Archiving nature’s heartbeat using smart phones

The impact of urban development and climate change has created the impetus to monitor changes in the environment, particularly, the behaviour, habitat and movement of fauna species. The aim of this chapter is to present the design and development of a sensor network based on smart phones to automatically collect and analyse acoustic and visual data for environmental monitoring purposes. Due to the communication and sophisticated programming facilities offered by smart phones, software tools can be developed to allow data to be collected, partially processed and sent to a remote server over the network for storage and further processing. This sensor network which employs a client-server architecture has been deployed in three applications: monitoring a rare bird species near Brisbane Airport, study of koalas behaviour at St Bees Island, and detection of fruit flies. The users of this system include scientists (e.g. ecologists, ornithologists, computer scientists) and community groups participating in data collection or reporting on the environment (e.g. students, bird watchers). The chapter focuses on the following aspects of our research: issues involved in using smart phones as sensors; the overall framework for data acquisition, data quality control, data management and analysis; current and future applications of the smart phone-based sensor network, and our future research directions.

Simple classification of walking activities using commodity smart phones

People interact with mobile computing devices everywhere, while sitting, walking, running or even driving. Adapting the interface to suit these contexts is important, thus this paper proposes a simple human activity classification system. Our approach uses a vector magnitude recognition technique to detect and classify when a person is stationary (or not walking), casually walking, or jogging, without any prior training. The user study has confirmed the accuracy.

Smart phones and tablets in the early years : a waste of time or a valuable opportunity for accessing information and communicating?

New digital media surrounds us. Little is known, however, about the influence of technology devices such as tablets (e.g. iPads) and smart phones on young children’s lives in home and school settings, and what it means for them throughout their schooling and beyond. Most research to date has focused on children aged six years and older, and much less (with a few exceptions) on preschool-aged children. This article draws on parent interviews to show how family members engage with technology as part of the flow of everyday life. Only time and increased understandings of everyday practices will tell the real values and scope of using digital media.

Pedestrian self-reported exposure to distraction by smart phones while walking and crossing the road

Pedestrian crashes account for approximately 14% of road fatalities in Australia. Crossing the road, while a minor part of total walking, presents the highest crash risk because of potential interaction with motor vehicles. Crash risk is elevated by pedestrian illegal use of the road, which may be widespread (e.g. 20% of crossings at signalised intersections at a sample of sites, Brisbane) and enforcement is rare. Effective road crossing requires integration of multiple skills and judgements, any of which can be hindered by distraction. Observational studies suggest that pedestrians are increasingly likely to ‘multitask’, using mobile technology for entertainment and communication, elevating the risk of distraction while crossing. To investigate this, intercept interviews were conducted with a convenience sample of 211 pedestrians aged 18-65 years in Brisbane CBD. Self-reported frequency of using a smart phone for activities at two levels of distraction: cognitive only (voice calls); or cognitive and visual (text messages, internet access) while walking or crossing the road was collected. Results indicated that smart phone use for potentially distracting activities while walking and while crossing the road was high, especially among 18-30 year olds, who were significantly more likely than 31-44yo or 45-65yo to report smart phone use while crossing the road. For 18-30yo and the higher risk activity of crossing the road, 32% texted at high frequency levels and 27% used internet at high frequency levels. Risky levels of distracted crossing appear to be a growing safety issue for 18-30yo, with greater attention to appropriate interventions needed.

Mobile technology platforms for flexible access to information resources : considerations in the use of ebook readers, smart phones, netbooks and iPads

Smart phones are everywhere, people are taking eBook readers on holiday, the iPad has queues of people wanting to buy one, and some netbooks can fit in a pocket. The technology is attractive and increasingly affordable – how can it help an individual in their access to and use of texts, journals, databases, clinical sources, the web and day-to-day information? The Library has been investigating and trialling mobile devices during 2010, and has received interesting feedback from staff and students on the effectiveness of the technology in the University and personal environments. Each device has its strengths and weaknesses depending on the needs of the individual or activity it is supporting – productivity, research, study, clinical or recreational. The presentation will explore these issues along with some of the practical implications at Deakin, and international experiences in academic environments.

