Wearable pressure sensing for intelligent gesture recognition

The development of wearable sensors has become a major area of interest due to their wide range of promising applications, including health monitoring, human motion detection, human-machine interfaces, electronic skin and soft robotics. Particularly, pressure sensors have attracted considerable attention in wearable applications. However, traditional pressure sensing systems are using rigid sensors to detect the human motions. Lightweight and flexible pressure sensors are required to improve the comfortability of devices. Furthermore, in comparison with conventional sensing techniques without smart algorithm, machine learning-assisted wearable systems are capable of intelligently analysing data for classification or prediction purposes, making the system ‘smarter’ for more demanding tasks. Therefore, combining flexible pressure sensors and machine learning is a promising method to deal with human motion recognition. This thesis focuses on fabricating flexible pressure sensors and developing wearable applications to recognize human gestures. Firstly, a comprehensive literature review was conducted, including current state-of-the-art on pressure sensing techniques and machine learning algorithms. Secondly, a piezoelectric smart wristband was developed to distinguish finger typing movements. Three machine learning algorithms, K Nearest Neighbour (KNN), Decision Tree (DT) and Support Vector Machine (SVM), were used to classify the movement of different fingers. The SVM algorithm outperformed other classifiers with an overall accuracy of 98.67% and 100% when processing raw data and extracted features. Thirdly, a piezoresistive wristband was fabricated based on a flake-sphere composite configuration in which reduced graphene oxide fragments are doped with polystyrene spheres to achieve both high sensitivity and flexibility. The flexible wristband measured the pressure distribution around the wrist for accurate and comfortable hand gesture classification. The intelligent wristband was able to classify 12 hand gestures with 96.33% accuracy for five participants using a machine learning algorithm. Moreover, for demonstrating the practical applications of the proposed method, a realtime system was developed to control a robotic hand according to the classification results. Finally, this thesis also demonstrates an intelligent piezoresistive sensor to recognize different throat movements during pronunciation. The piezoresistive sensor was fabricated using two PolyDimethylsiloxane (PDMS) layers that were coated with silver nanowires and reduced graphene oxide films, where the microstructures were fabricated by the polystyrene spheres between the layers. The highly sensitive sensor was able to distinguish throat vibrations from five different spoken words with an accuracy of 96% using the artificial neural network algorithm

Smart Multi-Sensor Wristband for Gesture Classification

No abstract available

How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?

This paper does not contain technical novelty but introduces our key discoveries in a data generation protocol, a database and insights. We aim to address the lack of large-scale datasets in micro-expression (MiE) recognition due to the prohibitive cost of data collection, which renders large-scale training less feasible. To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data. Specifically, we discover three types of Action Units (AUs) that can constitute trainable MiEs. These AUs come from real-world MiEs, early frames of macro-expression videos, and the relationship between AUs and expression categories defined by human expert knowledge. With these AUs, our protocol then employs large numbers of face images of various identities and an off-the-shelf face generator for MiE synthesis, yielding the MiE-X dataset. MiE recognition models are trained or pre-trained on MiE-X and evaluated on real-world test sets, where very competitive accuracy is obtained. Experimental results not only validate the effectiveness of the discovered AUs and MiE-X dataset but also reveal some interesting properties of MiEs: they generalize across faces, are close to early-stage macro-expressions, and can be manually defined.Comment: European Conference on Computer Vision 202

Mitochondrial Function Assessed by 31P MRS and BOLD MRI in Non-Obese Type 2 Diabetic Rats

The study aims to characterize age-associated changes in skeletal muscle bioenergetics by evaluating the response to ischemia-reperfusion in the skeletal muscle of the Goto-Kakizaki (GK) rats, a rat model of non-obese type 2 diabetes (T2D). 31P magnetic resonance spectroscopy (MRS) and blood oxygen level-dependent (BOLD) MRI was performed on the hindlimb of young (12 weeks) and adult (20 weeks) GK and Wistar (control) rats. 31P-MRS and BOLD-MRI data were acquired continuously during an ischemia and reperfusion protocol to quantify changes in phosphate metabolites and muscle oxygenation. The time constant of phosphocreatine recovery, an index of mitochondrial oxidative capacity, was not statistically different between GK rats (60.8 ± 13.9 sec in young group, 83.7 ± 13.0 sec in adult group) and their age-matched controls (62.4 ± 11.6 sec in young group, 77.5 ± 7.1 sec in adult group). During ischemia, baseline-normalized BOLD-MRI signal was significantly lower in GK rats than in their age-matched controls. These results suggest that insulin resistance leads to alterations in tissue metabolism without impaired mitochondrial oxidative capacity in GK rats. © 2016 The Authors

Microalgae as future superfoods: Fostering adoption through practice-based design research

