Yuhao Feng, Yanghui Rao, Yuyao Tang, Ninghua Wang, He Liu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Ankle joint with contracture and/or spasticity can severely disable the mobility and the independence of stroke survivors. Robot-assisted rehabilitation has been proposed to support physicians in providing effective therapies. In this paper, we propose a robot-assisted ankle rehabilitation system integrated with human-computer interaction interface and acquisition of Electromyography signals, joint torque and joint angle. Furthermore, we investigate the effects of proprioceptive neuromuscular facilitation (PNF) rehabilitation method applied in our robotic system. The proposed robot-assisted system has been used in real experiments and provides PNF rehabilitation to five stroke patients for six weeks. Preliminary experimental results suggest that PNF is effective in increasing ankle range of motion(ROM), decreasing ankle resistance torque, and alleviating joint stiffness.

Although survival analysis is known to outperform logistic regression, theoretically and according to evidence from other disciplines, little is known about how true this is in situations where the goal is detecting spatial predictors of land change. Furthermore, with the increasing availability of longitudinal land-change data, evidence is needed on the relative performance of these two different methods in situations with differing levels of data abundance. To fill this gap, we generated a pseudo land-change data set using an agent-based model of residential development in a virtual landscape. This agent-based model simulated the decisions of homebuyers in choosing residential locations based on the values of several spatial variables. Pseudo land-change maps, generated by the agent-based model with different weights on these spatial variables, were exposed to statistical analysis under the logistic and survival approaches. We evaluated how well the two approaches could reveal the spatial variables that were used in the agent-based model and compared the performance of the two methods when land-change data were collected under different sampling frequencies. Our results suggest that survival analysis outperforms logistic regression in detecting the variables that were included in agent decisions, largely because it takes into account time-dependent variables. Also, this research suggests that various properties of land-change processes (like amount of developed area and access of agents to information) affect the relative performance of these statistical approaches aimed at uncovering land-change predictor variables.