Sequential Decision Fusion for Environmental Classification in Assistive Walking
This work addresses the challenge of improving real-time environmental classification for assistive walking devices, benefiting amputees by potentially enhancing prosthesis usability in complex environments, though it appears incremental as it builds on existing vision-based methods.
The paper tackles the problem of environmental classification for powered prostheses by proposing a sequential decision fusion method using a hidden Markov model, which achieves higher accuracy and lower time delay compared to previous methods, as validated through experiments with able-bodied subjects and amputees.
Powered prostheses are effective for helping amputees walk on level ground, but these devices are inconvenient to use in complex environments. Prostheses need to understand the motion intent of amputees to help them walk in complex environments. Recently, researchers have found that they can use vision sensors to classify environments and predict the motion intent of amputees. Previous researchers can classify environments accurately in the offline analysis, but they neglect to decrease the corresponding time delay. To increase the accuracy and decrease the time delay of environmental classification, we propose a new decision fusion method in this paper. We fuse sequential decisions of environmental classification by constructing a hidden Markov model and designing a transition probability matrix. We evaluate our method by inviting able-bodied subjects and amputees to implement indoor and outdoor experiments. Experimental results indicate that our method can classify environments more accurately and with less time delay than previous methods. Besides classifying environments, the proposed decision fusion method may also optimize sequential predictions of the human motion intent in the future.