Wearable electric potential sensing: a new modality sensing hair touch and restless leg movement

Electric potential sensors (EPS) are classified as capacitive sensors with the ability to measure small variations in electric potential or electric field remotely and accurately. Here we show how a low cost single chip version of EPS can be integrated into a wearable device such as smart watch to provide relevant information about habitual movements specifically, hair touching and scratching as well as leg movement. This new modality could be used in consumer care product research such as studying the quality of shampoos and to study restless leg syndrome remotely without the need of wearing additional sensors. In both scenarios, a single sensor was worn on the wrist, similar to a smart watch, with the sensing electrode pointing away from the body (i.e. no skin contact)

Multi-cellular development: is there scalability and robustness to gain?

Evolving large phenotypes remains nowadays a problem due to the combinatorial explosion of the search space. Seeking better scalability and inspired by the development of biological systems several indirect genetic encodings have been proposed. Here two different developmental mechanisms are compared. The first, developed for hardware implementations, relies on simple mechanisms inspired upon gene regulation and cell differentiation. The second, inspired by Cellular Automata, is an Artificial Embryogeny system based on cell-chemistry. This paper analyses the scalability and robustness to phenotypic faults of these two systems, with a direct encoding strategy used for comparison. Results show that, while for direct encoding scalability is limited by the size of the search space, developmental systems performance appears to be related to the amount of regularity that they can extract from the phenotype. Finally the importance of comparing different genetic encodings is stressed, in particular to evaluate which key characteristics are necessary for better scalability or fault-tolerance. The lack of standard tests or benchmarks is highlighted and some characterisations are proposed

Hardware morphogenetic developmental system

Indirect genotype to phenotype mappings in the form of developmental systems may allow better scalability to larger phenotypes in evolvable hardware. This report reviews developmental systems used in evolvable hardware and it proposes a new classifications based on hardware characteristics. It then describes a genetic encoding and developmental system called the morphogenetic system which has been designed for multi-cellular circuits. This morphogenetic system is inspired upon gene expression and cell differentiation but it focuses on efficient hardware implementation. An hardware implementation on the dynamically reconfigurable POEtic circuit is described. It uses serial arithmetics and time-multiplexing to minimize ressource use

Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge

In this paper we summarize the contributions of participants to the Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp 2018. The SHL challenge is a machine learning and data science competition, which aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. We introduce the dataset used in the challenge and the protocol for the competition. We present a meta-analysis of the contributions from 19 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, two entries achieved F1 scores above 90%, eight with F1 scores between 80% and 90%, and nine between 50% and 80%

Benchmarking the SHL Recognition Challenge with classical and deep-learning pipelines

In this paper we, as part of the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. We aim to recognize eight modes of transportation (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from smartphone inertial sensors: accelerometer, gyroscope and magnetometer. The classical classifiers include naive Bayesian, decision tree, random forest, K-nearest neighbour and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We employ a post-processing scheme to improve the recognition performance. Results show that convolutional neural network operating on frequency domain raw data achieves the best performance among all the classifiers

An Evolving and Developing Cellular Electronic Circuit

A novel multi-cellular electronic circuit capable of evolution and development is described here. The circuit is composed of identical cells whose shape and location in the system is arbitrary. Cells all contain the complete genetic description of the final system, as in living organisms. Through a mechanism of development, cells connect to each other using a fully distributed hardware routing mechanism and differentiate by expressing a corresponding part of the genetic code thereby taking a specific functionality and connectivity in the system. The configuration of the system is found by using artificial evolution and intrinsic evolution at the schematic level is possible. Applications include the approximation of boolean functions and the evolution of a controller capable of navigating a Khepera robot while avoiding obstacles. The circuit is suited for a custom chip called POEtic, which is a generic platform to implement bio-inspired applications

Evolution of Embodied Intelligence

We provide an overview of the evolutionary approach to the emergence of artificial intelligence in embodied behavioral agents. This approach, also known as Evolutionary Robotics, builds and capitalizes upon the interactions between the embodied agent and its environment. Although we cover research carried out in several laboratories around the world, the choice of topics and approaches is based on work carried out at EPFL. We describe a large number of experiments including evolution of single robots in environments of increasing complexity, competitive and cooperative evolution, evolution of vi-sion-based systems, evolution of learning, and evolution of electronics and morphologies for autonomous robot

Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge 2019

In this paper we summarize the contributions of participants to the third Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCAWorkshop of UbiComp/ISWC 2020. The goal of this machine learning/data science challenge is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial sensor data of a smartphone in a user-independent manner with an unknown target phone position. The training data of a “train” user is available from smartphones placed at four body positions (Hand, Torso, Bag and Hips). The testing data originates from “test” users with a smartphone placed at one, but unknown, body position. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 15 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, one submission achieved F1 scores above 80%, three with F1 scores between 70% and 80%, seven between 50% and 70%, and four below 50%, with a latency of maximum of 5 seconds