Step Length Estimation in Daily Activities using RSSI-based Techniques

Step length, an essential component in gait analysis, is becoming appealing in many aspects of our life. It can reflect physical fitness among the young and the senior, e.g., obesity, falling probability and severity. It can also help predict the life expectancy of the elderly. Moreover, the disabled or patients with impaired cognitive functions also behave differently from healthy people in terms of step length. Another application of step length estimation is that it leverages non-GPS localisation where the global positioning system (GPS) is restricted or prohibited. Accurate measurements of step length are thus important in numerous applications. Unfortunately, the existing step length measurement techniques are yet matured. Their common drawbacks could be expensive costs, specific location requirements, constraints of human activities to be measured and of the movement direction of the human under test, proneness to errors due to occlusions, modest accuracy, or a combination of these drawbacks. An accurate path loss model between two human feet is also missing. Therefore, this thesis examines step length estimation and distance measurement between human body parts in wireless body area networks (WBANs). The thesis aims to overcome several above drawbacks by proposing novel techniques to estimate the step length of pedestrians, using our developed wearable, unobtrusive hardware during ambulation or other daily activities.