User's Physical Activity Research Articles

KIJANI App to Promote Physical Activity in Children and Adolescents: Protocol for a Mixed Method Evaluation.

The prevalence of physical inactivity among children and adolescents is alarmingly high despite the well-documented and comprehensive benefits of regular physical activity (PA). Therefore, PA promotion should start early in childhood and adolescence. Although reducing recreational screen time in children and adolescents is an urgent concern, digital approaches have the potential to make activity promotion attractive and age appropriate for the target group. KIJANI is a mobile app approach to promote PA in children and adolescents via gamification and augmented reality. This study protocol aims to describe the KIJANI intervention in detail, as well as the evaluation approach. KIJANI is based on the concept that virtual coins can be earned through PA, for example, in the form of a collected step count. With these coins, in turn, blocks can be bought, which can be used to create virtual buildings and integrate them into the player's real-world environment via augmented reality. PA of users is detected via accelerometers integrated into the smartphones. KIJANI can be played at predefined play locations that were comprehensively identified as safe, child-friendly, and attractive for PA by the target group in a partner project. The evaluation process will be divided into 2 different stages. The phase-I evaluation will be a mixed methods approach with one-on-one semistructured interviews and questionnaires to evaluate the user experience and receive feedback from the target group. After the implementation of results and feedback from the target group, the phase-II evaluation will proceed in the form of a 2-arm randomized controlled trial, in which the effectiveness of KIJANI will be assessed via objectively measured PA as well as questionnaires. The study received ethical approval from the ethical board of the Technical University of Munich. Participants for the phase-I evaluation are currently being recruited. The study will help to determine the efficacy, applicability, and user experience of a gamified activity promotion application in children and adolescents. Overall, digital health approaches provide easy and wide reachability at low cost and are age appropriate and attractive for the target group of adolescents. Strategies have to be developed to apply digital health approaches in the best possible way for activity promotion. DERR1-10.2196/55156.

Towards Automated Assessment of Frailty Status Using a Wrist-Worn Device.

Wearable sensors potentially enable monitoring the user's physical activity in daily life. Therefore, they are particularly appealing for the evaluation of older subjects in their environment, to capture early signs of frailty and mobility-related problems. This study explores the use of body-worn accelerometers for automated assessment of frailty during walking activity. Experiments involved 34 volunteers aged 70+, who were initially screened by geriatricians for the presence of frailty according to Fried's criteria. After screening, the volunteers were asked to walk 60 m at preferred speed, while wearing two accelerometers, one positioned on the lower back and the other on the wrist. Sensor-derived signals were analyzed independently to compare the ability of the two signals (wrist vs. lower back) in frailty status assessment. A gait detection technique was applied to identify segments made of four gait cycles. These segments were then used as input to compute 25 features in time and time-frequency domains, the latter by means of the Wavelet Transform. Finally, five machine learning models were trained and evaluated to classify subjects as robust or non-robust (i.e., pre-frail or frail). Gaussian naive Bayes applied to the features derived from the wrist sensor signal identified non-robust subjects with 91% sensitivity and 82% specificity, compared to 87% sensitivity and 64% specificity achieved with the lower back sensor. Results demonstrate that a wrist-worn accelerometer provides valuable information for the recognition of frailty in older adults, and could represent an effective tool to enable automated and unobtrusive assessment of frailty.

Efficiency of Solar Shading Devices to Improve Thermal Comfort in a Sports Hall

Thermal environment in sports facilities is probably one of the most important parameters, determining the safety and performance of athletes. Such facilities, due to the required operating temperature and physical activity of users, are a serious challenge for both investors and administrators, especially in summer. The additional criterion of low energy consumption in extremely airtight and well-insulated passive buildings often results in overheating of the interior, creating considerable economic and operational problems. The significant need to reduce solar gain during periods of high outdoor temperatures for low-energy buildings prompts a variety of design solutions. Sun shading systems, as an indispensable element of glazed surfaces, are designed to control the amount of solar radiation reaching the building interior, at the same time creating a favorable microclimate inside. This article analyzes the effects of sun shading, which have actually been applied and modified on the southern façade of a passive sports hall in Słomniki. Measurements of the thermal conditions in the hall were the starting point, on the basis of which a model of the object was created in the DesignBuilder program. Using simulation analyses, thermal conditions arising with the use of different variants of internal and external shading devices were studied in the program. The results presented in the article show that in a well-insulated hall of large volume, appropriately selected external shading devices are only able to reduce the access of sunlight to the rooms. External brise-soleils are able to limit the access of solar radiation to the rooms by up to 30%, but this is not enough to guarantee internal thermal comfort. Internal blinds do not affect the interior microclimate significantly and do not protect protection from overheating. Momentary differences in PMV values for different patterns of closing the blinds do not exceed 0.2.

