A mobile sensing solution for indoor and outdoor state detection

Abstract. One important research challenge in ubiquitous computing is determining a device’s indoor/outdoor environmental state. Particularly with modern smartphones, environmental information is important for enabling of new types of services and optimizing already existing functionalities. This thesis presents a tool for Android-powered smartphones called ContextIO for detecting the device’s indoor/outdoor state by combining different onboard sensors of the device itself. To develop ContextIO, we developed a plugin to AWARE mobile sensing framework. Together the plugin and its separate controller component collect rich environmental sensor data. The data analysis and ContextIO’s design considers collected data particularly about magnetometer, ambient light and GSM cellular signal strength. We manually derive thresholds in the data that can be used in combination to infer whether a device is indoor or outdoor. ContextIO uses the same thresholds to infer the state in real time. This thesis contributes an Android tool for inferring the device’s indoor/outdoor status, an open dataset that other researchers can use in their work and an analysis of the collected sensor data for environmental indoor/outdoor state detection.Tiivistelmä. Yksi jokapaikan tietotekniikan tutkimuskysymyksistä keskittyy selvittämään onko laitteen sijainti sisä- vai ulkotilassa. Etenkin uudet älypuhelimet pystyvät hyödyntämään tätä tietoa uudenlaisten palveluiden ja sovellusten kehittämisessä sekä vanhojen toiminnallisuuksien optimoinnissa. Tämä diplomityö esittelee Android-käyttöjärjestelmällä toimiville puhelimille suunnatun työkalun nimeltään ContextIO. Työkalu yhdistelee älypuhelimen sensorien tuottamaa tietoa ja havaitsee laitteen siirtymisen eri sijaintiin sisä- ja ulkotilojen suhteen. ContextIO:n suunnittelu ja kehitystyö perustuvat data-analyysiin, jonka data kerättiin AWARE-sensorialustan liitännäisellä sekä erillisellä nimeämistyökalulla. Data-analyysi keskittyy magnetometrin, valosensorin sekä GSM-kentän voimakkuuden hyödyntämiseen paikantamisessa. Kerätystä datasta määriteltiin raja-arvot, joita yhdistelemällä voidaan varsin luotettavasti todeta laitteen sijainti sisä- ja ulkotilojen suhteen. Nämä raja-arvot luovat perustan ContextIO:n reaaliaikaiselle laitteen sijainnin määrittämiselle. Tämän diplomityön pääasialliset tulokset ovat työkalu Android-pohjaisten älypuhelinten sijainnin määrittämiseen sisä- ja ulkotilojen suhteen, avoin datasetti, jota muut tutkijat voivat käyttää sekä sijainnin määrittämiseen keskittyvä data-analyysi

Multimodal Sensing for Robust and Energy-Efficient Context Detection with Smart Mobile Devices

Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly growing. There are already over 2 billion smartphone users worldwide [1] and the percentage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. This thesis presents how multimodal sensor data can be integrated to facilitate ro- bust and energy efficient mobile context detection, considering three important and challenging detection tasks: indoor localization, indoor-outdoor detection and human activity recognition. This thesis presents three methods for multimodal sensor inte- gration, each applied for a different type of context detection task considered in this thesis. These are gradually decreasing in design complexity, starting with a solution based on an engineering approach decomposing context detection to simpler tasks and integrating these with a particle filter for indoor localization. This is followed by man- ual extraction of features from different sensors and using an adaptive machine learn- ing technique called semi-supervised learning for indoor-outdoor detection. Finally, a method using deep neural networks capable of extracting non-intuitive features di- rectly from raw sensor data is used for human activity recognition; this method also provides higher degree of generalization to other context detection tasks. Energy efficiency is an important consideration in general for battery powered mo- bile devices and context detection is no exception. In the various context detection tasks and solutions presented in this thesis, particular attention is paid to this issue by relying largely on sensors that consume low energy and on lightweight computations. Overall, the solutions presented improve on the state of the art in terms of accuracy and robustness while keeping the energy consumption low, making them practical for use on mobile devices

Indoor outdoor detection

Abstract. This thesis shows a viable machine learning model that detects Indoor or Outdoor on smartphones. The model was designed as a classification problem and it was trained with data collected from several smartphone sensors by participants of a field trial conducted. The data collected was labeled manually either indoor or outdoor by the participants themselves. The model was then iterated over to lower the energy consumption by utilizing feature selection techniques and subsampling techniques. The model which uses all of the data achieved a 99 % prediction accuracy, while the energy efficient model achieved 92.91 %. This work provides the tools for researchers to quantify environmental exposure using smartphones

Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone

The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy

Learning Human Behaviour Patterns by Trajectory and Activity Recognition

The world’s population is ageing, increasing the awareness of neurological and behavioural impairments that may arise from the human ageing. These impairments can be manifested by cognitive conditions or mobility reduction. These conditions are difficult to be detected on time, relying only on the periodic medical appointments. Therefore, there is a lack of routine screening which demands the development of solutions to better assist and monitor human behaviour. The available technologies to monitor human behaviour are limited to indoors and require the installation of sensors around the user’s homes presenting high maintenance and installation costs. With the widespread use of smartphones, it is possible to take advantage of their sensing information to better assist the elderly population. This study investigates the question of what we can learn about human pattern behaviour from this rich and pervasive mobile sensing data. A deployment of a data collection over a period of 6 months was designed to measure three different human routines through human trajectory analysis and activity recognition comprising indoor and outdoor environment. A framework for modelling human behaviour was developed using human motion features, extracted in an unsupervised and supervised manner. The unsupervised feature extraction is able to measure mobility properties such as step length estimation, user points of interest or even locomotion activities inferred from an user-independent trained classifier. The supervised feature extraction was design to be user-dependent as each user may have specific behaviours that are common to his/her routine. The human patterns were modelled through probability density functions and clustering approaches. Using the human learned patterns, inferences about the current human behaviour were continuously quantified by an anomaly detection algorithm, where distance measurements were used to detect significant changes in behaviour. Experimental results demonstrate the effectiveness of the proposed framework that revealed an increase potential to learn behaviour patterns and detect anomalies