Advances in Micro-Electro-Mechanical Systems (MEMS) technology play a central role in the design of new generation of smartphones. Indeed MEMS sensors, such as accelerometers and gyroscopes, are currently embedded in most smart devices in order to augment their capabilities. In the near future, it is expected that these sensors will be further exploited for pedestrian navigation purposes. However, the processing of signals from MEMS sensors cannot provide accurate navigation solutions without external aiding, e.g. from GNSS (Global Navigation Satellite Systems) signals, since their signals deteriorate due to significant errors, principally biases and drift which requires frequent sensor resets.
When GNSS is not available and the sensors are mounted on the user’s foot, periodic zero velocity updates can be performed during the identified stance phases of the foot, namely the periods when the foot is flat on the ground. In the case of handheld devices, this approach cannot be adopted, since zero velocity periods are not present. Furthermore, when the sensors are held in a hand, the sensed motion can be decoupled from the global user’s motion rendering the situation much more complex to deal with. For this reason previous studies on pedestrian navigation are mainly focused on the body fixed sensor case.
In this thesis, algorithms for characterizing the gait cycle of a pedestrian holding an IMU (Inertial Measurement Unit) in hand are proposed but without constraining the user in its behaviour and thus taking into account several sensor carrying modes. In view of the variety and complexity of human motions, the recognition of the user’s global motion from handheld devices is first thoroughly examined. A classifier able to recognize several different motion modes, including standing, walking, running, climbing and descending the stairs, is designed and implemented. Then an algorithm for evaluating the linear displacement of a pedestrian walking on a flat plane using only a handheld IMU is proposed. The complete algorithm comprises the following three modules: (1) Characterization of the user's activity and recognition of the sensor carrying mode, (2) Step detection and (3) Step length evaluation.
The analysis leads to a novel step length model combining the user’s height, the step frequency and a set of three constants. First a universal model is proposed where the three constants have been trained with 12 different test subjects. Then, the same model is used for 10 different subjects to calibrate individually the set of constants. The validity of both universal and calibrated models is assessed in position domain using the above 10 test subjects. The fitted solution achieves an error between 2.5 and 5 % of the travelled distance, which is comparable with the performance of models proposed in the literature for body fixed sensors.