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The Nurosleeve, A User-Centered 3d Printed Hybrid Orthosis For Individuals With Upper Extremity Impairment, Mehdi Khantan, Mikael Avery, Phyo Thuta Aung, Rachel M. Zarin, Emma Hammelef, Nabila Shawki, Mijail Demian Serruya, Alessandro Naopli

Farber Institute for Neuroscience Faculty Papers

BACKGROUND: Active upper extremity (UE) assistive devices have the potential to restore independent functional movement in individuals with UE impairment due to neuromuscular diseases or injury-induced chronic weakness. Academically fabricated UE assistive devices are not usually optimized for activities of daily living (ADLs), whereas commercially available alternatives tend to lack flexibility in control and activation methods. Both options are typically difficult to don and doff and may be uncomfortable for extensive daily use due to their lack of personalization. To overcome these limitations, we have designed, developed, and clinically evaluated the NuroSleeve, an innovative user-centered UE hybrid orthosis.

METHODS: This …

Optimizing Wearable Assistive Devices With Neuromuscular Models And Optimal Control, Manish Sreenivasa, Matthew Millard, Paul Manns, Katja Mombaur

Faculty of Engineering and Information Sciences - Papers: Part B

The coupling of human movement dynamics with the function and design of wearable assistive devices is vital to better understand the interaction between the two. Advanced neuromuscular models and optimal control formulations provide the possibility to study and improve this interaction. In addition, optimal control can also be used to generate predictive simulations that generate novel movements for the human model under varying optimization criterion.

Pedestrian Lane Detection For Assistive Navigation Of Blind People, M Le, Son Lam Phung, Abdesselam Bouzerdoum

Faculty of Engineering and Information Sciences - Papers: Part A

Navigating safely in outdoor environments is a challenging activity for vision-impaired people. This paper is a step towards developing an assistive navigation system for the blind. We propose a robust method for detecting the pedestrian marked lanes at traffic junctions. The proposed method includes two stages: regions of interest (ROI) extraction and lane marker verification. The ROI extraction is performed by using colour and intensity information. A probabilistic framework employing multiple geometric cues is then used to verify the extracted ROI. The experimental results have shown that the proposed method is robust under challenging illumination conditions and obtains superior performance …