Human assisted tools for gait analysis and intelligent gait phase detection

Purpose The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent. Design/methodology/approach Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis. Findings The effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains. Practical implications This study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains. Originality/value The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

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Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis—Proof of Concept

Sensors ◽

10.3390/s21175749 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5749

Author(s):

Mustafa Sarshar ◽

Sasanka Polturi ◽

Lutz Schega

Keyword(s):

Gait Analysis ◽

Short Term Memory ◽

Inertial Sensors ◽

Validation Dataset ◽

Phase Detection ◽

Short Term ◽

Velocity Magnitude ◽

Gait Phase ◽

Long Short Term Memory ