Morality. In Study 2, participants rated robots of different levels of Anthropomorphism in terms of perceived Competence, Sociability, and Morality, and additionally, Legal Personality, and Decision-Making Authority. We also controlled for participants’ personality. Results showed that attributions of Competence and Morality varied as a function of the technical sophistication of the prostheses. For robots, Competence attributions were negatively related to Anthropomorphism. Perception of Sociability, Morality, Legal Personality, and Decision-Making Authority varied as functions of Anthropomorphism. Overall, this study contributes to technological design, which aims to ensure high acceptance and minimal undesirable side effects, both with regard to the application of bionic instruments and robotics. Additionally, first insights into whether more anthropomorphized robots will need to be considered differently in terms of legal practice are given.

A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this paper, we conduct a comparative study for the performance of four frameworks, Keras with TensorFlow, Pytorch, TensorFlow, and Cognitive Toolkit (CNTK), for the elaboration of neural networks. The number of neurons in the hidden layer of the neural networks is varied from 8 to 64 to understand its effect on the performance metrics of the frameworks. A test dataset is synthesized using an analytical model and real measured impedance spectra by an eddy current sensor coil on EUR 2 and TRY 1 coins. The dataset has been extended by using a novel method based on interpolation technique to create datasets with different difficulty levels to replicate the scenario with a good imitation of EUR 2 coins and to investigate the limit of the prediction accuracy. It was observed that the compared frameworks have high accuracy performance for a lower level of difficulty in the dataset. As the difficulty in the dataset is raised, there was a drop in the accuracy of CNTK and Keras with TensorFlow depending upon the number of neurons in the hidden layers. It was observed that CNTK has the overall worst accuracy performance with an increase in the difficulty level of the datasets. Therefore, the major comparison was confined to Pytorch and TensorFlow. It was observed for Pytorch and TensorFlow with 32 and 64 neurons in hidden layers that there is a minor drop in the accuracy with an increase in the difficulty level of the dataset and was above 90% until both the coins were 80% closer to each other in terms of electrical and magnetic properties. However, Pytorch with 32 neurons in the hidden layer has a reduction in model size by 70% and 16.3% and predicts the class, 73.6% and 15.6% faster in comparison to TensorFlow and Pytorch with 64 neurons.

In this paper we propose a method for the approximation of high-dimensional functions over finite intervals with respect to complete orthonormal systems of polynomials. An important tool for this is the multivariate classical analysis of variance (ANOVA) decomposition. For functions with a low-dimensional structure, i.e., a low superposition dimension, we are able to achieve a reconstruction from scattered data and simultaneously understand relationships between different variables.

To ensure traffic flow and road safety in automated driving, external human–machine interfaces (eHMIs) could prospectively support the interaction between automated vehicles (AVs; SAE Level 3 or higher) and pedestrians if implicit communication is insufficient. Particularly elderly pedestrians (≥65 years) who are notably vulnerable in terms of traffic safety might benefit of the advantages of additional signals provided by eHMIs. Previous research showed that eHMIs were assessed as useful means of communication in AVs and were preferred over exclusively implicit communication signals. However, the attitudes of elderly users regarding technology usage and acceptance are ambiguous (i.e., less intention to use technology vs. a tendency toward overreliance on technology compared to younger users). Considering potential eHMI malfunctions, an appropriate level of trust in eHMIs is required to ensure traffic safety. So far, little research respected the impact of multiple eHMI malfunctions on participants’ assessment of the system. Moreover, age effects were rarely investigated in eHMIs. In the current monitor-based study, N = 36 participants (19 younger, 17 elderly) repeatedly assessed an eHMI: During an initial measurement, when encountering a valid system and after experiencing eHMI malfunctions. Participants indicated their trust and acceptance in the eHMI, feeling of safety during the interaction and vigilance toward the eHMI. The results showed a positive effect of interacting with a valid system that acted consistently to the vehicle’s movements compared to an initial assessment of the system. After experiencing eHMI malfunctions, participants’ assessment of the system declined significantly. Moreover, elderly participants assessed the eHMI more positive across all conditions than younger participants did. The findings imply that participants considered the vehicle’s movements as implicit communication cues in addition to the provided eHMI signals during the encounters. To support traffic safety and smooth interactions, eHMI signals are required to be in line with vehicle’s movements as implicit communication cues. Moreover, the results underline the importance of calibrating an appropriate level of trust in eHMI signals. An adequate understanding of eHMI signals needs to be developed. Thereby, the requirements of different user groups should be specifically considered.