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Comprehensive measures of sound exposures in cinemas using smart phones

OBJECTIVES Sensorineural hearing loss from sound overexposure has a considerable prevalence. Identification of sound hazards is crucial, as prevention, due to a lack of definitive therapies, is the sole alternative to hearing aids. One subjectively loud, yet little studied, potential sound hazard is movie theaters. This study uses smart phones to evaluate their applicability as a widely available, validated sound pressure level (SPL) meter. Therefore, this study measures sound levels in movie theaters to determine whether sound levels exceed safe occupational noise exposure limits and whether sound levels in movie theaters differ as a function of movie, movie theater, presentation time, and seat location within the theater. DESIGN Six smart phones with an SPL meter software application were calibrated with a precision SPL meter and validated as an SPL meter. Additionally, three different smart phone generations were measured in comparison to an integrating SPL meter. Two different movies, an action movie and a children's movie, were measured six times each in 10 different venues (n = 117). To maximize representativeness, movies were selected focusing on large release productions with probable high attendance. Movie theaters were selected in the San Francisco, CA, area based on whether they screened both chosen movies and to represent the largest variety of theater proprietors. Measurements were analyzed in regard to differences between theaters, location within the theater, movie, as well as presentation time and day as indirect indicator of film attendance. RESULTS The smart phone measurements demonstrated high accuracy and reliability. Overall, sound levels in movie theaters do not exceed safe exposure limits by occupational standards. Sound levels vary significantly across theaters and demonstrated statistically significant higher sound levels and exposures in the action movie compared to the children's movie. Sound levels decrease with distance from the screen. However, no influence on time of day or day of the week as indirect indicator of film attendance could be found. CONCLUSIONS Calibrated smart phones with an appropriate software application as used in this study can be utilized as a validated SPL meter. Because of the wide availability, smart phones in combination with the software application can provide high quantity recreational sound exposure measurements, which can facilitate the identification of potential noise hazards. Sound levels in movie theaters decrease with distance to the screen, but do not exceed safe occupational noise exposure limits. Additionally, there are significant differences in sound levels across movie theaters and movies, but not in presentation time.

Positional Accuracy in Smart-Phones and Its Effect on LBS Applications.

Location-based services (LBS) highly rely on the location of the mobile user in order to provide the service tailored to that location. This location is calculated differently depending on the technology available in the used mobile device. No matter which technology is used, the location will never be calculated 100% correctly; instead there will always be a margin of error generated during the calculation, which is referred to as positional accuracy. This research has reviewed the eight most common positioning technologies available in the major current smart-phones and assessed their positional accuracy with respect to its usage by LBS applications. Given the vast majority of these applications, this research classified them into thirteen categories, and these categories were also classified depending on their level criticality as low, medium, or high critical, and whether they function indoor or outdoor. The accuracies of different positioning technologies are compared to these two criteria. Low critical outdoor and high critical indoor applications were found technologically covered; high and medium critical outdoor ones weren?t fully resolved. Finally three potential solutions are suggested to be implemented in future smartphones to resolve this technological gap: Real-Time Kinematics Global Positioning System (RTK GPS), terrestrial transmitters, and combination of Wireless Sensors Network and Radio Frequency Identification (WSN-RFID).

Az információterjedés szerepe az innováció-elfogadásban. Az okostelefonok és az on-line kollektív intelligencia (Role of informationspread in the innovation acceptance: the smart phones and the online collective intelligence)

