Feature engineering workflow for activity recognition from synchronized inertial measurement units

The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily life examples of machine learning, the underlying process of time-series feature engineering still seems to be time-consuming. This lengthy process inhibits the development of IMU-based machine learning applications in sport science and precision medicine. This contribution discusses a feature engineering workflow, which automates the extraction of time-series feature on based on the FRESH algorithm (FeatuRe Extraction based on Scalable Hypothesis tests) to identify statistically significant features from synchronized IMU sensors (IMeasureU Ltd, NZ). The feature engineering workflow has five main steps: time-series engineering, automated time-series feature extraction, optimized feature extraction, fitting of a specialized classifier, and deployment of optimized machine learning pipeline. The workflow is discussed for the case of a user-specific running-walking classification, and the generalization to a multi-user multi-activity classification is demonstrated.Comment: Multi-Sensor for Action and Gesture Recognition (MAGR), ACPR 2019 Workshop, Auckland, New Zealan

Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance

CAPAS: A context-aware system architecture for physical activities monitoring

Attribute grammars are widely used by compiler-generators since it allows complete specifications of static semantics. They can also be applied to other fields of research, for instance, to human activities recognition. This paper aims to present CAPAS, a Context-aware system Architecture to monitor Physical ActivitieS. One of the components that is present in the architecture is the attribute grammar which is filled after the prediction is made according to the data gathered from the user through the sensors. According to some predefined rules, the physical activity is validated after an analysis on the attribute grammar, if it meets those requirements. Besides that it proposes an attribute grammar itself which should be able to be incorporated in a system in order to validate the performed physical activity.This work has been supported by FCT – Fundação˜ para a Ciência e Tecnologia within the Project Scope: ˆ UID/CEC/00319/2019

Towards a robotic personal trainer for the elderly

The use of robots in the environment of the elderly has grown significantly in recent years. The idea is to try to increase the comfort and well-being of older people through the employment of some kind of automated processes that simplify daily work. In this paper we present a prototype of a personal robotic trainer which, together with a non-invasive sensor, allows caregivers to monitor certain physical activities in order to improve their performance. In addition, the proposed system also takes into account how the person feels during the performance of the physical exercises and thus, determine more precisely if the exercise is appropriate or not for a specific person.This work was partly supported by the Spanish Government (RTI2018-095390-B-C31) and FCT—Fundação para a Ciência e Tecnologia through the Post-Docscholarship SFRH/BPD/102696/2014 (A. Costa) and UID/CEC/00319/2019

Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks

The difficulty of mountainbike downhill trails is a subjective perception. However, sports-associations and mountainbike park operators attempt to group trails into different levels of difficulty with scales like the Singletrail-Skala (S0-S5) or colored scales (blue, red, black, ...) as proposed by The International Mountain Bicycling Association. Inconsistencies in difficulty grading occur due to the various scales, different people grading the trails, differences in topography, and more. We propose an end-to-end deep learning approach to classify trails into three difficulties easy, medium, and hard by using sensor data. With mbientlab Meta Motion r0.2 sensor units, we record accelerometer- and gyroscope data of one rider on multiple trail segments. A 2D convolutional neural network is trained with a stacked and concatenated representation of the aforementioned data as its input. We run experiments with five different sample- and five different kernel sizes and achieve a maximum Sparse Categorical Accuracy of 0.9097. To the best of our knowledge, this is the first work targeting computational difficulty classification of mountainbike downhill trails.Comment: 11 pages, 5 figure

Using an Indoor Localization System for Activity Recognition

Recognizing the activity performed by users is importantin many application domains, from e-health to home automation. Thispaper explores the use of a fine-grained indoor localization system, basedon ultra-wideband, for activity recognition. The user is supposed to weara number of active tags. The position of active tags is first determinedwith respect to the space where the user is moving, then some position-independent metrics are estimated and given as input to a previouslytrained system. Experimental results show that accuracy values as highas∼95% can be obtained when using a personalized model

EEuGene: employing electroencephalograph signals in the rating strategy of a hardware-based interactive genetic algorithm

