Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider stand still detection for indoor localization based on observations from a foot-mounted inertial measurement unit (IMU). The main contribution is a statistical framework for stand-still detection, which is a fundamental step in zero velocity update (ZUPT) to reduce the drift from cubic to linear in time. First, the observations are transformed to a test statistic having non-central chi-square distribution during zero velocity. Second, a hidden Markov model is used to describe the mode switching beten stand still, walking, running, crawling and other possible movements. The resulting algorithm computes the probability of being in each mode, and it is easily extendable to a dynamic navigation framework where map information can be included. Results of first mode probability estimation, second map matching without ZUPT and third step length estimation with ZUPT are provided.
['Jonas Callmer', 'David Törnqvist', 'Fredrik Gustafsson']
I am writing a research paper on a idea Probabilistic stand still detection using foot mounted IMU
[{"from": "human", "value": "I am writing a research paper on a idea Probabilistic stand still detection using foot mounted IMU"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider stand still detection for indoor localization based on observations from a foot-mounted inertial measurement unit (IMU). The main contribution is a statistical framework for stand-still detection, which is a fundamental step in zero velocity update (ZUPT) to reduce the drift from cubic to linear in time. First, the observations are transformed to a test statistic having non-central chi-square distribution during zero velocity. Second, a hidden Markov model is used to describe the mode switching beten stand still, walking, running, crawling and other possible movements. The resulting algorithm computes the probability of being in each mode, and it is easily extendable to a dynamic navigation framework where map information can be included. Results of first mode probability estimation, second map matching without ZUPT and third step length estimation with ZUPT are provided."}]