Sen Qiu

TL;DR: An adaptive stance-phase detection method is proposed based solely on an inertial sensor, which deals with the measurement fluctuations in swing and stance phases differently, and applies a clustering algorithm to partition the potential gait phases into true and false clusters, thereby yielding a time threshold to eliminate the false gait periods.

TL;DR: Experimental results demonstrate that the proposed self-contained inertial/magnetic sensor based method is capable of providing consistent beacon-free PDR in different scenarios, achieving less than 1% distance error and end-to-end position error.

TL;DR: In this paper, a comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning, is presented.

TL;DR: The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved and the proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift.

TL;DR: An adaptive method for gait detection is presented, which models human gait with a hidden Markov model (HMM), and employs a neural network (NN) to deal with the raw measurements and feed the HMM with classifications.