Development of a Learning-Based Intention Detection Framework for Power-Assisted Manual Wheelchair Users

Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation

“…The results in general reveal that the performance for all of the three (3) control strategies/methods in experiment 1 was satisfactory, even though this experiment was not benefited through the implementation of the novel adaptive control algorithm. We assume that the favorable results in experiment 1 were the benefits/advantages of the inclusion of a human user's weight perception (or cognition) in the dynamics and controls by using the optimum/best mass values (values of m1, m2, m) in the control systems obtained through the machine learning model [26,27]. The results in general reveal that, for experiment 1, the position control scheme/method produced the best/optimal performance in all evaluation criteria, 9 shows that (i) before the implementation of the novel control algorithm, the PLFs for the position control scheme were smaller than those for force control scheme/method 2, and the PLFs for both control schemes/methods were larger than the minimum load force requirements (about 5 N, which was equal to the object's simulated weight); (ii) the novel control algorithm reduced the PLFs, but the reduction was more intensive for the position control than for the force control, which means that for the position control, the PLF reduced to almost the minimum, but the reduction was not so much for the force control; (iii) the load force was proportional to the visual object sizes [20]; and so forth.…”

“…The results in general reveal that the performance for all of the three (3) control strategies/methods in experiment 1 was satisfactory, even though this experiment was not benefited through the implementation of the novel adaptive control algorithm. We assume that the favorable results in experiment 1 were the benefits/advantages of the inclusion of a human user's weight perception (or cognition) in the dynamics and controls by using the optimum/best mass values (values of m 1 , m 2 , m) in the control systems obtained through the machine learning model [26,27]. The results in general reveal that, for experiment 1, the position control scheme/method produced the best/optimal performance in all evaluation criteria, then force control scheme/method 2 could be ranked for its performance, and then force control scheme/method 1 could be ranked in terms of its ability to produce a satisfactory system performance.…”

“…The optimal control parameters necessary to produce optimal system-user interactions and system performance were not decided experimentally [23]. We posit that the optimal control parameters could be decided experimentally following a reinforcement learning model [24][25][26][27]. However, such initiatives were not taken.…”