Orientation Tracking for Humans and Robots Using Inertial Sensors

Proc. of 1999 International Symposium on Computational Intelligence in Robotics and Automation, Monterey, CA, December, 1999, pp. 187-194.Accepted/Published Conference Pape

Adaptive Kalman Filter for MEMS-IMU based Attitude Estimation under External Acceleration and Parsimonious use of Gyroscopes

International audienceThis paper presents a viable quaternion-based Adaptive Kalman Filter (q-AKF) that is designed for rigid body attitude estimation. This approach is an alternative to overcome the limitations of the classical Kalman filter. The q-AKF processes data from a small inertial/magnetic sensor module containing triaxial gyroscopes, accelerometers, and magnetometers. The proposed approach addresses two challenges. The first one concerns attitude estimation during various dynamic conditions, in which external acceleration occurs. Although external acceleration is one of the main source of loss of performance in attitude estimation methods, this problem has not been sufficiently addressed in the literature. An adaptive algorithm compensating external acceleration from the residual in the accelerometer is proposed. At each step, the covariance matrix associated with the external acceleration is estimated to adaptively tune the filter gain. The second challenge is focused on the energy consumption issue of gyroscopes for long-term battery life of Inertial Measurement Units. We study the way to reduce the gyro measurement acquisition while maintaining acceptable attitude estimation. Through numerical simulations, under external acceleration and parsimonious gyroscope's use, the efficiency of the proposed q-AKF is illustrated

Real-time recognition of human gestures for collaborative robots on assembly-line

International audienceWe present a framework and preliminary experimental results for real-time recognition of human operator actions. The goal is, for a collaborative industrial robot operating on same assembly-line as workers, to allow adaptation of its behavior and speed for smooth human-robot cooperation. To this end, it is necessary for the robot to monitor and understand behavior of humans around it. The real-time motion capture is performed using a "MoCap suit" of 12 inertial sensors estimating joint angles of upper-half of human body (neck, wrists, elbows, shoulders, etc...). In our experiment, we consider one particular assembly operation on car doors, which we have further subdivided into 4 successive steps: removing the adhesive protection from the waterproofing sheet, positioning the waterproofing sheet on the door, pre-sticking the sheet on the door, and finally installing the window "sealing strip". The gesture recognition is achieved continuously in real-time, using a technique combining an automatic time-rescaling similar to Dynamic Time Warp (DTW), and Hidden Markov Model (HMM) for estimating respective probabilities of the 4 learnt actions. Preliminary evaluation, conducted in real-world on an experimental assembly cell of car manufacturer PSA, shows a very promising action correct recognition rate of 96% on several repetitions of the same assembly operation by a single operator. Ongoing work aims at evaluating our framework for same actions recognition but on more executions by a larger pool of different human operators, and also to estimate false recognition rates on unrelated gestures. Another interesting potential perspective is the use of workers' motion capture in order to estimate effort and stress, for helping prevention of physical causes of some musculoskeletal disorders

Inertial and Magnetic Posture Tracking for Inserting Humans Into Networked Virtual Environments

Proceedings of ACM Symposium on Virtual Reality Software & Technology (VRST 2001), Banff, Alberta, Canada, 15 - 17 November 2001, pp.9-16.Accepted/Published Conference Pape

Energy-aware Adaptive Attitude Estimation Under External Acceleration for Pedestrian Navigation

International audienceIn this paper, we consider the problem of rigid bodyattitudeestimationunderexternalaccelerationusingasmallinertial/magneticsensors module containing a triad of gyroscope, accelerometer,andmagnetometer.Thepaperisfocusedontwomainchallenges. The first challenge concerns the attitude estimationduring dynamic case, in which external acceleration occurs. Thislatter leads to lose performance in attitude estimation methods. Aquaternion-based adaptive Kalman filter (q-AKF) compensatingexternal acceleration from the residual in the accelerometer isdesigned. At each step, the covariance matrix of the externalacceleration is estimated to tune the filter gain adaptively. Thesecond challenge is related to the energy consumption issue ofgyroscope. In order to ensure a longer battery life for the InertialMeasurement Units (IMUs), we study the way to reduce the gyromeasurements acquisition by switching on/off the sensor whilemaintaining an acceptable attitude estimation. A smart detectionapproach isproposed to decide whether the body is indynamic orstatic case. The efficiency of the q-AKF is demonstrated throughnumerical simulations and experimental tests

Drift compensated inertial position sensor for healthcare patient monitoring

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 71-72).In order to provide more effective health care, especially to the elderly, we must enable the physician to monitor the patient outside of the clinic or hospital. A patient's activities are a critical indicator of his or her well-being, and the physician must have an un-intrusive and inexpensive means of monitoring patient activity. The objective of this project was to design and construct a low-cost, low-power, six degree-of-freedom inertial activity monitor that can be used with a portable computer. In this thesis, I describe the design and implementation of a such a monitor that can communicate using several popular peripheral bus protocols. I describe a simple attitude estimation filter and give a qualitative assessment of its performance.by David Lee Nelson.M.Eng