Sequential Decision Fusion for Environmental Classification in Assistive Walking

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 9; pp. 1780 - 1790
• Main Authors: Zhang, Kuangen, Zhang, Wen, Xiao, Wentao, Liu, Haiyuan, De Silva, Clarence W., Fu, Chenglong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Algorithms; Amputation; Amputees; assistive walking; Cameras; Classification; Decision fusion; Delay effects; Electromyography; Environment; environmental classification; Female; Healthy Volunteers; hidden Markov model; Hidden Markov models; Human motion; Humans; Indoor environments; Legged locomotion; Long short-term memory; Male; Markov Chains; Models, Theoretical; Neural networks; Neural Networks, Computer; Prostheses; Prosthesis Design; Prosthetics; Recurrent neural networks; Reproducibility of Results; Self-Help Devices; sequential model; Three-dimensional displays; Time lag; Transition probabilities; Vision sensors; Voting; Walking; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2935765

Powered prostheses are effective for helping amputees walk in a single environment, but these devices are inconvenient to use in complex environments. In order to help amputees walk in complex environments, prostheses need to understand the motion intent of amputees. Recently, researchers have found that vision sensors can be utilized to classify environments and predict the motion intent of amputees. Although previous studies have been able to classify environments accurately in offline analysis, the corresponding time delay has not been considered. To increase the accuracy and decrease the time delay of environmental classification, the present paper proposes a new decision fusion method. In this method, the sequential decisions of environmental classification are fused by constructing a hidden Markov model and designing a transition probability matrix. The developed method is evaluated by inviting five able-bodied subjects and three amputees to perform indoor and outdoor walking experiments. The results indicate that the proposed method can classify environments with accuracy improvements of 1.01% (indoor) and 2.48% (outdoor) over the previous voting method when a delay of only one frame is incorporated. The present method also achieves higher classification accuracy than with the methods of recurrent neural network (RNN), long-short term memory (LSTM), and gated recurrent unit (GRU). When achieving the same classification accuracy, the method of the present paper can decrease the time delay by 67 ms (indoor) and 733 ms (outdoor) in comparison to the previous voting method. Besides classifying environments, the proposed decision fusion method may be able to optimize the sequential predictions of the human motion intent.