Smartphone-based Data Collection Research Articles

Voice Assessment in Patients with Amyotrophic Lateral Sclerosis: An Exploratory Study on Associations with Bulbar and Respiratory Function

Background: Speech production is a possible way to monitor bulbar and respiratory functions in patients with amyotrophic lateral sclerosis (ALS). Moreover, the emergence of smartphone-based data collection offers a promising approach to reduce frequent hospital visits and enhance patient outcomes. Here, we studied the relationship between bulbar and respiratory functions with voice characteristics of ALS patients, alongside a speech therapist’s evaluation, at the convenience of using a simple smartphone. Methods: For voice assessment, we considered a speech therapist’s standardized tool—consensus auditory-perceptual evaluation of voice (CAPE-V); and an acoustic analysis toolbox. The bulbar sub-score of the revised ALS functional rating scale (ALSFRS-R) was used, and pulmonary function measurements included forced vital capacity (FVC%), maximum expiratory pressure (MEP%), and maximum inspiratory pressure (MIP%). Correlation coefficients and both linear and logistic regression models were applied. Results: A total of 27 ALS patients (12 males; 61 years mean age; 28 months median disease duration) were included. Patients with significant bulbar dysfunction revealed greater CAPE-V scores in overall severity, roughness, strain, pitch, and loudness. They also presented slower speaking rates, longer pauses, and higher jitter values in acoustic analysis (all p < 0.05). The CAPE-V’s overall severity and sub-scores for pitch and loudness demonstrated significant correlations with MIP% and MEP% (all p < 0.05). In contrast, acoustic metrics (speaking rate, absolute energy, shimmer, and harmonic-to-noise ratio) significantly correlated with FVC% (all p < 0.05). Conclusions: The results provide supporting evidence for the use of smartphone-based recordings in ALS patients for CAPE-V and acoustic analysis as reliable correlates of bulbar and respiratory function.

Intention to participate in smartphone-based data collection: the case of smallholder farmers in Uganda

ABSTRACT Smartphone technology is increasingly being used to conduct surveys, including in low-income countries where the timely collection of socio-economic data is of great importance. This study investigated the intention of Ugandan smallholder farmers to participate in smartphone-based data collection (SPDC). The data was collected through a cross-sectional survey of 306 smallholder farmers. The results show that the willingness of farmers to participate in SPDC is high. The intention to participate in SPDC is directly influenced by attitude, subjective norms and perceived enjoyment. Indirect influences are perceived usefulness, perceived trustworthiness and perceived cost through their effects on attitude. Similarly, perceived ease of use indirectly influences intention through its effects on perceived usefulness and perceived enjoyment. Statistically significant differences were found in terms of age, gender, mobile phone ownership and education. These results show the great potential of SPDC for data collection in Uganda for researchers, policy makers, agribusinesses and stakeholders.

Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.

Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. Participants (N=2394) of the Beiwe Substudy of the Nurses' Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants' key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.

General Review of Smartphone Usability in Noise Mapping

The global urban population is steadily increasing, with more than half of the world's population currently residing in cities, and this trend is expected to double by 2050. As urbanization continues, noise pollution becomes a significant concern, affecting over 60% of major city dwellers and impacting human health on both physiological and psychological levels. To address this issue, governments and organizations are striving to develop effective noise assessment, regulation, and mitigation policies. This literature review explores the role of noise mapping and the potential of smartphones in collecting noise data to inform these policies. Traditional noise mapping techniques and smartphone-based data collection methods are discussed, along with their importance in urban planning, environmental studies, and public health. Key research questions are identified, including the methodologies employed for smartphone-based noise mapping, the accuracy of smartphone-collected data compared to traditional measurements, practical applications, challenges, and emerging trends. The review reveals that smartphones offer a cost-effective and widespread means of gathering noise data, enabling real-time insights and enhancing various domains' practical applications. However, challenges such as data accuracy, privacy concerns, and device limitations must be addressed. The future of smartphone-based noise mapping looks promising, with advancements in sensor technologies, artificial intelligence, and data analysis tools empowering researchers, urban planners, and policymakers to make informed decisions about noise pollution in urban environments.

