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While machine perception has shown promise recently for detecting impending terrains in the trip path, current methods are unable to recognize human intent, which is necessary for coordinated wearable robot operation, and are instead restricted to environment perception. Therefore, the goal of this research is to create a new system that accurately forecasts the user’s mode of movement by combining machine perception (which captures ambient data) with human vision (which represents user intent). The system can detect the user’s intended path in a complicated setting with various terrains since it has multimodal visual information. Moreover, a fusion algorithm based on dynamic time-warping techniques To produce flexible judgments on the time of locomotion mode change for wearable robot control, a fusion technique was devised to align the temporal forecasting from individual modalities. Through the use of experimental data gathered from five people, the system’s performance was verified. It demonstrated a high degree of intent detection accuracy (almost 96% on average) and dependable decision-making on locomotion transition with customizable lead time. These encouraging results show that combining machine perception and human vision may be used to identify lower limb wearable robots’ intent to move.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2024-04-06", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/tnsre.2022.3168796", "unstructured": "Minhan Li, Graduate Student Member, IEEE, Boxuan Zhong, Edgar Lobaton, Member, IEEE, and He Huang, Senior Member, IEEE, “Fusion of Human Vision and Machine Perception for Forecasting Intended Locomotion Mode”, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 30, 2022" }, { "key": "ref2", "doi": "10.14257/ijsip.2014.7.1.11", "unstructured": "V.Srinivas,Dr.Ch.Santhi Rani and Dr. T. 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While machine perception has shown promise recently for detecting impending terrains in the trip path, current methods are unable to recognize human intent, which is necessary for coordinated wearable robot operation, and are instead restricted to environment perception. Therefore, the goal of this research is to create a new system that accurately forecasts the user’s mode of movement by combining machine perception (which captures ambient data) with human vision (which represents user intent). The system can detect the user’s intended path in a complicated setting with various terrains since it has multimodal visual information. Moreover, a fusion algorithm based on dynamic time-warping techniques To produce flexible judgments on the time of locomotion mode change for wearable robot control, a fusion technique was devised to align the temporal forecasting from individual modalities. Through the use of experimental data gathered from five people, the system’s performance was verified. It demonstrated a high degree of intent detection accuracy (almost 96% on average) and dependable decision-making on locomotion transition with customizable lead time. These encouraging results show that combining machine perception and human vision may be used to identify lower limb wearable robots’ intent to move.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2024-04-06", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/tnsre.2022.3168796", "unstructured": "Minhan Li, Graduate Student Member, IEEE, Boxuan Zhong, Edgar Lobaton, Member, IEEE, and He Huang, Senior Member, IEEE, “Fusion of Human Vision and Machine Perception for Forecasting Intended Locomotion Mode”, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 30, 2022" }, { "key": "ref2", "doi": "10.14257/ijsip.2014.7.1.11", "unstructured": "V.Srinivas,Dr.Ch.Santhi Rani and Dr. T. Madhu, “Neural Network based Classification for Speaker Identification” International Journal of Signal Processing.Image Processing and Pattern Recognition., vol 7, No.1.pp.109-120.2014" }, { "key": "ref3", "doi": "10.1109/tase.2020.2993399", "unstructured": "B. Zhong, R.L.D.Silva, M., H. Huang, and E.Lobaton, “Environmental context forecasting for lower limb prostheses with uncertainty quantification,” IEEE Trans.Autom.Sci.Eng., vol.18, no.2,pp.458–470,Apr.2021" }, { "key": "ref4", "doi": "10.1109/lra.2021.3062003", "unstructured": "K.Zhangetal, “Foot placement forecasting for assistive walking by fusing sequential 3D gaze and environmental context,” IEEE Robot.Autom. Lett., vol.6, no.2,pp. 2509–2516, Apr. 2021" }, { "key": "ref5", "doi": "10.1109/wacv48630.2021.00111", "unstructured": "K.Min and J.J.Corso, “Integrating human vision into attention for egocentric activity recognition,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. 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