Model - based and Experimental Analysis of the Symmetry in Human Walking in Different Device Carrying Modes

The advent of embedded sensors and their low cost integration in handheld devices (e.g. smartphones) are making them increasingly aware of the human location and context. There have been attempts to extract certain gait features (e.g. step length, step frequency etc.) based on data recorded from handheld devices. However, these attempts have been mostly inspired from observations in biomechanics. Hence, there is a profound need to study the modeling of human walking gait cycle while taking into account the different device carrying modes. It is hypothesized that the presence of handheld device in one hand can alter the step level symmetry of human walking gait cycle without affecting the stride level symmetry. The aim of this paper is to present a model of human walking gait cycle in different device carrying modes over a stride, which is based on parametric optimization technique used in robotics motion generation and the results of a preliminary experimentation conducted using motion capture technology. Both simulation and pilot experiments confirm that the presence of a small mass in one hand can affect the step level symmetry of the human walking gait which constitutes the novel outcome of this paper. Overall, the model successfully captures human walking features and can stand useful for the enhancement of existing pedestrian navigation algorithms with handheld devices for an increased autonomy of elderly people and pedestrian's mobility in general

Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context