A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and Validity.

While ecological momentary assessment (EMA) is commonly used to study social contexts and social influence in the real world, EMA almost exclusively relies on participant self-report of present circumstances, including the proximity to influential peers. There is the potential for developing a proximity sensing approach that uses small Bluetooth beacons and smartphone-based detection and data collection to collect information about interactions between individuals passively in real time. This paper aims to describe the methods for evaluating the functionality and validity of a Bluetooth-based beacon and a smartphone app to identify when ≥2 individuals are physically proximal. We will recruit 20 participants aged 18 to 29 years with Android smartphones to complete a 3-week study during which beacon detection and self-report data will be collected using a smartphone app (MEI Research). Using an interviewer-administered social network interview, participants will identify up to 3 peers of the same age who are influential on health behavior (alcohol use in this study). These peers will be asked to carry a Bluetooth beacon (Kontakt asset tag) for the duration of the study; each beacon has a unique ID that, when detected, will be recorded by the app on the participant's phone. Participants will be prompted to respond to EMA surveys (signal-contingent reports) when a peer beacon encounter meets our criteria and randomly 3 times daily (random reports) and every morning (morning reports) to collect information about the presence of peers. In all reports, the individualized list of peers will be presented to participants, followed by questions about peer and participant behavior, including alcohol use. Data from multiple app data sets, including beacon encounter specifications, notification, and app logs, participant EMA self-reports and postparticipation interviews, and peer surveys, will be used to evaluate project goals. We will examine the functionality of the technology, including the stability of the app (eg, app crashes and issues opening the app), beacon-to-app detection (ie, does the app detect proximal beacons?), and beacon encounter notification when encounter criteria are met. The validity of the technology will be defined as the concordance between passive detection of peers via beacon-to-app communication and the participant's EMA report of peer presence. Disagreement between the beacon and self-report data (ie, false negatives and false positives) will be investigated in multiple ways (ie, to determine if the reason was technology-related or participant compliance-related) using encounter data and information collected from participants and peers. Participant recruitment began in February 2023, and enrollment was completed in December 2023. Results will be reported in 2025. This Bluetooth-based technology has important applications and clinical implications for various health behaviors, including the potential for just-in-time adaptive interventions that target high-risk behavior in real time. DERR1-10.2196/50241.

‘I crave not to feel uncomfortable’ – investigating craving for opioids and cannabis among individuals with chronic pain

Background The assessment of drug craving is common in survey studies, including those using real-time data collection methods, such as Ecological Momentary Assessment (EMA). However, few studies investigate how participants with chronic pain interpret the word ‘craving’ and how interpretations impact survey responses. Methods We conducted a mixed-methods study among 12 individuals with chronic pain who were using prescription opioids and cannabis. Participants completed baseline surveys, cognitive interviews, and 14-day smartphone-based EMA data collection. Analyses included deductive and inductive coding of interviews and t-tests of EMA self-reports of opioid and cannabis craving. Results Four participants had negative reactions to the word ‘craving’, including mentions that these questions offended them. The remaining eight participants mentioned no negative connotation. EMA data showed that participants without negative reactions reported a greater range (opioids), higher standard deviation (opioids), and higher maximum (opioids, cannabis) on Likert-type EMA craving items, compared to those with a negative reaction. Conclusions Some individuals with chronic pain may have a negative reaction to the word ‘craving’ related to opioid and cannabis use and this reaction may impact survey responses. Alternative wording of survey items is recommended, for example focused on ‘wanting’ or ‘needing’.

Event-related driver stress detection with smartphones in an urban environment: a naturalistic driving study

Driving in urban areas can be challenging and encounter acute stress. To detect driver stress, collecting data on real roads without interfering the driver is preferred. A smartphone-based data collection protocol was developed to support a naturalistic driving study. Sixty-one participants drove on predetermined real road routes, and driving information as well as physiological, psychological, and facial data were collected. The algorithm identified potentially stressful events based on the collected data. Participants classified these events as low, medium, or highly stressful events by watching recorded videos after the experiment. These events were then used to train prediction models. The best model achieved an accuracy of 92.5% in classifying low/medium/highly stressful events. The contribution of physiological, psychological, and facial expression indices and individual profile information was evaluated. The method can be applied to visualise the geographical distribution of stressors, monitor driver behaviour, and help drivers regulate their driving habits.

