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It has been a decade since smartphone application stores started allowing developers to post their own applications. This paper presents a narrative review on the state-of-the-art and the future of technology used by researchers in the field of mobile health promotion. Researchers build high cost, complex systems with the purpose of promoting health and collecting data. These systems promote health by using a feedback component that "educates" the subject. Other researchers instead use platforms which provide them with data collected by others, which allows for no communication with subjects, but may be cheaper than building a system to collect the data. This second type of systems cannot be used directly for health promotion. However, both types of systems are relevant to the field of health promotion, because they are precursors to a third type of systems that are emerging, the gig economy systems for mobile health data collection, which are low cost, globally available, and provide limited communication with subjects. If such systems evolve to include more channels for communication with the data-generating subjects, and also bring developers into the economy, they may eventually revolutionize the field of mobile health promotion and data collection by giving researchers new capabilities, such as the ability to replicate existing health promotion campaigns with the click of a button and the appropriate licenses. In this paper we present a review of state-of-the-art systems for mobile health promotion and data collection and a model for what these systems may look like in the future.

Human activity recognition (HAR) from wearable motion sensor data is a promising research field due to its applications in healthcare, athletics, lifestyle monitoring, and computer–human interaction. Smartphones are an obvious platform for the deployment of HAR algorithms. This paper provides an overview of the state-of-the-art when it comes to the following aspects: relevant signals, data capture and preprocessing, ways to deal with unknown on-body locations and orientations, selecting the right features, activity models and classifiers, metrics for quantifying activity execution, and ways to evaluate usability of a HAR system. The survey covers detection of repetitive activities, postures, falls, and inactivity.

Indoor positioning methods using location-sensitive features of available wireless signals can achieve high accuracy. In particular, many state-of-the-art methods exploit quite unique sets of location-dependent received signal strength (RSS) measurements from multiple wireless local area network (WLAN) access points (APs), also called as wireless fingerprints. However, reception of signals from WLAN APs is not often stable, and RSS measurements tend to be unavailable due to WLAN card or AP transient effects, limited sensitivity of WLAN cards, and fluctuating attenuation and reflection of signals due to a multipath environment, structural changes and moving objects. In certain hostile scenarios, bogus APs may be installed to disorient WLAN localisation algorithms. In this study, two approaches are proposed to mitigate the impact of faulty signal strength measurements. Performance figures are provided for both simulated and empirical environments in order to support conclusions.

Thresholding operators have been used successfully for denoising signals, mostly in the wavelet domain. These operators transform a noisy coefficient into a denoised coefficient with a mapping that depends on signal statistics and the value of the noisy coefficient itself. This paper demonstrates that a polynomial threshold mapping can be used for enhanced denoising of Principal Component Analysis (PCA) transform coefficients. In particular, two polynomial threshold operators are used here to map the coefficients obtained with the popular local pixel grouping method (LPG-PCA), which eventually improves the denoising power of LPG-PCA. The method reduces the computational burden of LPG-PCA, by eliminating the need for a second iteration in most cases. Quality metrics and visual assessment show the improvement.

Indoor positioning methods based on wireless local area network (WLAN) signal measurements have gained popularity because of high localisation accuracy. These methods use radio-maps obtained from wireless signal measurement surveys on location grids. Measurement sets from various WLAN access points are called fingerprints and they can be used to identify locations where the measurements are collected. WLAN positioning methods face unexpected changes in signal patterns because of attenuation changes or transient faults in WLAN cards or access points that often make signal strength readings unavailable. This study studies the effect of faulty measurements on the performance of popular state-of-the-art WLAN indoor positioning methods. Additionally, an integrity monitoring preprocessing algorithm is provided that demonstrates a possibility of faulty measurements mitigation for conventional methods such as K-nearest-neighbour. This is achieved by detecting and excluding faulty measurements prior to classification. Performance figures are provided for both simulated and empirical environments.

To realize the promising potential of services delivered via smart phones to help young adults quit smoking at a high level of cost-efficiency, we constructed a texting and mobile media system that was promoted in South Texas via social media advertising and other recruitment channels. During the 6-month service period described here, enrollments were achieved for 798 participants with a mean age of 29.3 years. Seven-month texted follow-up found that 21% (171) of the enrollees reported abstinence at that point. This is consistent with high rates of success found in studies of telephone counseling for young adults and confirms that text and mobile media service specifically designed for young adults provide a feasible and potentially cost-effective approach to promoting cessation.

Cell phones and other mobile devices have become part of human culture and are changing human activity and lifestyle patterns. Mobile phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications. The latest generations of smartphones incorporate global positioning system (GPS) and wireless local area network (WLAN) location finding modules, vision cameras, microphones, accelerometers, temperature sensors, etc. The availability of these sensors in mass-market communication devices creates exciting new opportunities for data mining applications. In particular, healthcare applications exploiting built-in sensors are very promising. This chapter reviews different aspects of human activity recognition, including review of state-of-the-art technology, implementation, and algorithmic aspects. With the advent of miniaturized sensing technology, which can be bodyworn or integrated in mobile devices, it is now possible to collect, store, and process data on different aspects of human physical activity. This data can enable automated activity profiling systems to generate activity patterns over extended periods of time for, e.g., health monitoring. Collection of activity patterns is dependent on recognition algorithms that can efficiently interpret body-worn sensor data. Existing activity recognition systems are constrained by practical limitations such as the number, location, and nature of used sensors. Other issues include ease of deployment, maintenance, costs, and the ability to perform daily activities unimpeded. Sensor outputs might vary for the same activity across different subjects and even for the same individual. Errors can also arise due to variability in sensor signals caused by differences in sensor orientation and placement, and from environmental factors such as temperature sensitivity. This chapter (1) reviews different reported methods addressing human activity recognition or classification problem, (2) analyzes implementation aspects on smartphones, and (3) suggests advanced algorithms. The content is based on the papers published by the authors in Refs. 1–3.

Recently indoor positioning methods based on WLAN signal measurements gained popularity because of high localization accuracy. These methods exploit radio maps obtained from wireless signal measurement surveys on location grids. Measurement sets from various WLAN access points are called fingerprints and characterize locations where the measurements are collected. As WLAN environments do not ensure continuous measurements availability, and faulty or rogue access points may unexpectedly change surveyed signal patterns, resiliency becomes an important issue to address using algorithmic methods. This paper first proposes a general fault model that integrates several reported models. Then performance degradations due to faults are studied for conventional fingerprinting methods. Two improvements to positioning systems are proposed for mitigating the impact of faulty measurements. The first improvement takes into account the intermittent unavailability of AP samples when calculating kNN. The second improvement allows the system to switch from a high accuracy method that works only under normal conditions, to a more resilient method whenever a high number of faults are suspected. Performance figures are provided for positioning with data surveyed from a real environment, to which varying amounts of faults have been introduced artificially.