A thread-based wearable sweat nanobiosensor

Non-invasive wearable biosensors provide an efficient way of continuously quantifying a person's biochemical parameters, and are highly valuable for predicting human physiological status and flagging risks and illness. Commercial wearable sensors are available for tracking a user's physical activities, but few could monitor user's health conditions through sweat analysis. Electronic textile (e-textile) biosensors enable new applications in this scenario because of its high flexibility/wearability, low cost, high level of electronic integration, and unobtrusiveness. However, challenges in developing e-textile sweat biosensors remain in the production of textile-based biosensing materials, skin interfacing design, and embedded data acquisition/transmission. Here, we propose a novel wearable electrochemical sweat biosensor based on conductive threads decorated with zinc-oxide nanowires (ZnO NWs) and apply it to detecting lactate and sodium in perspiration during physical exercise. The sweat biosensor is fully integrated with signal readout and data communication circuits in a wearable headband and is capable of monitoring human sweat accurately and wirelessly. We achieved the detection of lactate and sodium in linear ranges of 0–25 mM and 0.1–100 mM and limits of detection of 3.61 mM and 0.16 mM, respectively, which cover the clinically-relevant ranges of lactate and sodium in human sweat. We demonstrated accurate lactate and sodium measurements in human sweat from a healthy volunteer, and the results are in good agreement with standard test results. We also conducted on-body measurements on the same human volunteer during exercise and confirmed the robustness of the signal readout during body movements and the excellent accuracy of the testing results.

Field Test of a Passive Infrared Camera for Measuring Trail-Based Physical Activity.

Introduction: Trails are ubiquitous and far-reaching, but research on the impact trails have on physical activity is limited by the lack of resource-efficient, accurate, and practical systematic observation tools. Commonly used infrared trail sensors count trail use and may broadly differentiate activity (i.e., bicyclist vs. pedestrian), but cannot detect nuances needed for outcomes research such as frequency, intensity, time, and type of activity. Motion-activated passive infrared cameras (PICs), used in ecological research and visitor management in wildlife areas, have potential applicability as a systematic observation data collection tool.Materials and Methods: We conducted a 7-month field test of a PIC as a systematic observation data collection tool on a hiking trail, using photos to identify each trail user's physical activity type, age, sex, and other characteristics. We also tallied hourly trail use counts from the photos, using Bland–Altman plots, paired t-tests, Concordance Correlation Coefficient, Kendall's Tau-b, and a novel inter-counter reliability measure to test concordance against concurrent hourly counts from an infrared sensor.Results: The field test proved informative, providing photos of 2,447 human users of the trail over 4,974 h of data collection. Nearly all of the users were walkers (94.0%) and most were male (69.2%). More of the males used the trail alone (44.8%) than did females (29.8%). Concordance was strong between instruments (p < 0.01), though biased (p < 0.01). Inter-counter reliability was 91.1% during the field study, but only 36.2% when excluding the hours with no detectable trail use on either device. Bland–Altman plots highlighted the tendency for the infrared sensor to provide higher counts, especially for the subsample of hours that had counts >0 on either device (14.0%; 694 h).Discussion: The study's findings highlight the benefits of using PICs to track trail user characteristics despite the needs to further refine best practices for image coding, camera location, and settings. More widespread field use is limited by the extensive amount of time required to code photos and the need to validate the PICs as a trail use counter. The future potential of PICs as a trail-specific PA research and management tool is discussed.

Fabrication of Stretchable and Transparent Core–Shell Polymeric Nanofibers Using Coaxial Electrospinning and Their Application to Phototransistors

Abstract1D semiconducting nanomaterials are promising candidates for wearable electronic devices, in which variability of form (i.e., flexibility and stretchability) is a crucial factor for achieving stable operation during a user's physical activity. Although many stretchable 1D electronic materials have been suggested, they mainly rely on structural engineering, rather than the intrinsic stretchability of the materials, and hence suffer from electrical instability under mechanical deformation. In this work, stretchable core–shell polymeric nanofibers (NFs) are fabricated using coaxial electrospinning. The stretchable core–shell NFs, which consist of a stretchable core and a semiconducting shell, provide both mechanical robustness and superior electrical properties under external strain. The stretchable core–shell NF‐based transistors show high operational stability at up to 30% mechanical strain. Furthermore, fully stretchable organic field‐effect transistors fabricated using core–shell NFs and stretchable conductors exhibit stable operation and high optical transparency (71% of transmittance at a wavelength of 550 nm). The core–shell NFs also exhibit excellent optoelectronic properties, including a maximum light responsivity (R) of 84.2 A W−1 and an external quantum efficiency (EQE) of 178.6%, under illumination at a wavelength of 585 nm. The results demonstrate a viable approach to fabricating wearable electronic devices using stretchable core–shell polymeric NFs.