Associations between light exposure and sleep timing and sleepiness while awake in a sample of UK adults in everyday life

Experimental and interventional studies show that light can regulate sleep timing and sleepiness while awake by setting the phase of circadian rhythms and supporting alertness. The extent to which differences in light exposure explain variations in sleep and sleepiness within and between individuals in everyday life remains less clear. Here, we establish a method to address this deficit, incorporating an open-source wearable wrist-worn light logger (SpectraWear) and smartphone-based online data collection. We use it to simultaneously record longitudinal light exposure (in melanopic equivalent daylight illuminance), sleep timing, and subjective alertness over seven days in a convenience sample of 59 UK adults without externally imposed circadian challenge (e.g., shift work or jetlag). Participants reliably had strong daily rhythms in light exposure but frequently were exposed to less light during the daytime and more light in pre-bedtime and sleep episodes than recommended [T. M. Brown et al., PLoS Biol. 20, e3001571 (2022)]. Prior light exposure over several hours was associated with lower subjective sleepiness with, in particular, brighter light in the late sleep episode and after wake linked to reduced early morning sleepiness (sleep inertia). Higher pre-bedtime light exposure was associated with longer sleep onset latency. Early sleep timing was correlated with more reproducible and robust daily patterns of light exposure and higher daytime/lower night-time light exposure. Our study establishes a method for collecting longitudinal sleep and health/performance data in everyday life and provides evidence of associations between light exposure and important determinants of sleep health and performance.

Traditional surveys versus ecological momentary assessments: Digital citizen science approaches to improve ethical physical activity surveillance among youth.

The role of physical activity (PA) in minimizing non-communicable diseases is well established. Measurement bias can be reduced via ecological momentary assessments (EMAs) deployed via citizen-owned smartphones. This study aims to engage citizen scientists to understand how PA reported digitally by retrospective and prospective measures varies within the same cohort. This study used the digital citizen science approach to collaborate with citizen scientists, aged 13-21 years over eight consecutive days via a custom-built app. Citizen scientists were recruited through schools in Regina, Saskatchewan, Canada in 2018 (August 31-December 31). Retrospective PA was assessed through a survey, which was adapted from three validated PA surveys to suit smartphone-based data collection, and prospective PA was assessed through time-triggered EMAs deployed consecutively every day, from day 1 to day 8, including weekdays and weekends. Data analyses included paired t-tests to understand the difference in PA reported retrospectively and prospectively, and linear regressions to assess contextual and demographic factors associated with PA reported retrospectively and prospectively. Findings showed a significant difference between PA reported retrospectively and prospectively (p = 0.001). Ethnicity (visible minorities: β = - 0.911, 95% C.I. = -1.677, -0.146), parental education (university: β = 0.978, 95% C.I. = 0.308, 1.649), and strength training (at least one day: β = 0.932, 95% C.I. = 0.108, 1.755) were associated with PA reported prospectively. In contrast, the number of active friends (at least one friend: β = 0.741, 95% C.I. = 0.026, 1.458) was associated with retrospective PA. Physical inactivity is the fourth leading cause of mortality globally, which requires accurate monitoring to inform population health interventions. In this digital age, where ubiquitous devices provide real-time engagement capabilities, digital citizen science can transform how we measure behaviours using citizen-owned ubiquitous digital tools to support prevention and treatment of non-communicable diseases.

Increasing the Acceptance of Smartphone-Based Data Collection.

