Real-Time Human Lower Limbs Motion Estimation and Feedback for Potential Applications in Robotic Gait Aid and Training

Wang, Lei; Li, Qingguo; Yi, Jingang; Zhang, Jinyuan; Liu, Tao

doi:10.1109/icra48506.2021.9561553

Cited by 3 publications

(1 citation statement)

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“…Due to its irreplaceable portability, research on wearable systems applied to the HRI field has become prevalent. Wang monitored the stride length, gait cycle, and other gait parameters of healthy people and patients in real-time using only two IMUs attached to two shanks and used the method of support vector machine (SVM) to realize the classification between patients and healthy subjects [7,8], which was also used to control the velocity of AR to suit patient gait. Falla et al used a needle ECG measurement system to analyze the characteristics of the sternocleidomastoid muscle unit of nine women with chronic neck pain and nine healthy subjects and found that chronic neck pain affects the change in neural drive to muscles with force direction [9].…”

Section: Introductionmentioning

confidence: 99%

Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field

Liu,

Liu

2023

Sensors

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Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system’s volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.

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Section: Introductionmentioning

confidence: 99%