There currently exist no practical tools to identify functional movements in the upper extremities (UEs). This absence has limited the precise therapeutic dosing of patients recovering from stroke. In this proof-of-principle study, we aimed to develop an accurate approach for classifying UE functional movement primitives, which comprise functional movements. Data were generated from inertial measurement units (IMUs) placed on upper body segments of older healthy individuals and chronic stroke patients. Subjects performed activities commonly trained during rehabilitation after stroke. Data processing involved the use of a sliding window to obtain statistical descriptors, and resulting features were processed by a Hidden Markov Model (HMM). The likelihoods of the states, resulting from the HMM, were segmented by a second sliding window and their averages were calculated. The final predictions were mapped to human functional movement primitives using a Logistic Regression algorithm. Algorithm performance was assessed with a leave-one-out analysis, which determined its sensitivity, specificity, and positive and negative predictive values for all classified primitives. In healthy control and stroke participants, our approach identified functional movement primitives embedded in training activities with, on average, 80% precision. This approach may support functional movement dosing in stroke rehabilitation.

A fuzzy configuration space description method that provides the path planning solution for autonomous mobile robots in dynamically changing environment is proposed based on a hybrid planning algorithm that combines total solutions and reactive control through fuzzy proximity measures. The system (made with C++) that monitors and controls mobile robots remotely is created using a multithreaded model while taking advantage of high performance OpenGL routines to counter the increase in computational cost generated by this approach. Experiments on a real Lego robot are performed using a personal computer with a 1.5GHz Pentium4 CPU and a CCD camera. The efficiency of the hybrid algorithm and the potential of this approach, as a distributed system, in greatly changing dynamic environments are shown. The system provides a starting point for further development of distributed robotic systems, for application in human support tasks where interaction with nonprecise human behaviors are better mentioned with fuzzy parameters.