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Graduate student researcher Nili Krausz successfully defended her doctoral thesis, “Environmental Sensing for Improved Forward Prediction for Powered Lower Limb Prostheses,” on October 23.
State-of-the-art powered prosthetic legs use signals from muscle activity (electromyography, or EMG for short), as well as data from mechanical sensors embedded in the prosthesis, in order to predict when individuals wish to transition between movements — for example, walking on level ground to climbing stairs. However, this approach is highly dependent on specific gait patterns that may vary between individuals. In her research at the Center for Bionic Medicine, Nili investigated how incorporating depth sensors that record data from the environment could possibly complement and enhance current control strategies, allowing prosthesis users to transition between movements more seamlessly.
Congratulations, Dr. Krausz!
Related Publications
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Hu BH, Krausz NE, and Hargrove LJ. (2018, August). A novel method for bilateral gait segmentation using a single thigh-mounted depth sensor and IMU. In 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) (pp. 807-812). IEEE.
Krausz NE and Hargrove LJ. (2015, April). Recognition of ascending stairs from 2D images for control of powered lower limb prostheses. In 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 615-618). IEEE.
Krausz NE, Lenzi T, and Hargrove LJ. (2015). Depth sensing for improved control of lower limb prostheses. IEEE Transactions on Biomedical Engineering, 62(11), 2576-2587.