A Novel End-Effector Robot System Enabling to Monitor Upper-Extremity Posture During Robot-Aided Planar Reaching Movements

Hwang, Yeji; Lee, Seongpung; Hong, Jin; Kim, Jonghyun

doi:10.1109/lra.2020.2974453

Cited by 6 publications

(2 citation statements)

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“…Similar to our previous study [ 13 ], the system consisted of a sensor, user interface, and scoring algorithm. A depth sensor, Kinect V2 (Microsoft, Redmond, WA, USA) was used to extract motion features ( Figure 2 ), which is an inexpensive and easy to use sensor that has been widely applied in the rehabilitation area [ 13 , 15 , 16 , 17 , 32 ], and whose effectiveness was verified in our previous FMA studies [ 13 , 17 ]. The system has the same user interface as our previous automated FMA system [ 13 ], which provided an instruction video that was prerecorded by a well-experienced clinician.…”

Section: Methodsmentioning

confidence: 99%

Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment

Lee

Hwang

Lee

et al. 2021

Sensors

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In rehabilitation, the Fugl–Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.

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Section: Methodsmentioning

confidence: 99%

Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment

Lee

Hwang

Lee

et al. 2021

Sensors

Self Cite

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show abstract

“…Moreover, the benefit of the proposed models provides the capability to monitor the patient's arm posture during ADL, which is a main feature for clinical assessments of the upper limb [30]. There are some attempts to use external sensors to measure the upper limb posture when using the 3D end-effector type of rehabilitation robot, such as magnetic sensors and IMUs [19], or external RGB-D cameras [31]. However, our proposed methods do not need to rely on external sensors, which reduce the system complexity and enhance the robotic system's usability.…”

Section: A Evaluation Of Estimated Human Joint Anglesmentioning

confidence: 99%