A tanulmány célja az innováció-terjedés marketingvonzatú irodalmának bemutatása, valamint betekintés az exploratív tartalomelemzés módszerével az informális, kollektív intelligenciát generáló on-line felületek, nevesül a blogok és fórumok kutathatóságába. Az okostelefonok innováció-elfogadásának példáján keresztül a szerzők megpróbálják felderíteni az információterjedés elméletének megvalósulását az elemzett felületeken. Bemutatnak három, a mintára jellemző információterjedési és felvett szerep mintát, melyek alapul szolgálhatnak a további célirányú kutatások számára. / === / Adaptive smart phones that give space for user-added functions create active online discussions. Committed users are ready to share information, advise others, while less expert users seek this information. In their paper the authors show that related user-generated content i.e. blogs and bulletin boards provide a rich data source for analysis, which gives them the opportunity to further elaborate on the diffusion of information in the case of smart phone usage among online Hungarian users. Online collective intelligence may well contribute to the diffusion of innovations through diffusing information. Following a thorough review on the literature on the diffusion of innovations, in their exploratory content analysis, they found two categories of users on the analyzed boards: a first group we dubbed "experts" (corresponding to innovators in Bass's typology) that made a special effort trying to solve particular problems thus contributing to collective intelligence, thus reducing (among others) the perceived complexity of these phones and adding to their trialability, both factors influencing users' innovation acceptance, and a second group, "simple users" (or imitators in Bass's typology), uninterested in product innovation, still asking questions and searching for solutions concerning extant technology. Manufacturers do not seem yet to regard these boards as a source of valuable data, even though these clearly serve as an important pool of information and a growing factor of decision for their potential customers.

Please take out your phones : on the spot solicitation of student feedback in class

The use of mobile devices such as smart phones and tablets in classrooms has been met with mixed sentiments. Some instructors and teachers see them as a distraction and regularly ban their usage. Others who see their potential to enhance learning have started to explore ways to integrate them into their teaching in an attempt to improve student engagement. In this paper we report on a pilot study that forms part of a university-wide project reconceptualising its approach to the student evaluation of learning and teaching. In a progressive decision to embrace mobile technology, the university decided to trial a smart phone app designed for students to check-in to class and leave feedback on the spot. Our preliminary findings from trialling the app indicate that the application establishes a more immediate feedback loop between students and teachers. However, the app’s impact depends on how feedback is shared with students and how the teaching team responds.

Testing the accuracy of luminance maps acquired by smart phone cameras

This paper looks at the accuracy of using the built-in camera of smart phones and free software as an economical way to quantify and analyse light exposure by producing luminance maps from High Dynamic Range (HDR) images. HDR images were captured with an Apple iPhone 4S to capture a wide variation of luminance within an indoor and outdoor scene. The HDR images were then processed using Photosphere software (Ward, 2010.) to produce luminance maps, where individual pixel values were compared with calibrated luminance meter readings. This comparison has shown an average luminance error of ~8% between the HDR image pixel values and luminance meter readings, when the range of luminances in the image is limited to approximately 1,500cd/m2.

An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring

In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts using the smart phone’s in-built accelerometer. The algorithm considers both the variance of an individual’s walking pattern and the orientation of the smart phone. Experimental validation of the algorithm involved the collection of data from 10 participants using five phones (worn at five different body positions) whilst walking on a treadmill at a controlled speed for periods of 5 min. Results indicated that, for steps detected by the OFASD algorithm, there were no significant differences between where the phones were placed on the body (p > 0.05). The mean step detection accuracies ranged from 93.4 % to 96.4 %. Compared to measurements acquired using existing dedicated commercial devices, the results demonstrated that using a smart phone for monitoring physical activity is promising, as it adds value to an accepted everyday accessory, whilst imposing minimum interaction from the user. The algorithm can be used as the underlying component within an application deployed within a smart phone designed to promote self-management of chronic disease where activity measurement is a significant factor, as it provides a practical solution, with minimal requirements for user intervention and less constraints than current solutions.

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user's memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors.

Associations of self-compassion and global self-esteem with positive and negative affect and stress reactivity in daily life: Findings from a smart phone study

The present study examined trait self-compassion and trait self-esteem in relation to positive (PA) and negative affect (NA), as well as their associations with stress reactivity in daily life. One hundred and one subjects completed questionnaires on perceived stress and affect twice a day for 14 consecutive days on smart phones. Results indicated that self-compassion and global self-esteem were positively related to PA and negatively to NA. After controlling for self-esteem, self-compassion remained significantly associated with PA and NA, whereas self-esteem was no longer associated with PA and NA after controlling for self-compassion. Furthermore, results indicated that self-compassion buffered the effect of stress on NA, whereas this was not the case for global self-esteem. Neither self-compassion nor self-esteem moderated the relation of stress on PA in separate models. The results of the present study add to the growing literature regarding beneficial relations of self-compassion and psychological well-being and further emphasize the distinction of self-compassion and global self-esteem.