Yaqian Xu

DCCLA: Automatic Indoor Localization Using Unsupervised Wi-Fi Fingerprinting

People spend most of their time in a few significant places and often indoors in a small number of select rooms and locations. Indoor localization in terms of a users current place, related to a users daily life, routines or activities, is an important context. We implemented an automatic approach DCCLA Density-based Clustering Combined Localization Algorithm to automatically learn the Wi-Fi fingerprints of the significant places based on density-based clustering. In order to accommodate the influence of the signal variation, clustering procedure separately works on a list of RSSIs Received Signal Strength Indicators from each AP Access Point. In this paper, the approach is experimentally investigated in a laboratory setup and a real-world scenario in an office area with adjacent rooms, which is a key challenge to distinguish for place learning and recognition approaches. From these experiments, we compare and identify the most suitable parameters for the unsupervised learning.

An Ontology-Based Reasoning Framework for Context-Aware Applications

Context-aware applications process context information to support users in their daily tasks and routines. These applications can adapt their functionalities by aggregating context information through machine-learning and data processing algorithms, supporting users with recommendations or services based on their current needs. In the last years, smartphones have been used in the field of context-awareness due to their embedded sensors and various communication interfaces such as Bluetooth, WiFi, NFC or cellular. However, building context-aware applications for smartphones can be a challenging and time-consuming task. In this paper, we describe an ontology-based reasoning framework to create context-aware applications. The framework is based on an ontology as well as micro-services to aggregate, process and represent context information.

From the lab to the real-world: An investigation on the influence of human movement on Emotion Recognition using physiological signals

The recognition of human emotions using physiological signals such as Electrodermal Activity (EDA), Electrocardiogram (ECG) or Electromyography (EMG), has been extensively researched in the past attracting a lot of interest during the last few decades. Although showing a relatively satisfactory performance under lab conditions, Emotion Recognition (ER) systems using physiological signals are not widely used in real-world scenarios. One important fact is that, in the real world, physiological signals may be influenced by human movement and therefore, they cannot be used as a unique indicative of emotions. In this paper, we investigate the influence of human movement on ER using physiological signals. We compare different measures of emotion before and after a test person has performed some physical activity (e.g. walking, going upstairs). We discuss the main differences between recognizing emotions in the lab and the real world and provide new insights into the development of ER systems in real-world scenarios.

Unsupervised Indoor Localization with Motion Detection

Unsupervised indoor localization has received increasing attention in recent years. It enables automatically learning and recognizing the significant locations from Wi-Fi measurements continuously collected from mobile devices in a user’s daily life, without requiring data annotation from professional staff or users. However, such systems suffer from continuous Wi-Fi collection, which results in a high power consumption of mobile devices. These problems can be addressed through activating Wi-Fi collection when it is necessary and deactivating Wi-Fi collection when “enough” data is collected. By using the acceleration readings from the embedded accelerometer sensor, a motion detection algorithm is implemented for an unsupervised localization system DCCLA (Density-based Clustering Combined Localization Algorithm). The information of motion states (i.e. a mobile device in motion or not in motion) is then used to automatically activate and deactivate the process of Wi-Fi collection, and thus save power. Tests carried out by different users in real-world scenarios show an improved performance of unsupervised indoor localization, in terms of location accuracy and power consumption.

WHERE: An Autonomous Localization System with Optimized Size of the Fingerprint Database

Wi-Fi fingerprinting without site surveys is one interesting approach for indoor localization. Current approaches in this field either achieve high accuracy with a large fingerprint database, or yield lower accuracy when the database size is small. In this paper, we propose a novel RSS (Received Signal Strength)-range based approach for fingerprint building, which optimizes the size of the fingerprint database while maintaining the accuracy at the same level. In this approach, a fingerprint is a low-dimensional vector of RSS-ranges, which are extracted from a high-dimensional vector of Wi-Fi scans in the process of fingerprint building. The proposed approach is used and evaluated in the autonomous localization system, which we call WHERE. The evaluation results show the system can optimize the size of the fingerprint database while maintaining an accuracy of room-level.