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)

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

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

Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2020

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

Enabling Reproducible Research in Sensor-Based Transportation Mode Recognition With the Sussex-Huawei Dataset

Transportation and locomotion mode recognition from multimodal smartphone sensors is useful to provide just-in-time context-aware assistance. However, the field is currently held back by the lack of standardized datasets, recognition tasks and evaluation criteria. Currently, recognition methods are often tested on ad-hoc datasets acquired for one-off recognition problems and with differing choices of sensors. This prevents a systematic comparative evaluation of methods within and across research groups. Our goal is to address these issues by: i) introducing a publicly available, large-scale dataset for transportation and locomotion mode recognition from multimodal smartphone sensors; ii) suggesting twelve reference recognition scenarios, which are a superset of the tasks we identified in related work; iii) suggesting relevant combinations of sensors to use based on energy considerations among accelerometer, gyroscope, magnetometer and GPS sensors; iv) defining precise evaluation criteria, including training and testing sets, evaluation measures, and user-independent and sensor-placement independent evaluations. Based on this, we report a systematic study of the relevance of statistical and frequency features based on information theoretical criteria to inform recognition systems. We then systematically report the reference performance obtained on all the identified recognition scenarios using a machine-learning recognition pipeline. The extent of this analysis and the clear definition of the recognition tasks enable future researchers to evaluate their own methods in a comparable manner, thus contributing to further advances in the field. The dataset and the code are available online

FLOWERING REPRESSOR AAA(+) ATPase 1 is a novel regulator of perennial flowering in Arabis alpina

Arabis alpina is a polycarpic perennial, in which PERPETUAL FLOWERING1 (PEP1) regulates flowering and perennial traits in a vernalization-dependent manner. Mutagenesis screens of the pep1 mutant established the role of other flowering time regulators in PEP1-parallel pathways. Here we characterized three allelic enhancers of pep1 (eop002, 085 and 091) which flower early. We mapped the causal mutations and complemented mutants with the identified gene. Using quantitative reverse transcriptase PCR and reporter lines, we determined the protein spatiotemporal expression patterns and localization within the cell. We also characterized its role in Arabidopsis thaliana using CRISPR and in A. alpina by introgressing mutant alleles into a wild-type background. These mutants carried lesions in an AAA(+) ATPase of unknown function, FLOWERING REPRESSOR AAA(+) ATPase 1 (AaFRAT1). AaFRAT1 was detected in the vasculature of young leaf primordia and the rib zone of flowering shoot apical meristems. At the subcellular level, AaFRAT1 was localized at the interphase between the endoplasmic reticulum and peroxisomes. Introgression lines carrying Aafrat1 alleles required less vernalization to flower and reduced number of vegetative axillary branches. By contrast, A. thaliana CRISPR lines showed weak flowering phenotypes. AaFRAT1 contributes to flowering time regulation and the perennial growth habit of A. alpina

Summary of SHL Challenge 2023: Recognizing Locomotion and Transportation Mode from GPS and Motion Sensors

In this paper we summarize the contributions of participants to the fifth Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp/ISWC 2023. 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 motion (accelerometer, gyroscope, magnetometer) and GPS (GPS location, GPS reception) sensor data of a smartphone in a user-independent manner. 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. The challenge evaluates the recognition performance by comparing predicted to ground-truth labels at every 10 milliseconds, but puts no constraints on the maximum decision window length. Overall, five submissions achieved F1 scores above 90%, three between 80% and 90%, two between 70% and 80%, three between 50% and 70%, and two below 50%. While the task this year is facing the technical challenges of sensor unavailability, irregular sampling, and sensor diversity, the overall performance based on GPS and motion sensors is better than previous years (e.g. the best performance reported in SHL 2020, 2021 and 2023 are 88.5%, 75.4% and 96.0%, respectively). This is possibly due to the complementary between the GPS and motion sensors and also the removal of constraints on the decision window length. Finally, we present a baseline implementation to help understand the contribution of each sensor modality to the recognition task

Thigh-Derived Inertial Sensor Metrics to Assess the Sit-to-Stand and Stand-to-Sit Transitions in the Timed Up and Go (TUG) Task for Quantifying Mobility Impairment in Multiple Sclerosis

INTRODUCTION: Inertial sensors generate objective and sensitive metrics of movement disability that may indicate fall risk in many clinical conditions including multiple sclerosis (MS). The Timed-Up-And-Go (TUG) task is used to assess patient mobility because it incorporates clinically-relevant submovements during standing. Most sensor-based TUG research has focused on the placement of sensors at the spine, hip or ankles; an examination of thigh activity in TUG in multiple sclerosis is wanting. METHODS: We used validated sensors (x-IMU by x-io) to derive transparent metrics for the sit-to-stand (SI-ST) transition and the stand-to-sit (ST-SI) transition of TUG, and compared effect sizes for metrics from inertial sensors on the thighs to effect sizes for metrics from a sensor placed at the L3 level of the lumbar spine. 23 healthy volunteers were compared to 17 ambulatory persons with MS (PwMS, HAI <= 2). RESULTS: During the SI-ST transition, the metric with the largest effect size comparing healthy volunteers to PwMS was the Area Under the Curve of the thigh angular velocity in the pitch direction -- representing both thigh and knee extension; the peak of the spine pitch angular velocity during SI-ST also had a large effect size, as did some temporal measures of duration of SI-ST, although less so. During the ST-SI transition the metric with the largest effect size in PwMS was the peak of the spine angular velocity curve in the roll direction. A regression was performed. DISCUSSION: We propose for PwMS that the diminished peak angular velocities during SI-ST directly represents extensor weakness, while the increased roll during ST-SI represents diminished postural control. CONCLUSIONS: During the SI-ST transition of TUG, angular velocities can discriminate between healthy volunteers and ambulatory PwMS better than temporal features. Sensor placement on the thighs provides additional discrimination compared to sensor placement at the lumbar spine