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

Bone size and bone strength are increased in obese male adolescents

Context: Controversy exists on the effect of obesity on bone development during puberty. Objective: Our objective was to determine differences in volumetric bone mineral density (vBMD) and bone geometry in male obese adolescents (ObAs) in overlap with changes in bone maturation, muscle mass and force development, and circulating sex steroids and IGF-I. We hypothesized that changes in bone parameters are more evident at the weight-bearing site and that changes in serum estradiol are most prominent. Design, Setting, and Participants: We recruited 51 male ObAs (10-19 years) at the entry of a residential weight-loss program and 51 healthy age-matched and 51 bone-age-matched controls. Main Outcome Measures: vBMD and geometric bone parameters, as well as muscle and fat area were studied at the forearm and lower leg by peripheral quantitative computed tomography. Muscle force was studied by jumping mechanography. Results: In addition to an advanced bone maturation, differences in trabecular bone parameters (higher vBMD and larger trabecular area) and cortical bone geometry (larger cortical area and periosteal and endosteal circumference) were observed in ObAs both at the radius and tibia at different pubertal stages. After matching for bone age, all differences at the tibia, but only the difference in trabecular vBMD at the radius, remained significant. Larger muscle area and higher maximal force were found in ObAs compared with controls, as well as higher circulating free estrogen, but similar free testosterone and IGF-I levels. Conclusions: ObAs have larger and stronger bones at both the forearm and lower leg. The observed differences in bone parameters can be explained by a combination of advanced bone maturation, higher estrogen exposure, and greater mechanical loading resulting from a higher muscle mass and strength

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

Applying the adverse outcome pathway (AOP) for food sensitization to support in vitro testing strategies

Background Before introducing proteins from new or alternative dietary sources into the market, a compressive risk assessment including food allergic sensitization should be carried out in order to ensure their safety. We have recently proposed the adverse outcome pathway (AOP) concept to structure the current mechanistic understanding of the molecular and cellular pathways evidenced to drive IgE-mediated food allergies. This AOP framework offers the biological context to collect and structure existing in vitro methods and to identify missing assays to evaluate sensitizing potential of food proteins. Scope and approach In this review, we provide a state-of-the-art overview of available in vitro approaches for assessing the sensitizing potential of food proteins, including their strengths and limitations. These approaches are structured by their potential to evaluate the molecular initiating and key events driving food sensitization. Key findings and conclusions The application of the AOP framework offers the opportunity to anchor existing testing methods to specific building blocks of the AOP for food sensitization. In general, in vitro methods evaluating mechanisms involved in the innate immune response are easier to address than assays addressing the adaptive immune response due to the low precursor frequency of allergen-specific T and B cells. Novel ex vivo culture strategies may have the potential to become useful tools for investigating the sensitizing potential of food proteins. When applied in the context of an integrated testing strategy, the described approaches may reduce, if not replace, current animal testing approaches

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

Inferring complex textile shape from an integrated carbon black-infused ecoflex-based bend and stretch sensor array

We demonstrate how an array of custom-made strain and bend sensors could be integrated into a stretchable sleeve to infer the textile deformation. The angles and elongation measured by the sensors can be used by an optimisation-based algorithm to infer the textile geometrical model by minimising a loss function. We evaluated this on 4 shapes highlighting different body-part characteristics. We demonstrated that a 3.11 mm reconstruction error on complex geometries can be reduced up to 0.08 mm with the computation of angles. This proves the potential of the proposed prototype for capturing the shape of a body parts, muscle density measurement, body shape acquisition, the fabrication of orthoses and prostheses, or to perform movement sensing for human activity recognition, where it could be included in sports leggings for biomechanical analysis, or in everyday garments for motion and gesture sensing

Sex steroids in relation to sexual and skeletal maturation in obese male adolescents

Background: Childhood obesity is associated with an accelerated skeletal maturation. However, data concerning pubertal development and sex steroid levels in obese adolescents are scarce and contrasting. Objectives: To study sex steroids in relation to sexual and skeletal maturation and to serum prostate specific antigen (PSA), as a marker of androgen activity, in obese boys from early to late adolescence. Methods: Ninety obese boys (aged 10-19 y) at the start of a residential obesity treatment program and 90 age-matched controls were studied cross-sectionally. Pubertal status was assessed according to the Tanner method. Skeletal age was determined by an x-ray of the left hand. Morning concentrations of total testosterone (TT) and estradiol (E2) were measured by liquid chromatographytandem mass spectrometry, free T (FT) was measured by equilibrium dialysis, and LH, FSH, SHBG, and PSA were measured by immunoassays. Results: Genital staging was comparable between the obese and nonobese groups, whereas skeletal bone advancement (mean, 1 y) was present in early and midadolescence in the obese males. Although both median SHBG and TT concentrations were significantly (P < .001) lower in obese subjects during mid and late puberty, median FT, LH, FSH, and PSA levels were comparable to those of controls. In contrast, serum E2 concentrations were significantly (P < .001) higher in the obese group at all pubertal stages. Conclusion: Obese boys have lower circulating SHBG and TT, but similar FT concentrations during mid and late puberty in parallel with a normal pubertal progression and serum PSA levels. Our data indicate that in obese boys, serum FT concentration is a better marker of androgen activity than TT. On the other hand, skeletal maturation and E2 were increased from the beginning of puberty, suggesting a significant contribution of hyperestrogenemia in the advancement of skeletal maturation in obese boys