Classifying Human Activity Patterns from Smartphone Collected GPS data: a Fuzzy Classification and Aggregation Approach

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.

A tale of three cities: uncovering human-urban interactions with geographic-context aware social media data

Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people's presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed stochastic models to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people's interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users' location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users' interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places.

Individual level spatial-temporal modelling of exposure potential of livestock in the Cove Wash watershed, Arizona

Personal exposure studies suffer from uncertainty issues, largely stemming from individual behavior uncertainties. Built on spatial-temporal exposure analysis and methods, this study proposed a novel approach to spatial-temporal modeling that incorporated behavior classifications taking into account uncertainties, to estimate individual livestock exposure potential. The new approach was applied in a community-based research project with a Tribal community in the southwest United States. The community project examined the geospatial and temporal grazing patterns of domesticated livestock in a watershed containing 52 abandoned uranium mines (AUMs). Thus, the study aimed to 1) classify Global Positioning System (GPS) data from livestock into three behavior subgroups - grazing, traveling or resting; 2) calculate the daily cumulative exposure potential for livestock; 3) assess the performance of the computational method with and without behavior classifications. Using Lotek Litetrack GPS collars, we collected data at a 20-minute-interval for 2 flocks of sheep and goats during the spring and summer of 2019. Analysis and modeling of GPS data demonstrated no significant difference in individual cumulative exposure potential within each flock when animal behaviors with probability/uncertainties were considered. However, when daily cumulative exposure potential was calculated without consideration of animal behavior or probability/uncertainties, significant differences among animals within a herd were observed, which does not match animal grazing behaviors reported by livestock owners. These results suggest that the proposed method of including behavior subgroups with probability/uncertainties more closely resembled the observed grazing behaviors reported by livestock owners. Results from the research may be used for future intervention and policy-making on remediation efforts in communities where grazing livestock may encounter environmental contaminants. This research also demonstrates a novel robust geographic information system (GIS)-based framework to estimate cumulative exposure potential to environmental contaminants and provides critical information to address community questions on livestock exposure to AUMs.

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions

Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC – a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches—standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs—acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol

Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.

A systematic review of smartphone-based human activity recognition methods for health research

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

Quantified Activity Patterns for Young Children in Beach Environments Relevant for Exposure to Contaminants

In a study to evaluate beach play activities, 120 children were videotaped to observe and quantify factors that could influence their exposure to contaminants in the beach environment. Children aged 1 to 6 years were followed by researchers with video cameras at beaches (two in Miami, Florida and two in Galveston, Texas) for approximately one hour each. Factors evaluated included time spent in various beach locations, various activities engaged in, and various surfaces contacted (including contacts by hand and mouth). Activities recorded in the videos were transcribed to text files to allow for quantitative analyses. Across all sexes, age groups, and beaches, Wading was the most common activity and Seawater was the most common location where children played. The left hand was found to not be in contact with objects most of the time, while the right hand, considered the most dominant hand in most cases, contacted Plastic-Toys the most. Although activity patterns collection through videotaping and videotranslation can be labor-intensive, once collected, they can be widely useful for estimates of exposures to all contaminants in the beach environment (e.g., microorganisms and chemicals) as well as UV exposure, with considerations for whether the contaminants are found in water, sand or both. These activity patterns were collected to potentially look at exposures following the Deepwater Horizon 2010 Spill.

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

BACKGROUND

Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations.

OBJECTIVE

This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being-related outcomes. In addition, it explores users' engagement and possible challenges in using such self-monitoring systems.

METHODS

A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems?

RESULTS

Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred.

CONCLUSIONS

One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users' health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.

Smartphone Sensors for Health Monitoring and Diagnosis

Over the past few decades, we have witnessed a dramatic rise in life expectancy owing to significant advances in medical science and technology, medicine as well as increased awareness about nutrition, education, and environmental and personal hygiene. Consequently, the elderly population in many countries are expected to rise rapidly in the coming years. A rapidly rising elderly demographics is expected to adversely affect the socioeconomic systems of many nations in terms of costs associated with their healthcare and wellbeing. In addition, diseases related to the cardiovascular system, eye, respiratory system, skin and mental health are widespread globally. However, most of these diseases can be avoided and/or properly managed through continuous monitoring. In order to enable continuous health monitoring as well as to serve growing healthcare needs; affordable, non-invasive and easy-to-use healthcare solutions are critical. The ever-increasing penetration of smartphones, coupled with embedded sensors and modern communication technologies, make it an attractive technology for enabling continuous and remote monitoring of an individual's health and wellbeing with negligible additional costs. In this paper, we present a comprehensive review of the state-of-the-art research and developments in smartphone-sensor based healthcare technologies. A discussion on regulatory policies for medical devices and their implications in smartphone-based healthcare systems is presented. Finally, some future research perspectives and concerns regarding smartphone-based healthcare systems are described.