To study human behavior, social scientists are increasingly collecting data from mobile apps and sensors embedded in smartphones. A major challenge of studies implemented on general population samples, however, is that participation rates are rather low. While previous research has started to investigate the factors affecting individuals' decision to participate in such studies, less is known about features of the study design which are under the researcher's control and can increase the acceptance of smartphone-based data collection methods. Guided by the Technology Acceptance Model, we varied study characteristics in a vignette experiment to examine their effect on individuals' willingness to download a research app on their smartphone. Data were collected from 1,876 members of the NORC AmeriSpeak Panel, a probability-based panel of the general population aged 18+ in the United States. Respondents were randomly assigned to eight vignettes and, after each vignette, were asked to rate their willingness to participate in the described hypothetical study. The results show that individuals are more willing to participate in smartphone-based studies where they have some control over the data collection process, by having the option either to temporarily switch off the data collection or to review the data before submission. Furthermore, they are more willing to participate in research to which they are invited via postal letter rather than receiving a postal letter plus a phone call from an interviewer who walks them through the app installation. Finally, unconditional incentives increase their willingness to engage with smartphone-based data collection over conditional incentives.

Symptom-based stratification algorithm for heterogeneous symptoms of dry eye disease: a feasibility study.

To test the feasibility of a dry eye disease (DED) symptom stratification algorithm previously established for the general population among patients visiting ophthalmologists. This retrospective cross-sectional study was conducted between December 2015 and October 2021 at a university hospital in Japan; participants who underwent a comprehensive DED examination and completed the Japanese version of the Ocular Surface Disease Index (J-OSDI) were included. Patients diagnosed with DED were stratified into seven clusters using a previously established symptom-based stratification algorithm for DED. Characteristics of the patients in stratified clusters were compared. In total, 426 participants were included (median age [interquartile range]; 63 [48-72] years; 357 (83.8%) women). Among them, 291 (68.3%) participants were diagnosed with DED and successfully stratified into seven clusters. The J-OSDI total score was highest in cluster 1 (61.4 [52.2-75.0]), followed by cluster 5 (44.1 [38.8-47.9]). The tear film breakup time was the shortest in cluster 1 (1.5 [1.1-2.1]), followed by cluster 3 (1.6 [1.0-2.5]). The J-OSDI total scores from the stratified clusters in this study and those from the clusters identified in the previous study showed a significant correlation (r = 0.991, P < 0.001). The patients with DED who visited ophthalmologists were successfully stratified by the previously established algorithm for the general population, uncovering patterns for their seemingly heterogeneous and variable clinical characteristics of DED. The results have important implications for promoting treatment interventions tailored to individual patients and implementing smartphone-based clinical data collection in the future.

Disruptions in Accessing Women's Health Care Services: Evidence Using COVID-19 Health Services Disruption Survey.

The world is experiencing the Coronavirus pandemic and the aftermath consequences leading to lockdown and travel restrictions. The pandemic has brought a far-reaching impact on various sectors, including the economic, and health sectors. Therefore, this study analyses the possible impact of the COVID-19 pandemic on the disruption - in accessing family planning measures i.e. contraception facilities; and in seeking help from women organizations for health services. This study has used the COVID-19 health services disruption survey-2020 and data was obtained from the Institute of Health Metrics and Evaluation. This survey was conducted in 76 countries using the smartphone-based premise data collection platform. Respondents were individual members of the general population ages 15-49 years who were identified as women. Data were collected from 12,354 respondents. Our data analysis has been done on both aggregate samples as well as region-wise samples (i.e. Africa, Asia, Europe, and Latin America & the Caribbean) to assess the change in levels of service delivery in both pre-COVID and post-COVID periods. Descriptive statistics results shows that two particular reasons - unable to access due to lockdown restrictions (5.4%), and fear of being infected with COVID-19 (9.7%) were reported as mostly impacting the access to contraception facilities due to lockdown restriction. Further, the logit regression using socio-economic and demographic variables suggests that geographical location (rural), and poor financial status turned out negative and significant, showing higher odds of facing difficulty than the reference category for the aggregate sample. The region-wise analysis suggests that Europe and Asia are the regions with the highest percentage of respondents reporting unavailability of services during COVID-19. The study concludes that pandemic-related emergencies affect the health care system, especially women-related health care services. The implication of our study indicates the requirement of a supply chain strategy for managing health care demand during emerging situations. So, the disruptions and bottlenecks in health care facilities should be addressed by various governments through appropriate policies and interventions.

