2-Point Error Estimation Algorithm for 3-D Thigh and Shank Angles Estimation Using IMU

Han, Yi; Wong, Kiing Ing; Murray, Iain

doi:10.1109/jsen.2018.2865764

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…Besides, synchronized force sensitive resistors (FSRs) used in [30] can be added for gait phase detection. With 3-D angle estimation algorithm such as [14], [15], and [28], the 3-D joint angle information could also be provided to the clinicians for visual inspection. Other than providing the joint angle information, we could add gait phase information to the video [30].…”

Section: Resultsmentioning

confidence: 99%

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Automatic Synchronization of Markerless Video and Wearable Sensors for Walking Assessment

2019

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As walking assessment is commonly done through visual inspection, it is beneficial to make joint angle information available to the clinicians for better assessment. The main challenge is that the video camera and inertial sensor are usually two separate systems, and the recordings are hard to be initialized at the same time manually. This creates a problem that the inertial sensor data is not temporally synchronized with the video camera. This paper proposes a method to synchronize the video and sensor data by detecting and matching the maximum backward swings of the leg. The proposed method is validated by blinking LED and transmitting LED flag to the computer at the same time. The synchronization error of the proposed method is low at about 0±2 frames compared to the validation method.

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Section: Resultsmentioning

confidence: 99%

“…5 shows the shank and foot angle estimated using IMU data. The angle estimation algorithm used in this research is based on our previous research [28]. This angle estimation algorithm has been validated against gold standard Vicon optical motion capture system.…”

Section: Transceiver Nrf24l01+mentioning

confidence: 99%

Automatic Synchronization of Markerless Video and Wearable Sensors for Walking Assessment

2019

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“…Lower-limb prostheses are designed to minimize the impact of amputation and make the patient more autonomous [36]. Han et al introduced a technique known as the 2-point error estimation algorithm to estimate the pitch, roll, and yaw angles using the accelerometer and gyroscope alone [37]. Gait phase recognition was presented in [?]…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Human Gait Cycle With Body Equilibrium Based on Leg Orientation

et al. 2022

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Gait analysis identifies the posture during movement in order to provide the correct actions for a normal gait. A person's gait may differ from others and can be recognized by specific patterns. Healthy individuals exhibit normal gait patterns, while lower limb amputees exhibit abnormal gait patterns. To better understand the pitfalls of gait, it is imperative to develop systems capable of capturing the gait patterns of healthy individuals. The main objective of this research was to introduce a new concept in gait analysis by computing the static and dynamic equilibrium in a real-world environment. A relationship was also presented among the parameters stated as static & dynamic equilibrium, speed, and body states. A sensing unit was installed on the designed metal-based leg mounting assembly on the lateral side of the leg. An algorithm was proposed based on two variables: the position of the leg in space and the angle of the knee joint measured by an IMU sensor and a rotary encoder. It was acceptable to satisfy the static conditions when the body was in a fixed position and orientation, whether lying down or standing. While walking and running, the orientation is determined by the position and knee angle variables, which fulfill the dynamic condition. High speed reveals a rapid change in orientation, while slow speed reveals a slow change in orientation. The proposed encoder-based feedback system successfully determined the flexion at 47 • , extension at 153 • , and all seven gait cycle phases were recognized within this range of motion. Body equilibrium facilitates individuals when they are at risk of falling or slipping.INDEX TERMS Gait analysis, IMU sensor, rotary encoder, static and dynamic equilibrium, body orientation.

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“…Fig. 4 shows the 3D thigh and shank angles estimated using the method in our previous research [20], which had been validated against gold standard Vicon optical motion capture system. The same sensors, microcontrollers, and sampling period (100 Hz) were used in [20] and in this research.…”

Section: Feature Extractionmentioning

confidence: 99%

Comparison of machine learning methods for the construction of a standalone gait diagnosis device

Han¹,

Wong²,

Murray

2020

IET signal process.

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In this research, the authors investigate the feasibility of selecting three-dimensional thigh and shank angles as the features of machine learning methods. Four common machine learning techniques, i.e. random forest, k-nearest neighbour, support vector machine and perceptron, were compared in terms of accuracy and memory usage so that a real-time standalone gait diagnosis device can be constructed using low-end inertial measurement units (IMUs). With proper re-sampling and normalisation, they discovered that the support vector machine and perceptron resulted in the top two highest accuracies (96-99%) among the four machine learning methods. The memory requirement of the perceptron is the lowest among the machine learning methods. Therefore, perceptron was selected as the classification algorithm for the standalone gait diagnosis device. The trained perceptron was transferred to the thigh and shank's IMUs to process the data locally in real-time. The constructed standalone gait diagnosis device lit up green or red light emitting diodes when normal or abnormal gaits were detected, respectively. This standalone device was further tested in real-life and achieved a mean classification accuracy of 96.50%. AccGy stands for acceleration and gyroscope. The extracted features are similar to that of Mikos et al. [4]. DFT stands for discrete Fourier transform. The features are the first 5 harmonic components (5 instantaneous amplitudes and 5 phases) of the angle waveform. These results were obtained using all features extracted from 3D thigh and shank sensor data. Values in bold font means ≥ 96%.

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2-Point Error Estimation Algorithm for 3-D Thigh and Shank Angles Estimation Using IMU

Cited by 14 publications

References 18 publications

Automatic Synchronization of Markerless Video and Wearable Sensors for Walking Assessment

Automatic Synchronization of Markerless Video and Wearable Sensors for Walking Assessment

Analysis of Human Gait Cycle With Body Equilibrium Based on Leg Orientation

Comparison of machine learning methods for the construction of a standalone gait diagnosis device

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