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Micro-sensor human motion capture has shown its potentials because of its ubiquity and low cost. One of the biggest challenges in micro-sensor motion estimation is the drift problem caused by integration of angular rates to obtain orientation. To reduce the drift, existing algorithms make use of gravity and earth magnetic filed measured by accelerometers(More)
Our prototype system mainly consists of three parts:  Sensor subsystem: includes 16-20 micro-sensor nodes (each node contains a 3-axis accelerometer, a 3-axis gyroscope, and a 3axis magnetometer) and a base station connected by a data bus. Sensor nodes are placed on the segments of the human body (head, shoulders, spine, upper and lower limbs) to collect(More)
One of the biggest challenges in micro-sensor motion capture is the drift problem caused by integration of angular rates to obtain orientation estimation. To reduce the drift, existing algorithms make use of gravity and earth magnetic field measured by accelerometers and magnetometers. Unfortunately, the gravity measurement can be strongly interfered by(More)
This paper proposes an ambulatory algorithm to estimate Center of Mass (CoM) displacement of the subjects for Micro-sensor Motion Capture (MMoCap) system. Estimation of the CoM displacement is based on gait analysis and segmental kinematics. In our MMoCap system, the human body is modeled as a kinematic chain of rigid segments linked by joints, and the(More)
Tracking multiple maneuvering targets remains a challenge because of clutter and spurious targets. We propose a Signature-Driven multiple target Tracking (SDT) method which fuses target data in spectral, spatial and temporary spaces to form signatures of targets. Markov properties of target features and dynamics are well defined in the signature of targets(More)
The multiple hypothesis tracking (MHT) approach has been proven to be successful in multiple target tracking applications, however, its computational complexity remains a major hurdle to its practical implementation. This paper presents an efficient MHT implementation, referred to as “GRASP-MHT”, which integrates a greedy randomized adaptive search(More)
The human body displacement estimation in different gait patterns using wearable sensors is extremely challenging due to lack of external references. In this paper, we present a novel algorithm to estimate the Center of Mass (CoM) displacement of human body during walking, running and hopping using 7 body-worn Sensor Measurement Units (SMUs). The lower body(More)