Thomas Moder

Smartphone-based indoor positioning utilizing motion recognition

More than half of the population in Western Europe and North America own a smartphone, providing a large market for both indoor and outdoor location-based services. In order to gain a ubiquitous solution for a smartphone-based indoor positioning, motion recognition may be utilized. Motion recognition can be used to adapt relative positioning solutions as well as the position filtering process. The presented motion recognition is based on classic machine learning techniques, filtered within the time and motion domain to gain a more robust estimation. The outcome of the motion recognition is used within a Pedestrian Dead Reckoning (PDR) algorithm as well as in a particle filter, but is especially helpful within the step detection process of the PDR. Within the step length estimation of PDR, the step length is strongly overestimated when walking on stairs. Contrary, when walking fast, the step length is underestimated by standard step length models. This estimation can be improved using motion recognition.

3D indoor positioning with pedestrian dead reckoning and activity recognition based on Bayes filtering

Since state-of-the-art smartphones do usually not comprise barometers, ubiquitous 3D indoor positioning requires a compensation of the missing height information. A pedestrian activity classification (PAC) algorithm enabling the activity detection of going up- or downstairs can deliver this missing information. Additionally, this PAC can be used for the support of pedestrian dead reckoning (PDR) algorithms. An efficient PAC assists PDR algorithms by using activity information for the reduction of errors within step length estimation. Within this paper, a PAC based on inertial smartphone measurements followed by a stair detection to constrain floor changes in the multi-level filtering process is illustrated. The output of the PAC, the absolute WLAN positioning, as well as the PDR algorithm are filtered within a particle filter and presented within this paper.

Evaluation of smartphone-based indoor positioning using different Bayes filters

Within the research project LOBSTER, a system for analyzing the behavior of escaping groups of people in crisis situations within public buildings to support first responders is developed. The smartphone-based indoor localization of the escaping persons is performed by using positioning techniques like WLAN fingerprinting and dead reckoning realized with MEMS-IMU. Hereby, WLAN fingerprinting is analyzed especially in areas of few access points and the IMU-based dead reckoning is accomplished using step detection and heading estimation. The data of all sensors are fused in combination with building layouts using different Bayes filters. The behavior of the Bayes filters is investigated especially within indoor environments. The restrictions of the Kalman filter are shown as well as the advantages of a Particle filter using building plans.

Walking Aid Identification Using Wearables

The distribution of wrist-worn wearable devices grows rapidly, also among aging people. Within such wearables, inertial sensors are incorporated and may be used, next to their intended purpose, for identifying the currently used walking aid of the senior. After detecting whether the user is moving or not moving, a machine learning approach can be used to identify the currently used walking aid using acceleration and angular rate features. To overcome the wearable attitude uncertainty, the computed features are based on the normalized measurements of the three sensor axes, and they overlap at approximately 0.25 s. The defined walking aids for this approach include standing, walking normal, use of a walking cane and the use of a walker or a wheelchair. A ten-fold cross validation with the labelled training data delivers recall values of 98 % for a window size of 2.56 s. When predicting the currently used walking aid in real time, blunders may occur in the classification. Such blunders can additionally be overcome by the modelling of the probability of the transition between the use of one walking aid to the use of another. The determination of the used walking aid in real time delivers 97 % correctly identified walking aids within defined test scenarios. The identification of the currently used walking aid is mainly used as input parameter for positioning or routing applications, e.g., planning a path which is walkable with the currently used walking aid.

Indoor Navigation and Audiovisual Aiding Within Public Transport

In this paper, we present a specially designed indoor navigation and audiovisual aiding system for blind or visually impaired people within public transport. The developed system relies on a positioning algorithm, which is based on inertial and radio signal data. With an additional map-matching process, the position solution is restricted to a routing graph, which is designed on the basis of a tactile paving network. In addition, we developed an audiovisual operator help service. With this webRTC based technology, the help seeking user can make an audiovisual call to an acquaintance or professional operator.

Calibration of smartphone sensor data usable for pedestrian dead reckoning

There are an increasing number of smartphone applications using indoor positioning. Ideally, no additional infrastructure is needed and only sensors already present in smartphones are used to compute positions indoors. Therefore, approaches such as pedestrian dead reckoning and human motion monitoring are currently being reviewed. In fact, these approaches are not only being reviewed by research facilities, but are already implemented as application programming interfaces in smartphone operating systems. Step detection, turn detection or magnetic orientation exist as implemented interfaces. However, these interfaces use the raw, uncalibrated smartphone sensor measurements. The presented research analyzes the sensors that can be used for pedestrian dead reckoning, discusses how to calibrate sensor measurements without costly equipment and shows the benefits of using calibrated rather than raw, uncalibrated sensor measurements for pedestrian dead reckoning applications.

Indoor position determination using location fingerprinting and vehicle sensor data

In this paper we present a system for position determination of vehicles in indoor areas such as parking garages or underground parking lots. By combining location fingerprinting using Bluetooth signal strengths and relative positioning using vehicle sensor data (speed and yaw rate) in a collective filter arrangement, an ubiquitous and stable vehicle positioning is provided. Hereby, both a Kalman filter and a particle filter were investigated and compared. By further incorporating signals from Global Navigation Satellite Systems in outdoor areas, a seamless outdoor-indoor positioning could be achieved. With the presented techniques, an accuracy of <; 2 m is feasible at a low financial effort.