Detecting Reach to Grasp Activities using Motion and Muscle Activation Data

Hauser, Nathaniel

doi:10.1109/embc.2018.8512937

Cited by 4 publications

(1 citation statement)

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“…For the FSR, the mean, the RMS, the standard deviation, and the maximum value were taken. Finally, features usually utilized in similar methodology and procedures were extracted from the IMU, due to their statistical representation of large datasets [36]. These features included: the mean, the RMS, and the standard deviation for each axis of the accelerometer and gyroscope, as well as the signal magnitude area (SMA) of the accelerometer and gyroscope.…”

Section: Flex Bending Forcementioning

confidence: 99%

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Sarwat

Maged

et al. 2021

Sensors

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The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

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Section: Flex Bending Forcementioning

confidence: 99%