Wearable Sensing

In an aging population, the demand for nurse workers increases to care for elders. Helping nurse workers make their work more efficient, will help increase elders quality of life, as the nurses can focus their efforts on care activities instead of other activities such as documentation.
Activity Recognition can be used for this goal. If we can recognize what activity a nurse is engaged in, we can partially automate documentation process to reduce time spent on this task, monitor care plan compliance to assure that all care activities have been done for each elder, among others.

Last Updated On: 
Fri, 12/06/2019 - 03:40

The files contain the research data. The noisy unprocessed audio data and processed audio data using the proposed speech enhancement method are uploaded. 

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 A human-user study with ten healthy subjects provides this experimental setup. The experimental protocol consists in capturing kinematic data while subjects walk, with the donned H2 lower-limb exoskeleton, across seven experimental conditions: Subjects completed 7 trials each. There were 7 different trials, 3 with marks every 30, 45 and 60 cm on 3 separate 10 meter lanes.

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In robotic grasping and manipulation, force feedback is one of the most important factors. In the absence of force feedback, force control and compliant grasping is almost impossible. In this study a novel Vibrational Haptic feedback system is designed. The system gives individual digit awareness of a multipronged robotic gripper to the user. It also gives force level feedback from each fingertip and simultaneous multiple force level feedback, all through one wearable elastic “Vibrational Haptic Band (Vi-HaB)”.

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AoT for Smart Society provides solutions of industry 4.0 standards in which contains custom-built multisensory wearable suit with cloud connectivity interfaced Artificial Intelligent techniques and Machine Learning algorithms in order to detect, to monitor and to analyze biofeedback control and visualization during human daily activities.

 

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The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.

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Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.

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VideoSupplement "SEM-assisted (LVEM-assisted) isopotential mapping of dielectric charging of the nonwoven fabric structures using Sobel–Feldman operator (Sobel filter)" for our article in russian journal (translated in English). 

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Microfluidic Lab-on-a-dish (3D printing).

 

O.V. Gradov group, INEPCP RAS, 2017-2018.

https://vimeo.com/273508513

 

Acknowledgements:

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Wearable Inertial Measurement Units (IMU) measuring acceleration, earth magnetic field and gyroscopic measurements can be considered for capturing human skeletal postures in real time. This dataset contains IMU readings (accelerometer, magnetometer and gyroscope) for common shoulder exercises: extension- flexion and abduction-adduction and simultaneously measures VICON readings and Kinect readings.

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2989 Views

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