Falling for smartphones: A smartphone-centric data collection tool for measuring distracted walking

Falls and severe head injurieshave been increasingly associated with the use ofsmartphoneswhile walking.Agrowing bodyof academic researchidentifiescognitive, perceptual, and motor deficits causedbysmartphone use while walking. Despite the dangers associated withsmartphone use while walking, the true prevalencecan be challenging to assess.To better address the growing injury risk related to smartphone-induced distraction, we have built a smartphone-based data collection tool to assess smartphone use frequency while walking. In the current study, 35 participants installed the data collection app on their Android smartphones. On average, the application collected 520 data points per day. The application also indicated participants walked 12% of their day, and 49% of their time walking included smartphone use. The current study demonstrates the prevalence of smartphone-induced distraction while walking and smartphone applications' potential efficacy to collect behavioral data.

Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review.

BackgroundThe broad availability of smartphones and the number of health apps in app stores have risen in recent years. Health apps have benefits for individuals (eg, the ability to monitor one’s health) as well as for researchers (eg, the ability to collect data in population-based, clinical, and observational studies). Although the number of health apps on the global app market is huge and the associated potential seems to be great, app-based questionnaires for collecting patient-related data have not played an important role in epidemiological studies so far.ObjectiveThis study aims to provide an overview of studies that have collected patient data using an app-based approach, with a particular focus on longitudinal studies. This literature review describes the current extent to which smartphones have been used for collecting (patient) data for research purposes, and the potential benefits and challenges associated with this approach.MethodsWe conducted a scoping review of studies that used data collection via apps. PubMed was used to identify studies describing the use of smartphone app questionnaires for collecting data over time. Overall, 17 articles were included in the summary.ResultsBased on the results of this scoping review, there are only a few studies that integrate smartphone apps into data-collection approaches. Studies dealing with the collection of health-related data via smartphone apps have mainly been developed with regard to psychosomatic, neurodegenerative, respiratory, and cardiovascular diseases, as well as malign neoplasm. Among the identified studies, the duration of data collection ranged from 4 weeks to 12 months, and the participants’ mean ages ranged from 7 to 69 years. Potential can be seen for real-time information transfer, fast data synchronization (which saves time and increases effectivity), and the possibility of tracking responses longitudinally. Furthermore, smartphone-based data-collection techniques might prevent biases, such as reminder bias or mistakes occurring during manual data transfers. In chronic diseases, real-time communication with physicians and early detection of symptoms enables rapid modifications in disease management.ConclusionsThe results indicate that using mobile technologies can help to overcome challenges linked with data collection in epidemiological research. However, further feasibility studies need to be conducted in the near future to test the applicability and acceptance of these mobile apps for epidemiological research in various subpopulations.

Do Vehicles Sense, Detect and Locate Speed Bumps?

Pavement surface condition is an essential metric in providing quality and safe road infrastructure to the commuters. One of the keys to roadway condition monitoring is the detection and classification of roadway speed bumps which affect driving comfort and transport safety. The paper presents a data-driven framework and related field studies on the use of supervised machine learning and smartphone sensor technology for the detection and georeferencing of speed bumps. The study proposes a low-cost and automated method to obtain up-to-date information about speed bumps, with the use of smartphones mounted on vehicles. The proposed methodology is based on readily available and accurate technologies, it can be utilized in crowd-sourced applications for pavement management systems (PMS) and geographical information system (GIS) implementations, and it has already been field-tested for the detection and classification of cracks, rutting, ravelling, patches and potholes, exhibiting accuracy levels higher than 90%. The smartphone-based data collection and speed-bump detection algorithms discussed in this paper are complemented with robust regression analysis and Random Under Sampling (RUS) Boosted trees classification models. Ongoing work will further investigate the automated measurement of the geometric properties of the detected bumps and their compliance with regulatory requirements.

Smartphone-Based Data Collection in Ophthalmology.

