Inertial orientation tracker having gradual automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion

inertial orientation tracker having automatic drift compensation using an at rest sensor for tracking parts of a human body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive sate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion

Inertial orientation tracker apparatus method having automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion

Inertial Orientation Trackers with Drift Compensation

A class of inertial-sensor systems with drift compensation has been invented for use in measuring the orientations of human heads (and perhaps other, similarly sized objects). These systems can be designed to overcome some of the limitations of prior orientation-measuring systems that are based, variously, on magnetic, optical, mechanical-linkage, and acoustical principles. The orientation signals generated by the systems of this invention could be used for diverse purposes, including controlling head-orientation-dependent virtual reality visual displays or enabling persons whose limbs are paralyzed to control machinery by means of head motions. The inventive concept admits to variations too numerous to describe here, making it necessary to limit this description to a typical system, the selected aspects of which are illustrated in the figure. A set of sensors is mounted on a bracket on a band or a cap that gently but firmly grips the wearer s head to be tracked. Among the sensors are three drift-sensitive rotationrate sensors (e.g., integrated-circuit angular- rate-measuring gyroscopes), which put out DC voltages nominally proportional to the rates of rotation about their sensory axes. These sensors are mounted in mutually orthogonal orientations for measuring rates of rotation about the roll, pitch, and yaw axes of the wearer s head. The outputs of these rate sensors are conditioned and digitized, and the resulting data are fed to an integrator module implemented in software in a digital computer. In the integrator module, the angular-rate signals are jointly integrated by any of several established methods to obtain a set of angles that represent approximately the orientation of the head in an external, inertial coordinate system. Because some drift is always present as a component of an angular position computed by integrating the outputs of angular-rate sensors, the orientation signal is processed further in a drift-compensator software module

Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter

Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick-responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations, and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers

Inertial and Magnetic Sensor Data Compression Considering the Estimation Error

This paper presents a compression method for inertial and magnetic sensor data, where the compressed data are used to estimate some states. When sensor data are bounded, the proposed compression method guarantees that the compression error is smaller than a prescribed bound. The manner in which this error bound affects the bit rate and the estimation error is investigated. Through the simulation, it is shown that the estimation error is improved by 18.81% over a test set of 12 cases compared with a filter that does not use the compression error bound

A Zero Velocity Detection Algorithm Using Inertial Sensors for Pedestrian Navigation Systems

In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable

PDR with a Foot-Mounted IMU and Ramp Detection

The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps

Automatic Schaeffer's gestures recognition system

Schaeffer's sign language consists of a reduced set of gestures designed to help children with autism or cognitive learning disabilities to develop adequate communication skills. Our automatic recognition system for Schaeffer's gesture language uses the information provided by an RGB-D camera to capture body motion and recognize gestures using dynamic time warping combined with k-nearest neighbors methods. The learning process is reinforced by the interaction with the proposed system that accelerates learning itself thus helping both children and educators. To demonstrate the validity of the system, a set of qualitative experiments with children were carried out. As a result, a system which is able to recognize a subset of 11 gestures of Schaeffer's sign language online was achieved.This work has been supported by the Spanish Government DPI2013-40534-R Grant, supported with Feder funds

Height Compensation Using Ground Inclination Estimation in Inertial Sensor-Based Pedestrian Navigation

In an inertial sensor-based pedestrian navigation system, the position is estimated by double integrating external acceleration. A new algorithm is proposed to reduce z axis position (height) error. When a foot is on the ground, a foot angle is estimated using accelerometer output. Using a foot angle, the inclination angle of a road is estimated. Using this road inclination angle, height difference of one walking step is estimated and this estimation is used to reduce height error. Through walking experiments on roads with different inclination angles, the usefulness of the proposed algorithm is verified