Algorithm of model development and validation. by Bethan Cracknell Daniels (20469396)

The accuracy of all employed learning algorithms. by Quan Shao (20148209)

Overview of the development of the biomarkers in the motor, exteroceptive, interoceptive, and cerebral domains. by Marialuisa Gandolfi (8639238)

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Metrics of the complex network algorithm. by Xumei Pan (21464351)

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Landslide susceptibility maps of the study area were developed using the RF algorithm for (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. by Raja Das (1841107)

Classification accuracy of feature set 2 and feature set 3 in each algorithm. by Quan Shao (20148209)

Pseudocode of the BBAC_I Algorithm. by Zezhi Lin (22521663)

Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

Analysis of single features of vehicle movement. by Yaqin He (4627711)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Performance comparison for different algorithms reported in terms of bootstrapped canonical correlation of each component. by Shashwat Kumar (19120858)

Excerpts from the raw acquired data set with the tracking algorithm. by Hannes Benecke (17409369)

Data Sheet 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.pdf by Tim Unger (20457145)

“…Advances in computer vision and human pose estimation allow for human movement analysis using consumer-grade cameras. This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

The algorithm to define the study population. by Michael C. Reed (6622226)

Patient selection algorithm. by Dwi Kisworo Setyowireni (22656144)

Image 5_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Advances in computer vision and human pose estimation allow for human movement analysis using consumer-grade cameras. This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

Image 4_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Advances in computer vision and human pose estimation allow for human movement analysis using consumer-grade cameras. This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”