Consumers’ eating habits are gradually changing. In the next few decades, this shift will not be solely dictated by individuals’ decisions but by the need to feed an ever-increasing population in the face of global resources’ impoverishment. Novel superfoods rich in nutrients and produced with sustainable methods, including microalgae, maybe a solution. However, their unusual aspect, the palatability, and the lack of knowledge by most people could be obstacles to adoption. This study aims at encouraging the use of microalgae as food, highlighting the importance that design plays in the transition towards more sustainable production and consumption patterns. Through practice-based design research, characterized by empirical experiments, a survey, an engaging workshop, and the development of a fully-functional open-source product, the authors conceptualize a theoretical framework within which similar product-service systems could thrive. This real-world experimentation is of interest for academics, professionals, makers in the field of design, etc. It suggests that multidisciplinarity, education, and replicability are the keys to addressing this topic and paves the way for further technical and humanistic research

Effect of solvation shell structure on thermopower of liquid redox pairs

Recent advancements in thermogalvanic batteries offer a promising route to efficient harvesting of low-grade heat with temperatures below 100 {\deg}C. The thermogalvanic temperature coefficient {\alpha}, usually referred to as effective thermopower, is the key parameter determining the power density and efficiency of thermogalvanic batteries. However, the current understanding of improving {\alpha} of redox pairs remains at the phenomenological level without microscopic insights, and the development of electrolytes with high {\alpha} largely relies on experimental trial and error. This work applies the free energy perturbation method based on molecular dynamics simulations to predict the {\alpha} of the {Fe^{3+}/Fe^{2+}} redox pair in aqueous and acetone solutions. We showed that {\alpha} of the {Fe^{3+}/Fe^{2+}} redox pair can be increased from 1.5{\pm}0.3 mV/K to 4.1{\pm}0.4 mV/K with the increased acetone to water fraction. The predicted {\alpha} of {Fe^{3+}/Fe^{2+}} both in pure water and acetone show excellent agreement with experimental values. By monitoring the fluctuation of dipole orientations in the first solvation shell, we discovered a significant change in the variance of solvent dipole orientation between Fe^{3+} and Fe^{2+}, which can be a microscopic indicator for large magnitudes of {\alpha}. The effect of acetone weight fraction in the mixed acetone-water solvent on the {\alpha} of {Fe^{3+}/Fe^{2+}} is also studied. Acetone molecules are found to intercalate into the first solvation shell of the {Fe^{2+}} ion at high acetone fractions, while this phenomenon is not observed in the solvation shell of the Fe^{3+} ion. Such solvation shell structure change of {Fe^{2+}} ions contributes to the enhanced {\alpha} at high acetone fractions. Our discovery provides atomistic insights into how solvation shell order can be leveraged to develop electrolytes with high thermopower

Fusion of wearable and contactless sensors for intelligent gesture recognition

This paper presents a novel approach of fusing datasets from multiple sensors using a hierarchical support vector machine algorithm. The validation of this method was experimentally carried out using an intelligent learning system that combines two different data sources. The sensors are based on a contactless sensor, which is a radar that detects the movements of the hands and fingers, as well as a wearable sensor, which is a flexible pressure sensor array that measures pressure distribution around the wrist. A hierarchical support vector machine architecture has been developed to effectively fuse different data types in terms of sampling rate, data format and gesture information from the pressure sensors and radar. In this respect, the proposed method was compared with the classification results from each of the two sensors independently. Datasets from 15 different participants were collected and analyzed in this work. The results show that the radar on its own provides a mean classification accuracy of 76.7%, while the pressure sensors provide an accuracy of 69.0%. However, enhancing the pressure sensors’ output results with radar using the proposed hierarchical support vector machine algorithm improves the classification accuracy to 92.5%

Hierarchical sensor fusion for micro-gestures recognition with pressure sensor array and radar

This paper presents a hierarchical sensor fusion approach for human micro-gesture recognition by combining an Ultra Wide Band (UWB) Doppler radar and wearable pressure sensors. First, the wrist-worn pressure sensor array (PSA) and Doppler radar are used to respectively identify static and dynamic gestures through a Quadratic-kernel SVM (Support Vector Machine) classifier. Then, a robust wrapper method is applied on the features from both sensors to search the optimal combination. Subsequently, two hierarchical approaches where one sensor acts as ‛enhancer‚ of the other are explored. In the first case, scores from Doppler radar related to the confidence level of its classifier and the prediction label corresponding to the posterior probabilities are utilized to maximize the static hand gestures classification performance by hierarchical combination with PSA data. In the second case, the PSA acts as an ‛Enhancer‚ for radar to improve the dynamic gesture recognition. In this regard, different weights of the ‛Enhancer‚ sensor in the fusion process have been evaluated and compared in terms of classification accuracy. A realistic cross-validation method is chosen to test one unknown participant with the model trained by data from others, demonstrating that this hierarchical fusion approach for static and dynamic gestures yields approximately 16.7% improvement in classification accuracy in the best cases