The rising interest in collaborative robotics leads to research solutions in order to increase robot interaction with the environment. The development of methods that permit robots to recognize and track human motion is relevant for safety and collaboration matters. A large quantity of data can be measured in real time by Microsoft Kinect®, a well-known low-cost depth sensor, able to recognize human presence and to provide postural information by extrapolating a skeleton. However, the Kinect sensor tracks motion with relatively low accuracy and jerky behavior. For this reason, the effective use in industrial applications in which the measurement of arm velocity is required can be unsuitable. The present work proposes a filtering method that allows the measurement of more accurate velocity values of human arm, based on row data provided by the Kinect sensor. The estimation of arm motion is achieved by a Kalman filter based on a kinematic model and by the imposition of fixed lengths for the skeleton links detected by the sensor. The development of the method is supported by experimental tests. The achieved results suggest the practical applicability of the developed algorithms.

Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics / Palmieri, P.; Melchiorre, M.; Scimmi, L. S.; Pastorelli, S.; Mauro, S.. - ELETTRONICO. - 91:(2021), pp. 326-334. ((Intervento presentato al convegno The Third International Conference of IFToMM ITALY tenutosi a ONLINE nel 9-11 September 2020 [10.1007/978-3-030-55807-9_37].

Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics

Palmieri P.;Melchiorre M.;Scimmi L. S.;Pastorelli S.;Mauro S.
2021

Abstract

The rising interest in collaborative robotics leads to research solutions in order to increase robot interaction with the environment. The development of methods that permit robots to recognize and track human motion is relevant for safety and collaboration matters. A large quantity of data can be measured in real time by Microsoft Kinect®, a well-known low-cost depth sensor, able to recognize human presence and to provide postural information by extrapolating a skeleton. However, the Kinect sensor tracks motion with relatively low accuracy and jerky behavior. For this reason, the effective use in industrial applications in which the measurement of arm velocity is required can be unsuitable. The present work proposes a filtering method that allows the measurement of more accurate velocity values of human arm, based on row data provided by the Kinect sensor. The estimation of arm motion is achieved by a Kalman filter based on a kinematic model and by the imposition of fixed lengths for the skeleton links detected by the sensor. The development of the method is supported by experimental tests. The achieved results suggest the practical applicability of the developed algorithms.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2847083