# An Intelligent Error Correction Algorithm for Elderly Care Robots
## 2 Related Work
### Intelligent Error Correction  
With the rapid development of the neural network, the recognition accuracy was still unsatisfactory in practical application, so an intelligent error correction algorithm was also applied to the gesture recognition algorithm based on deep learning. In 2006, the Dynamic Bayesian classifier was proposed to correct similar gesture recognition errors by combining motion-based and pose-based features [19]. Ref [20] proposed a real-time gesture recognition algorithm. This system includes 36 kinds of gestures, and mainly improves the accuracy of similar gestures by establishing a combination of features, including position, direction, and speed. In reference [21], HMM (Hidden Markov Model) was used to improve the recognition rate of similar gestures in the database by capturing the jumping trajectory information and quantifying the motion features in 3D space. In reference [22], the mechanism of gesture error recognition based on convolution neural network was explored, and an intelligent error correction algorithm based on a probability statistical model and convolution features was proposed. Through the intelligent detection and error correction of gesture recognition, intelligent interactive teaching was realized. With the continuous development of human-computer interaction, robots no longer only accepted human ideas, but began to have their own \"ideas\". At first, the robot could correct its steady-state motion error to achieve the purpose of self-correction. Reference [23] obtained the motion error information from the internal encoder of the robot, fed the obtained information back to the iterative learning controller (ILC) to calculate the compensation variable, added the compensation variable to the original position reference command of the robot, and finally corrected the steady-state motion error of the robot. Furthermore, the robot could judge the error by learning some physiological characteristics of humans when facing the error. In reference [24], the evaluative feedback obtained from human brain signals measured by scalp EEG was used to accelerate the repetitive learning of robot agents in a sparse reward environment. The robot decoded it into a noise error feedback signal and used the feedback signal to sense the impending error and successfully avoid it. Finally, the obstacle-avoidance function was tested. Later, robots were able to correct some human irregularities. In reference [25], the robot was used to capture and display humans' wrong operation, and then the standardized operation was demonstrated. The goal of correcting humans' irregular operation was achieved, and the efficiency of human-computer cooperation was improved. Literature [26; 27] proposed a gesture-correction method using implicit feedback in reinforcement learning (RL). This method used the event-related activity of human electroencephalogram (EEG)-error-relatedpotential (ErrP) as the implicit feedback generated by RL. The NAO (a humanoid robot developed by SoftBankRobotics) robot would judge whether its recognition result was correct according to the feedback of the human EEG, and finally executed the corresponding instructions according to the feedback. To conclude, first of all, most of the gesture recognition algorithms are applied to young people who can make standard gestures, which is not suitable for the object of this paper: the elderly. Therefore, the recognition results applied to the elderly are not satisfactory. Secondly, most of the error correction systems only make error reminders by judging whether there are errors with the set template. In this paper, based on the elderly-care robot, an intelligent error correction algorithm is studied. The mechanism of error correction is explored from the behavior level of the elderly and the robot cognition level, and the error correction mechanism is established.