Estimating the distance between a device and an access point is fundamental for many Wi-Fi-based positioning applications. Methods based on the Received Signal Strength Indicator (RSSI) have limitations in terms of accuracy and stability. This work presents a study on the Wi-Fi Round Trip Time (RTT) technique, which enables more precise distance measurements between a device and a compatible access point by using signal time-of-flight instead of signal attenuation. To achieve this, a native Android application was developed to obtain distances from RTT and facilitate the collection and analysis of experimental data. Additionally, the developed application includes a collaborative and up-to-date database of compatible devices. For testing, two different smartphone models and a Wi-Fi access point capable of responding to RTT requests were used. The results highlight the temporal evolution of the measurements, the limitations of this technology for positioning. Also, we found that Wi-Fi RTT is more reliable in terms of measurements and sampling frequency than Wi-Fi RSSI fingerprinting.

Wi-Fi-based indoor positioning applications usually employ methods based on the Received Signal Strength Indicator (RSSI), an indicator of the signal attenuation. However, relying on this indicator has accuracy and stability limitations, hampering the performance of indoor positioning systems. This work studied the indoor positioning based on Wi-Fi Round Trip Time, which employs the signal’s time-of-flight to accurately measure the distance between compatible devices. We deployed four RTT-compatible access points in a research laboratory and collected RTT measurements from two compatible Android smartphones on 20 reference locations. Then, we compared the centroid, Euclidean-based k-Nearest Neighbours (KNN) and a KNN with an adapted Euclidean distance for RTT data for position estimation using the collected dataset. We also explored the effect of using individual and time-aggregated RTT measurements. The results of the study show that a 1s time aggregation improves the mean errors of the KNN-based methods and that the adapted Euclidean distance provides the best mean positioning errors (0.4−0.6m).

Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.

Over the last years, numerous studies have emerged using Wi-Fi channel state information, enabling device-free (passive) sensing for applications such as motion detection, indoor positioning or human activity recognition. More recently, the development framework for the low-cost ESP32 microcontrollers has added support for obtaining channel state information data. In this work, we collected channel state information data for human activity recognition, where activities are relatively localized with respect to the Wi-Fi infrastructure. The data was collected in several runs, duly spaced in time, and a convolutional neural network model was used for the classification of activities. Classification performance evaluation showed a clear degradation when a model evaluated with data collected 10 minutes after the data used for training showed a 52% relative loss in the accuracy of the classification.

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

In various usage scenarios, smartphones are used as measuring instruments to systematically and unobtrusively collect data measurements (e.g., sensor data, user activity, phone usage data). Unfortunately, in the race towards extending battery life and improving privacy, mobile phone manufacturers are gradually restricting developers in (frequently) scheduling background (sensing) tasks and impede the exact scheduling of their execution time (i.e., Android’s “best effort” approach). This evolution hampers successful deployment of smartphones in sensing applications in scientific contexts, with unreliable and incomplete sampling rates frequently reported in literature. In this article, we discuss the ins and outs of Android’s background tasks scheduling mechanism, and formulate guidelines for developers to successfully implement reliable task scheduling. Implementing these guidelines, we present a software library, agnostic from the underlying Android scheduling mechanisms and restrictions, that allows Android developers to reliably schedule tasks with a maximum sampling rate of one minute. Our evaluation demonstrates the use and versatility of our task scheduler, and experimentally confirms its reliability and acceptable energy usage.

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only$k$-means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future.

Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95thand 99thpercentiles) is highly improved with respect to the traditional fingerprint.