Virtual reality is increasingly used for tasks such as work and education. Thus, rendering scenarios that do not interfere with such goals and deplete user experience is becoming progressively more relevant. We present a physiologically-adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of not-playable characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal the task complexity. Our findings suggest that physiologically-adaptive virtual reality systems can improve users’ experience in a wide range of scenarios.

Deep Learning techniques have become the mainstream and unquestioned standard in many fields, e.g. convolutional neural networks (CNN) for image analysis and recognition tasks. As testing and validation of graphical user interfaces (GUIs) is increasingly relying on computer vision, CNN models that predict such subjective and informal dimensions of user experience as aesthetic or complexity perception start to achieve decent accuracy. They however require huge amounts of human-labeled training data, which are costly or unavailable in the field of Human-Computer Interaction (HCI). More traditional approaches rely on manually engineered features that are extracted from UI images with domain-specific algorithms and are used in ``traditional'' Machine Learning models, such as feedforward artificial neural networks (ANN) that generally need fewer data. In our paper, we compare the prediction quality of CNN (a modified GoogLeNet architecture) and ANN models to predict visual perception per Aesthetics, Complexity, and Orderliness scales for about 2700 web UIs assessed by 137 users. Our results suggest that the ANN architecture produces smaller Mean Squared Error (MSE) for the training dataset size (N) available in our study, but that CNN should become superior with Nthinspace>thinspace2912. We also propose the regression model that can help HCI researchers to foretell MSE in their ML experiments.

The main principles for designing successful UIs in a perfect world have long been known---considering many possible solutions for a problem and involving representative users in the process. In practice, however, reasons for violating those principles can be plentiful: the infamous tight budgets and schedules, lack of management buy-in, restrictions for face-to-face meetings, etc. Yet, design tools that do not require real users, such as AI-/ML-powered solutions, which could mitigate these issues seem to experience a rather low adoption rate in industry. In this paper, we present a survey with 34 professional digital designers and user researchers intended to investigate the above hypotheses. We inquire into awareness and usage of 61 such tools and platforms, as well as participants' design and research processes and general design tool adoption in industry. From the results we identify three particular challenges and three opportunities. Finding and recruiting relevant participants for user studies seems to be indeed problematic, and professional designers and researchers often lack the time and resources to follow a textbook process. They are, however, open to novel tools addressing these shortcomings---particular for ideation and evaluation---but at the same time seem to be largely unfamiliar with AI-/ML-based approaches or do not (yet) see added value in them. With these findings as a starting point, the Web Engineering community can work towards a deeper understanding of designers' and researchers' needs that could be met with AI-/ML-based support tools.

Computational neuroscience models of vision and neural network models for object recognition are often framed by different research agendas. Computational neuroscience mainly aims at replicating experimental data, while (artificial) neural networks target high performance on classification tasks. However, we propose that models of vision should be validated on object recognition tasks. At some point, mechanisms of realistic neuro-computational models of the visual cortex have to convince in object recognition as well. In order to foster this idea, we report the recognition accuracy for two different neuro-computational models of the visual cortex on several object recognition datasets. The models were trained using unsupervised Hebbian learning rules on natural scene inputs for the emergence of receptive fields comparable to their biological counterpart. We assume that the emerged receptive fields result in a general codebook of features, which should be applicable to a variety of visual scenes. We report the performances on datasets with different levels of difficulty, ranging from the simple MNIST to the more complex CIFAR-10 or ETH-80. We found that both networks show good results on simple digit recognition, comparable with previously published biologically plausible models. We also observed that our deeper layer neurons provide for naturalistic datasets a better recognition codebook. As for most datasets, recognition results of biologically grounded models are not available yet, our results provide a broad basis of performance values to compare methodologically similar models.

In numerous activities, humans need to attend to multiple sources of visual information at the same time. Although several recent studies support the evidence of this ability, the mechanism of multi-item attentional processing is still a matter of debate and has not been investigated much by previous computational models. Here, we present a neuro-computational model aiming to address specifically the question of how subjects attend to two items that deviate defined by feature and location. We simulate the experiment of Adamo et al. (2010) which required subjects to use two different attentional control sets, each a combination of color and location. The structure of our model is composed of two components “attention” and “decision-making”. The important aspect of our model is its dynamic equations that allow us to simulate the time course of processes at a neural level that occur during different stages until a decision is made. We analyze in detail the conditions under which our model matches the behavioral and EEG data from human subjects. Consistent with experimental findings, our model supports the hypothesis of attending to two control settings concurrently. In particular, our model proposes that initially, feature-based attention operates in parallel across the scene, and only in ongoing processing, a selection by the location takes place.