Smartphone Sensor-Based Human Activity Recognition Robust to Different Sampling Rates

There is a research field of human activity recognition that automatically recognizes a user's physical activity through sensing technology incorporated in smartphones and other devices. When sensing daily activity, various measurement conditions, such as device type, possession method, wearing method, and measurement application, are often different depending on the user and the date of the measurement. Models that predict activity from sensor values are often implemented by machine learning and are trained using a large amount of activity-labeled sensor data measured from many users who provide labeled sensor data. However, collecting activity-labeled sensor data using each user's individual smartphones causes data being measured in inconsistent environments that may degrade the estimation accuracy of machine learning. In this study, I propose an activity recognition method that is robust to different sampling rates -- even in the measurement environment. The proposed method applies an adversarial network and data augmentation by downsampling to a common activity recognition model to achieve the acquisition of feature representations that make the sampling rate unspecifiable. Using the Human Activity Sensing Consortium (HASC), which is a dataset of basic activity recognition using smartphone sensors, I conducted an evaluation experiment to simulate an environment in which various sampling rates were measured. As a result, I found that estimation accuracy was reduced by the conventional method in the above environment and could be improved by my proposed method.

Impact of a riverside accessibility intervention on use, physical activity, and wellbeing: A mixed methods pre-post evaluation

IntroductionAccess to natural outdoor environments can promote physical activity, social cohesion, and improved psychological well-being. In 2016, an urban riverside regeneration project to facilitate access to the riverbank for pedestrians and cyclists was conducted in Barcelona (Spain). We aim to evaluate its effect in terms of changes in use and physical activity of users, and changes in local’s use and perception of the urban riverside, and their corresponding self-perceived health and well-being. MethodsWe conducted systematic observations, before and after the intervention, using the System for Observing Parks and Recreation in Communities (SOPARC) to quantify the use and physical activity levels of users and compared them over time. Qualitative assessment consisted of semi-structured face-to-face interviews with the locals. ResultsWe observed a 25% increase in users of the renovated area of the river after the intervention. There was an increase in sedentary users and those engaged in moderate levels of physical activity [7.7% vs. 12.0% sedentary users, and 66.9% vs. 68.7% moderately active users before and after the intervention respectively, p < 0.001]. The growth of users in the renovated area was mainly driven by females, adults, children, and the non-Caucasian population. Resident interviewees, in general, reported to be happy to live near the river, where they usually go for a stroll, and thought living near the riverside area might benefit their health and well-being. Overall, residents seemed satisfied with the intervention. ConclusionsNature-based interventions in socioeconomically-deprived neighbourhoods might reduce inequalities in access to natural areas, creating attractive destinations for residents, promoting physical activity and/or creating opportunities for social interactions, and improving their health and well-being.

Platys

We introduce a high-level abstraction of location called place . A place derives its meaning from a user's physical space, activities, or social context. In this manner, place can facilitate improved user experience compared to the traditional representation of location, which is spatial coordinates. We propose the Platys framework as a way to address the special challenges of place-aware application development. The core of Platys is a middleware that (1) learns a model of places specific to each user via active learning , a machine learning paradigm that seeks to reduce the user-effort required for training the middleware, and (2) exposes the learned user-specific model of places to applications at run time, insulating application developers from dealing with both low-level sensors and user idiosyncrasies in perceiving places. We evaluated Platys via two studies. First, we collected place labels and Android phone sensor readings from 10 users. We applied Platys' active learning approach to learn each user's places and found that Platys (1) requires fewer place labels to learn a user's places with a desired accuracy than do two traditional supervised approaches, and (2) learns places with higher accuracy than two unsupervised approaches. Second, we conducted a developer study to evaluate Platys' efficiency in assisting developers and its effectiveness in enabling usable applications. In this study, 46 developers employed either Platys or the Android location API to develop a place-aware application. Our results indicate that application developers employing Platys, when compared to those employing the Android API, (1) develop a place-aware application faster and perceive reduced difficulty and (2) produce applications that are easier to understand (for developers) and potentially more usable and privacy preserving (for application users).

Crowdsensing in Urban Areas for City-Scale Mass Gathering Management: Geofencing and Activity Recognition

The widespread availability of smartphones today equipped with several physical and virtual sensors allows to directly collect various information about surrounding physical and logical context for different purposes that range from detecting user's current physical activity and also user presence in a designated area, often referred to as geofencing, to determining current social pulse of individuals and entire communities. Mobile crowdsensing seems a promising solution for enabling the design/development and deployment of a wide range of advanced applications in various fields. In particular, public safety, transportation, and energy monitoring and management in urban environments can benefit from mobile crowdsensing in terms of advanced provisioned applications as well as savings of investments in the urban sensing infrastructure. However, enabling those advanced smart urban applications requires complex signal processing, machine learning, and resource management algorithms that are often beyond the skills of many mobile app developers. This paper describes the pivotal relevance of these facilities for mobile crowdsensing applications and presents our open-source solution, called Mobile Sensing Technology (MoST), for activity detection and geofencing, comparing it with the reference implementations provided by Google as part of the Google Play Services library. Experimental results within the testbed framework of a crowd-management application scenario validate MoST design guidelines and demonstrate the general-purpose, unintrusive, and power-efficient characteristics of MoST sensing capabilities.