Due to their widespread use among the population and their wide range of functions and sensors, smartphones are suitable for data collection for medical purposes. App-supported input masks, patient diaries, and patient information systems, mobile access to the patient file as well as telemedical services will continue to find their way into our field of expertise in the future. In addition, the use of smartphone sensors (GPS and motion sensors, touch display, microphone) and coupling possibilities with biosensors (for example with Continuous Glucose Monitoring [CGM] systems), advanced camera technology, the possibility of regular and appointment independent checking of the visual system (visual acuity/contrast vision) as well as real-time data transfer offer interesting possibilities for patient treatment and clinical research. The present review deals with the current status and future perspectives of smartphone-based data collection and possible applications in ophthalmology.

Big Data‚ KI und Maschinenlernen auf dem Weg zur Precision-Psychiatry – wie verändern sie den therapeutischen Alltag?

Currently, we are witnessing an increasing interest in predictive models and personalized diagnosis and treatment choice in psychiatric research. Against this background, the emerging field of Precision Psychiatry is trying to establish precise diagnostics and personalized therapy through Big Data. Electronic Health Records (EHR), smartphone-based data collection and advances in genotyping and imaging allow for a detailed clinical and neurobiological characterization of numerous patients. In order to revolutionize the treatment of psychiatric disorders, a personalization of psychiatry through machine learning (ML) and artificial intelligence (AI) is needed. We must therefore establish an AI ecosystem to develop and strictly validate custom-tailored AI and ML solutions. Furthermore, personalized predictions and detailed patient information must be integrated in AI-based Clinical Decision Support systems. Only in this way can Big Data, ML and AI support the clinician most effectively and help personalize treatment in psychiatry.

Leveraging Smartphone-Based Data Collection to Map Homogenous Longitudinal Trajectory Subgroups of Self-Report Symptoms in the Early Aftermath of Trauma

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among trauma survivors. Traditional classification of APNS (e.g., posttraumatic stress, pain) artificially fragment APNS into syndromes based on medical specialty bailiwick, unmoored to brain function. Mapping trajectories of discrete symptom types over time, assessing biobehavioral markers of these trajectories, and evaluating how these trajectories developmentally interact and aggregate may provide new insights into APNS pathogenesis and/or treatment.

Qualitative Collaborative Sensing In Smart Phone Based Wireless Sensor Networks

Collaborative sensing has become a novel approach for smart phone based data collection. In this process individuals contributes to the participatory data collection by sharing the data collected using their smart phone sensors. Since the data is gathered by human participants it is difficult to guarantee the Quality of the data received. Mobility of the participant and accuracy of the sensor also matters for the quality of data shared in such environment. If the data shared by such participants are of low quality the purpose of collaborative sensing fails. So there must be approach to gather good quality of data from participants. In this paper we propose a Truth Estimation Algorithm (TEA) to identify the truth value of the data received and filter out anomalous data items to improve the quality of data. To encourage the participants to share quality information we also propose an Incentive Allocation Algorithm (IAA) for qualitative data collection.

Smartphone-Based Data Collection System for Repetitive Concrete Temperature Monitoring in High-Rise Building Construction

The systematic collection and management of on-site information in high-rise building construction are important factors in construction management. Recently, wireless sensor network (WSN) technology has been utilized to manage the various tasks involved in high-rise construction efficiently and in a timely manner. However, because of the repeated installation of sensors and repeaters along with the construction progress, the existing WSN technology is ineffective when applied to the temperature management of concrete in structural work. Here, we propose a new data collection method in which a worker uses a smartphone to repeatedly monitor concrete temperature. In field implementation, the proposed system enables concrete temperature management without a transmission gap for monitoring in 60-min intervals with smartphones provided to 20% of the structural workers. Next, a case study was performed on a high-rise building construction site to analyze the effectiveness of the proposed system in terms of cost savings by avoiding schedule delay. The results of the case study show that the proposed system can reduce the additional work costs resulting from delays in concrete curing and save up to $18,907 in labor costs. In addition, this system can reduce the temperature management time of the quality manager and enable more efficient management. It is also expected that this system will contribute to on-site waste management by reducing the number of embedded sensors.