Concordance between GPS-based smartphone app for continuous location tracking and mother's recall of care-seeking for child illness in India

Background

Traditionally, health care-seeking behaviour for child illness is assessed through population-based national demographic and health surveys. GPS-based technologies are increasingly used in human behavioural research including tracking human mobility and spatial behaviour. This paper assesses how well a care-seeking event to a health care facility for child illness, as recalled by the mother in a survey setting using questions sourced from Demographic and Health Surveys, concurs with one that is identified by TrackCare, a GPS-based location-aware smartphone application.

Methods

Mothers residing in the Vadu HDSS area in Pune district, India having at least one young child were randomly assigned to receive a GPS-enabled smartphone with a pre-installed TrackCare app configured to record the device location data at one-minute intervals over a 6-month period. Spatio-temporal parameters were derived from the location data and used to detect a care-seeking event to any of the health care facilities in the area. Mothers were asked to recall a child illness and if, where and when care was sought, using a questionnaire during monthly visits over a 6-month period. Concordance between the mother's recall and the TrackCare app to identify a care-seeking event was estimated according to percent positive agreement.

Results

Mean concordance for a care-seeking event between the two methods (mother's recall and TrackCare location data) ranged up to 45%, was significantly higher (P-value <0.001) for care-seeking at a hospital as compared to a clinic and for a health care facility in the private sector compared to that in the public sector. Overall, the proportion of disagreement for a care-seeking event not detected by TrackCare but reported by mother ranged up to 77% and was significantly higher (P-value <0.001) compared to those not reported by mother but detected by TrackCare.

Conclusions

Given the uncertainty and limitations in use of continuous location tracking data in a field setting and the complexity of classifying human activity patterns, additional research is needed before continuous location tracking can serve as a gold standard substitute for other methods to determine health care-seeking behaviour. Future performance may be improved by incorporating other smartphone-based sensors, such as Wi-Fi and Bluetooth, to obtain more precise location estimates in areas where GPS signal is weakest.

A Machine Learning Approach to Determine Oyster Vessel Behavior

In this work, we address a multi-class classification task of oyster vessel behaviors determination by classifying them into four different classes: fishing, traveling, poling (exploring) and docked (anchored). The main purpose of this work is to automate the oyster vessel behaviors determination task using machine learning and to explore different techniques to improve the accuracy of the oyster vessel behavior prediction problem. To employ machine learning technique, two important descriptors: speed and net speed, are calculated from the trajectory data, recorded by a satellite communication system (Vessel Management System, VMS) attached to the vessels fishing on the public oyster grounds of Louisiana. We constructed a support vector machine (SVM) based method which employs Radial Basis Function (RBF) as a kernel to accurately predict the behavior of oyster vessels. Several validation and parameter optimization techniques were used to improve the accuracy of the SVM classifier. A total 93% of the trajectory data from a July 2013 to August 2014 dataset consisting of 612,700 samples for which the ground truth can be obtained using rule-based classifier is used for validation and independent testing of our method. The results show that the proposed SVM based method is able to correctly classify 99.99% of 612,700 samples using the 10-fold cross validation. Furthermore, we achieved a precision of 1.00, recall of 1.00, F1-score of 1.00 and a test accuracy of 99.99%, while performing an independent test using a subset of 93% of the dataset, which consists of 31,418 points.

Aging in Activity Space: Results From Smartphone-Based GPS-Tracking of Urban Seniors

OBJECTIVES

Prior research emphasizes the importance of the residential neighborhood context during later life but little attention has been afforded to other areas that older adults encounter as they move beyond their residential environments for daily activities and social interactions. This study examines the predominance of the residential context within older adults' everyday lives.

METHOD

We provided 60 older adults in four New York City neighborhoods with iPhones, which captured Global Positioning Systems (GPS) locations at 5-min intervals over 1 week (n = 55,561) and 17 ecological momentary assessments (EMAs) over 4 days (n = 757) to assess real-time activities.

RESULTS

Older adults in our sample spent nearly 40% of their time outside of their residential tracts and they visited 28 other tracts, on average. Exercising, shopping, socializing, and social activities were especially likely to take place outside of residential tracts. Differences in residential and nonresidential poverty exposure vary across gender, race/ethnicity, education, car ownership, and residential areas.

DISCUSSION

Measuring activity space, rather than relying on residential tracts, allows examination of the social environments that are relevant for older adults' everyday lives. Variation in characteristics of activity spaces may be an underexplored source of differences in health and well-being during later life.

Addressing location uncertainties in GPS-based activity monitoring: A methodological framework

Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals' spatio-temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual-collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale-adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone-collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals' life trajectories.

How Sensors Might Help Define the External Exposome

The advent of the exposome concept, the advancement of mobile technology, sensors, and the “internet of things” bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.