스마트 폰의 3축 가속도 센서를 이용한 실시간 물리적 동작 인식 기법

In recent years, research on user's activity recognition using a smart phone has attracted a lot of attentions. A smart phone has various sensors, such as camera, GPS, accelerometer, audio, etc. In addition, smart phones are carried by many people throughout the day. Therefore, we can collect log data from smart phone sensors. The log data can be used to analyze user activities. This paper proposes an approach to inferring a user's physical activities based on the tri-axis accelerometer of smart phone. We propose recognition method for four activity which is physical activity; sitting, standing, walking, running. We have to convert accelerometer raw data so that we can extract features to categorize activities. This paper introduces a recognition method that is able to high detection accuracy for physical activity modes. Using the method, we developed an application system to recognize the user's physical activity mode in real-time. As a result, we obtained accuracy of over 80%.

Design and evaluation of user’s physical experience in an Ambient Interactive Storybook and full body interaction games

The paper presents the design and evaluation of an original Ambient Interactive Storybook (AIS) for children, including its platform, the background story, and 10 full body interactive games. The evaluation, which focused on the user's physical experience and elements important to the designer, has been methodologically derived from the Krofli?'s and Laban's framework Body, Space, Time and Relationship, and additionally supported by sport science measurements. An experiment with 8 participants playing 10 games for 20 min was conducted and recorded to a digital video. Participants' physical experience was evaluated through the analysis of postures, the quality of the movement, the body parts used in the interaction, the playing area, the direction of movement, direction of gaze, tempo, dynamics and the quantity of motion (QoM). Results of the experiment are discussed in relation and with implications for game design. Conclusions are drawn with the summary of main findings, to better understand the mechanisms and principles involved the design of user's physical activity in full body interactive games for children. The theoretical work of Laban and Krofli? proved to be useful for interaction and games design in the transition from desktop to full body interactive games.

Recognizing context for annotating a live life recording

In the near future, it will be possible to continuously record and store the entire audio---visual lifetime of a person together with all digital information that the person perceives or creates. While the storage of this data will be possible soon, retrieval and indexing into such large data sets are unsolved challenges. Since today's retrieval cues seem insufficient we argue that additional cues, obtained from body-worn sensors, make associative retrieval by humans possible. We present three approaches to create such cues, each along with an experimental evaluation: the user's physical activity from acceleration sensors, his social environment from audio sensors, and his interruptibility from multiple sensors.

Mapping cognitive demands in complex problem-solving worlds

Tool builders have focused, not improperly, on tool building--how to build better performing machine problem-solvers, where the implicit model is a human expert solving a problem in isolation. A critical task then for the designer working in this paradigm is to collect human knowledge for computerization in the stand alone machine problem-solver. But tool use involves more. Building systems that are problem-solvers in isolation does not guarantee high performance in actual work contexts where the performance of the joint person-machine system is the relevant criterion. The key to the effective application of computational technology is to conceive, model, design, and evaluate the joint human-machine cognitive system (Hollnagel & Woods, 1983). Like Gestalt principles in perception, a decision system is not merely the sum of its parts, human and machine. The configuration or organization of the human and machine components is a critical determinant of the performance of the system as a whole (e.g. Sorkin & Woods, 1985). The joint cognitive system paradigm (Woods, 1986; Woods, Roth & Bennett, in press) demands a problemdriven, rather than technology-driven, approach where the requirements and bottlenecks in cognitive task performance drive the development of tools to support the human problem-solver. In this paper we describe an approach to understand the cognitive activities performed by joint human-machine cognitive systems. The real impediment to effective knowledge acquisition is the lack of an adequate language to describe cognitive activities in particular domains--what are the cognitive implications of some application's task demands and of the aids and interfaces available to the people in the system; how do people behave/perform in the cognitive situations defined by these demands and tools. Because this independent cognitive description has been missing, an uneasy mixture of other types of description of a complex situation has been substituted---descriptions in terms of the application itself, of the implementation technology of the interfaces/aids, of the user's physical activities or user psychohaetrics. We describe one approach to provide an independent cognitive description of complex situations that can be used to understand the sources of both good and poor performance, i.e. the cognitive problems to be solved or challenges to be met.