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inertial measurement unit

Validation Study of Torso-Dynamics Estimation System (TES) for Hands-Free Physical Human-Robot Interaction

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This data was collected during a validation study of our Torso-Dynamics Estimation System (TES). The TES consisted of a Force Sensing Seat (FSS) and an inertial measurement unit (IMU) that measured the kinetics and kinematics of the subject's torso motions. The FSS estimated the 3D forces, 3D moments, and 2D COPs while the IMU estimated the 3D torso angles. To validate the TES, the FSS and IMU estimates were compared to gold standard research equipment (AMTI force plate and Qualisys motion capture system, respectively). 

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CASPER: Context-Aware Anomaly Detection System for Industrial Robotic Arms

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Industrial cyber-physical systems (ICPS), which is the backbone of Industry 4.0, are the result of adapting emerging information communication technologies (ICT) to the industrial control systems (ICS). ICPS utilize autonomous robotic arms to accomplish manufacturing tasks. These arms follow a certain predetermined trajectory during the task. 

In this dataset, we present four files generated from a setup that contains two Universal Robot UR3e collaborative robotic arms:

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Shoulder Physiotherapy Activity Recognition 9-Axis Dataset

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Shoulder Physiotherapy Activity Recognition 9-Axis Dataset (SPARS9x) 
Suggested uses of this dataset include performing supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with unlabeled activities of daily living data.
Description:

This dataset contains inertial data consisting of 1) physiotherapy exercise recordings, and 2) unlabeled other activity data recordings, each collected by Huawei 2 smart watches worn by healthy subjects. Subjects peform 20 repetitions of each exercise for each shoulder.

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Activities of Daily Living

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Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living). 

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