This paper argues that there are central features of Chinese English regardless of a speaker's Chinese first language (L1) or dialect. The current state of research on Chinese English is reviewed, outlining phonological, lexical, syntactical, prosodic, and discourse and pragmatic features of Chinese English. These features are categorized according to their pervasiveness based on different L1 backgrounds, to show that there are core features of Chinese English. The argument is limited by the low number of quantitative studies and the absence of large-scale quantitative studies.

Natural and human-like arm motions are promising features to facilitate social understanding of humanoid robots. To this end, we integrate biophysical characteristics of human arm-motions into sampling-based motion planning. We show the generality of our method by evaluating it with multiple manipulators. Our first contribution is to introduce a set of cost functions to optimize for human-like arm postures during collision-free motion planning. In a subsequent step, an optimization phase is used to improve the human-likeness of the initial path. Additionally, we present an interpolation approach for generating obstacle-aware and multi-modal velocity profiles. We thus generate collision-free and human-like motions in narrow passages while allowing for natural acceleration in free space.

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

To ensure safety and support drivers’ comfort and acceptance, automated vehicles (AVs) need to communicate with other traffic participants . Hence , a user centred implementation of manual driving behaviour is considered as beneficial in AVs . Currently, manual driving is often coordinated by implicit communication. Thus, specific parameters regarding implicit communication and potential influencing factors need to be further investigated for the implementation in AVs. The present video simulation study investigated the effects of participants’ age, vehicle types and vehicles’ speed on participants’ gap acceptance ( GA). P re recorded real world video ma terial displ ayed a left turn parking scenario from the drivers’ perspective including an overlap of the oncoming vehicles’ trajectory . F our different vehicle types (truck, passenger car, scooter, bicycle) approaching with four different speeds re spectively (10 25 km/h) were presented to N 42 particip ants (including two age groups: 30 years vs. > 45 years). The results show ed that t ime gaps increased by vehicles’ size, with smallest gaps for the bicycle and largest for the truck. Despite a similar obje ct size, the selected gaps for the scooter and the bicycle also differed . Moreover, lower time gaps were selected for higher speed level s (i.e., riskier time gaps). Older participants preferred mor e conservative gaps compared to younger participants (i.e., larger time gaps). Besides safety issues, AVs should particularly consider speed related time gaps to meet human expectations and enhance comfort and acceptance. Age related time gaps could serve as a basis for different automated driving style profiles, e.g. defensive vs. dynamic driving style.

External human-machine interfaces (eHMIs) could enhance the inter-action between automated vehicles and surrounding traffic participants if im-plicit signals are insufficient. Traffic safety requires an appropriate level of trust considering potential eHMI failures. Thus, the current study investigated effects of eHMI system failures on elderly participants’ system assessment. During a video-based study, participants assessed an augmented eHMI in three experi-mental blocks of trials. The first block exclusively displayed valid eHMI signals that acted consistently to the vehicle’s movements. The additional blocks also comprised invalid eHMI signals that mismatched the vehicle’s movements. Re-sults revealed higher ratings of trust and perceived safety for a valid system act-ing consistently to vehicle’s movements compared to an initial assessment without system experience. After experiencing eHMI failures, participants’ sys-tem assessment declined. Results underline that eHMI signals are required to be in line with vehicles’ movements to support traffic safety and smooth commu-nication with other traffic participants.

Classification of physiological data provides a data driven approach to study central aspects of motor control, which changes with age. To implement such results in real-life applications for elderly it is important to identify age-specific characteristics of movement classification. We compared task-classification based on EEG derived activity patterns related to brain network characteristics between older and younger adults performing force tracking with two task characteristics (sinusoidal; constant) with the right or left hand. We extracted brain network patterns with dynamic mode decomposition (DMD) and classified the tasks on an individual level using linear discriminant analysis (LDA). Next, we compared the models’ performance between the groups. Studying brain activity patterns, we identified signatures of altered motor network function reflecting dedifferentiated and compensational brain activation in older adults. We found that the classification performance of the body side was lower in older adults. However, classification performance with respect to task characteristics was better in older adults. This may indicate a higher susceptibility of brain network mechanisms to task difficulty in elderly. Signatures of dedifferentiation and compensation refer to an age-related reorganization of functional brain networks, which suggests that classification of visuomotor tracking tasks is influenced by age-specific characteristics of brain activity patterns. In addition to insights into central aspects of fine motor control, the results presented here are relevant in application-oriented areas such as brain computer interfaces.

