A novel 7 degrees of freedom model for upper limb kinematic reconstruction based on wearable sensors

Peppoloni, Lorenzo; Filippeschi, Alessandro; Ruffaldi, Emanuele; Avizzano, Carlo Alberto

doi:10.1109/sisy.2013.6662551

Cited by 42 publications

(56 citation statements)

References 9 publications

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“…In most of the cases these parameters are assumed to be known: the IMU frame is supposed to be physically aligned to the body frame (e.g., [50,60,87]). In other cases these parameters are obtained by means of a calibration procedure that is carried out at the beginning of the capturing session (e.g., [47]). Another subset is related to the dimensions of the human body: human limb lengths are typically either measured (e.g., [87]), taken from anthropometric models (e.g., [64]) or calculated by means of calibration procedures (e.g., [64,88]).…”

Section: Imu-based Human Motion Trackingmentioning

confidence: 99%

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Filippeschi

Schmitz

Miezal

et al. 2017

Sensors

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280

188

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Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

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Section: Imu-based Human Motion Trackingmentioning

confidence: 99%

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Filippeschi

Schmitz

Miezal

et al. 2017

Sensors

Self Cite

280

188

View full text Add to dashboard Cite

show abstract

“…Estimation errors of less than 3°and 12°were achieved, respectively, for upper-arm motion and forearm motion. Peppoloni et al [80] and ElGohary et al [81] also present similar ideas for kinematic analysis using wearable inertial sensors.…”

Section: B Processing Techniquesmentioning

confidence: 88%

Wearable Sensing for Solid Biomechanics

Wong

Zhang

et al. 2015

IEEE Sensors J.

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Abstract-Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.

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“…For further information about the algorithms the reader can refer to [Peppoloni et al 2013;Ruffaldi et al 2014]. Surface EMG array sensors monitor the activity of the operator's forearm flexors (flexor carpi radialis, the palmaris longus and the flexor carpi ulnaris).…”

Section: Motion Reconstruction and Muscle Effortmentioning

confidence: 99%

Augmented reality-aided tele-presence system for robot manipulation in industrial manufacturing

Peppoloni

Brizzi

Ruffaldi

et al. 2015

Proceedings of the 21st ACM Symposium on Virtual Reality Software and Technology

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This work investigates the use of a highly immersive telepresence system for industrial robotics. A Robot Operating System integrated framework is presented where a remote robot is controlled through operator's movements and muscle contractions captured with a wearable device. An augmented 3D visual feedback is sent to the user providing the remote environment scenario from the robot's point of view and additional information pertaining to the task execution. The system proposed, using robot mounted RGB-D camera, identifies known objects and relates their pose to robot arm pose and to targets relevant to the task execution. The system is preliminary validated during a pick-and-place task using a Baxter robot. The experiment shows the practicability and the effectiveness of the proposed approach.

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A novel 7 degrees of freedom model for upper limb kinematic reconstruction based on wearable sensors

Cited by 42 publications

References 9 publications

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Wearable Sensing for Solid Biomechanics

Augmented reality-aided tele-presence system for robot manipulation in industrial manufacturing

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