A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

Liu, Xiangzhi; Zeng, Bin; Zhang, Bin; Liu, Tao

doi:10.1109/tase.2022.3232632

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…The wearable system contains two Fuzhi

-gait (based on InvenSense MPU-6050 chip, including a 3D accelerometer with a range of ±8 g and a 3D gyroscope with a range of ±1000

/s, with a size of 25 × 40 × 15 mm, a weight of 100 g, and a sample rate of 100 Hz, which will not become a burden for the subjects to walk and was also used in [ 23 ]). Each IMU is attached close to the ankle on the lateral side of each shank.…”

Section: Methodsmentioning

confidence: 99%

Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems

Han

Liu

Zhang

et al. 2023

Sensors

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The rehabilitation evaluation of Parkinson’s disease has always been the research focus of human assistive systems. It is a research hotspot to objectively and accurately evaluate the gait condition of Parkinson’s disease patients, thereby adjusting the actuators of the human–machine system and making rehabilitation robots better adapt to the recovery process of patients. The rehabilitation evaluation of Parkinson’s disease has always been the research focus of rehabilitation robots. It is a research hotspot to be able to objectively and accurately evaluate the recovery of Parkinson’s disease patients, thereby adjusting the driving module of the human–machine collaboration system in real time, so that rehabilitation robots can better adapt to the recovery process of Parkinson’s disease. The gait task in the Unified Parkinson’s Disease Rating Scale (UPDRS) is a widely accepted standard for assessing the gait impairments of patients with Parkinson’s disease (PD). However, the assessments conducted by neurologists are always subjective and inaccurate, and the results are determined by the neurologists’ observation and clinical experience. Thus, in this study, we proposed a novel machine learning-based method of automatically assessing the gait task in UPDRS with wearable sensors as a more convenient and objective alternative means for PD gait assessment. In the design, twelve gait features, including three spatial–temporal features and nine kinematic features, were extracted and calculated from two shank-mounted IMUs. A novel nonlinear model is developed for calculating the score of gait task from the gait features. Twenty-five PD patients and twenty-eight healthy subjects were recruited for validating the proposed method. For comparison purpose, three traditional models, which have been used in previous studies, were also tested by the same dataset. In terms of percentages of participants, 84.9%, 73.6%, 73.6%, and 66.0% of the participants were accurately assigned into the true level with the proposed nonlinear model, the support vector machine model, the naive Bayes model, and the linear regression model, respectively, which indicates that the proposed method has a good performance on calculating the score of the UPDRS gait task and conformance with the rating done by neurologists.

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“…The wearable system contains two Fuzhi

-gait (based on InvenSense MPU-6050 chip, including a 3D accelerometer with a range of ±8 g and a 3D gyroscope with a range of ±1000

Section: Methodsmentioning

confidence: 99%

Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems

Han

Liu

Zhang

et al. 2023

Sensors

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“…As shown in Fig. 1-(2), based on our previous research results on gait [37], two IMUs were placed on the shanks to estimate the position of the thigh and have been verified on healthy subjects and patients. Similarly, the gait events have been identified through the IMUs' motion data placed The simplified walking model is only a four-link model composed of two thighs and shanks, so the weight and COM of the upper body are concentrated on the hip joint.…”

Section: B Force Estimation For Single Support Phasementioning

confidence: 99%

An IMU-Based Ground Reaction Force Estimation Method and Its Application in Walking Balance Assessment

Liu,

Zhang,

Zhang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Walking is one of the most common daily movements of the human body. Therefore, quantitative evaluation of human walking has been commonly used to assist doctors in grasping the disease degree and rehabilitation process of patients in the clinic. Compared with the kinematic characteristics, the ground reaction force (GRF) during walking can directly reflect the dynamic characteristics of human walking. It can further help doctors understand the degree of muscle recovery and joint coordination of patients. This paper proposes a GRF estimation method based on the elastic elements and Newton-Euler equation hybrid driving GRF estimation method. Compared with the existing research, the innovations are as follows. i) The hardware system consists of only two inertial measurement units (IMUs) placed on shanks. The acquisition of the overall motion characteristics of human walking is realized through the simplified four-link walking model and the thigh prediction method. ii) The method was validated not only on 10 healthy subjects but also on 11 Parkinson's patients and 10 stroke patients with normalized mean absolute errors (NMAEs) of 5.95%±1.32%, 6.09%±2.00%, 5.87%±1.59%. iii) This paper proposes a dynamic balance assessment method based on the acquired motion data and the estimated GRF. It evaluates the overall balance ability and fall risk at four key time points for all subjects recruited. Because of the low-cost system, ease of use, low motion interference and environmental constraints, and high estimation accuracy, the proposed GRF estimation method and walking balance automatic assessment have broad clinical value.

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A Clinic-Oriented Ground Reaction Force Prediction Method in Gait

Liu,

He,

Han

et al. 2023

Lecture Notes in Computer Science

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A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

Cited by 3 publications

References 33 publications

Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems

Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems

An IMU-Based Ground Reaction Force Estimation Method and Its Application in Walking Balance Assessment

A Clinic-Oriented Ground Reaction Force Prediction Method in Gait

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