We describe a novel interface and development platform for an interactive Genetic Algorithm (iGA) that uses Electroencephalograph (EEG) signals as an indication of fitness for selection for successive generations. A gaming headset was used to generate EEG readings corresponding to attention and meditation states from a single electrode. These were communicated via Bluetooth to an embedded iGA implemented on the Arduino platform. The readings were taken to measure subjects’ responses to predetermined short sequences of synthesised sound, although the technique could be applied any appropriate problem domain. The prototype provided sufficient evidence to indicate that use of the technology in this context is viable. However, the approach taken was limited by the technical characteristics of the equipment used and only provides proof of concept at this stage. We discuss some of the limitations of using biofeedback systems and suggest possible improvements that might be made with more sophisticated EEG sensors and other biofeedback mechanisms

How to prevent ROP in preterm infants in Indonesia?

Background and Aims: Retinopathy of prematurity (ROP) is a severe disease in preterm infants. It is seen more frequently in Low-Middle Income Countries (LMIC) like Indonesia compared to High-Income Countries (HIC). Risk factors for ROP development are -extreme- preterm birth, use of oxygen, neonatal infections, respiratory problems, inadequate nutrition, and blood and exchange transfusions. In this paper, we give an overview of steps that can be taken in LMIC to prevent ROP and provide guidelines for screening and treating ROP. Methods: Based on the literature search and data obtained by us in Indonesia's studies, we propose guidelines for the prevention, screening, and treatment of ROP in preterm infants in LMIC. Results: Prevention of ROP starts before birth with preventing preterm labor, transferring a mother who might deliver <32 weeks to a perinatal center and giving corticosteroids to mothers that might deliver <34 weeks. Newborn resuscitation must be done using room air or, in the case of very preterm infants (<29-32 weeks) by using 30% oxygen. Respiratory problems must be prevented by starting continuous positive airway pressure (CPAP) in all preterm infants <32 weeks and in case of respiratory problems in more mature infants. If needed, the surfactant should be given in a minimally invasive manner, as ROP's lower incidence was found using this technique. The use of oxygen must be strictly regulated with a saturation monitor of 91-95%. Infections must be prevented as much as possible. Both oral and parenteral nutrition should be started in all preterm infants on day one of life with preferably mothers' milk. Blood transfusions can be prevented by reducing the amount of blood needed for laboratory analysis. Discussion: Preterm babies should be born in facilities able to care for them optimally. The use of oxygen must be strictly regulated. ROP screening is mandatory in infants born <34 weeks, and infants who received supplemental oxygen for a prolonged period. In case of progression of ROP, immediate mandatory treatment is required. Conclusion: Concerted action is needed to reduce the incidence of ROP in LMIC. "STOP - R1O2P3" is an acronym that can help implement standard practices in all neonatal intensive care units in LMIC to prevent development and progression

Variability in childhood allergy and asthma across ethnicity, language, and residency duration in El Paso, Texas: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>We evaluated the impact of migration to the USA-Mexico border city of El Paso, Texas (USA), parental language preference, and Hispanic ethnicity on childhood asthma to differentiate between its social and environmental determinants.</p> <p>Methods</p> <p>Allergy and asthma prevalence was surveyed among 9797 fourth and fifth grade children enrolled in the El Paso Independent School District. Parents completed a respiratory health questionnaire, in either English or Spanish, and a sub-sample of children received spirometry testing at their school. Here we report asthma and allergy outcomes across ethnicity and El Paso residency duration.</p> <p>Results</p> <p>Asthma and allergy prevalence increased with longer duration of El Paso residency independent of ethnicity and preferred language. Compared with immigrants who arrived in El Paso after entering first grade (18%), lifelong El Paso residents (68%) had more prevalent allergy (OR, 1.72; 95% CI, 1.32 - 2.24), prevalent asthma (OR, 1.75; 95% CI, 1.24 - 2.46), and current asthma (OR, 2.01; 95% CI, 1.37 - 2.95). Spirometric measurements (FEV₁/FVC and FEF_25-75) also declined with increasing duration of El Paso residency (0.16% and 0.35% annual reduction, respectively).</p> <p>Conclusion</p> <p>These findings suggest that a community-wide environmental exposure in El Paso, delayed pulmonary development, or increased health of immigrants may be associated with allergy and asthma development in children raised there.</p