Lane changes (LCs) are highly complex, therefore the announcement and anticipation at an early stage of these maneuvers is important for traffic safety. Since manual drivers mainly apply implicit driving cues, automated ve-hicles (AVs) need to be able to detect and apply these cues to enhance the ac-ceptance of AVs and therefore exploit the benefits of automation. The current study aimed at identifying typical communication cues and prototypical se-quence patterns that announce prospective LCs at an early stage. In total, N = 298 LCs were annotated in video recordings of a real-world driving data set. The analysis revealed the turn indicator and the vehicles’ lateral movement towards the target lane as the most frequently and initially applied communica-tion cues to announce LCs. The identified driving cues could be used by AVs to announce and anticipate prospective LCs at an early stage and therefore en-hance traffic safety and efficiency.

Gait analysis plays an important role in various applications such as health care, clinical rehabilitation, sport training and pedestrian navigation. In order to monitor the human gait, an interesting approach is to analyze the foot plantar pressure distribution between the foot and the ground. In recent years, the emergence of flexible, soft and lightweight sensors facilitates the rapid technological advances in in-shoe foot pressure measurements, thereby especially carbon nanotubes-based sensors provide an outstanding solution for the implementation of flexible, soft pressure sensors in foot pressure distribution analysis. This chapter focuses on the design and implementation of multiwalled carbon nanotubes (CNT)/polydimethylsil-oxane (PDMS) based nanocomposite pressure sensors for the analysis of the foot pressure distribution. The sensor is durable, stable and shows sensitivity of 3.3 kΩ Ω /kPa and hysteresis smaller than 3.64% with maximum detectable pressure up to 217 kPa, which is suitable for the measurement of human foot pressure. The proposed sensor has been implemented in a flexible in-sole, which is designed based on normal arch foot anatomy. A total of 12 sensors are distributed in the heel, lateral back foot, midfoot and front foot. The foot pressure distribution for different persons while walking and standing using nanocomposite sensor based in-sole were investigated by measuring the changing in resistance of the pressure sensors, when pressure applied on it. It shows that foot pressure distribution is higher in the fore foot and the heel while person standing in normal position. While walking, initially the foot pressure is in the heel and then transferred to the entire foot and finally it is concentrated on the fore foot.

In this paper, we study the error behavior of the nonequispaced fast Fourier transform (NFFT). This approximate algorithm is mainly based on the convenient choice of a compactly supported window function. So far, various window functions have been used and new window functions have recently been proposed. We present novel error estimates for NFFT with compactly supported, continuous window functions and derive rules for convenient choice from the parameters involved in NFFT. The error constant of a window function depends mainly on the oversampling factor and the truncation parameter.

Foot pressure measurement plays an essential role in healthcare applications, clinical rehabilitation, sports training and pedestrian navigation. Among various foot pressure measurement techniques, in-shoe sensors are flexible and can measure the pressure distribution accurately. In this paper, we describe the design and characterization of flexible and low-cost multi-walled carbon nanotubes (MWCNT)/Polydimethylsiloxane (PDMS) based pressure sensors for foot pressure monitoring. The sensors have excellent electrical and mechanical properties an show a stable response at constant pressure loadings for over 5000 cycles. They have a high sensitivity of 4.4 kΩ/kPa and the hysteresis effect corresponds to an energy loss of less than 1.7%. The measurement deviation is of maximally 0.13% relative to the maximal relative resistance. The sensors have a measurement range of up to 330 kPa. The experimental investigations show that the sensors have repeatable responses at different pressure loading rates (5 N/s to 50 N/s). In this paper, we focus on the demonstration of the functionality of an in-sole based on MWCNT/PDMS nanocomposite pressure sensors, weighing approx. 9.46 g, by investigating the foot pressure distribution while walking and standing. The foot pressure distribution was investigated by measuring the resistance changes of the pressure sensors for a person while walking and standing. The results show that pressure distribution is higher in the forefoot and the heel while standing in a normal position. The foot pressure distribution is transferred from the heel to the entire foot and further transferred to the forefoot during the first instance of the gait cycle.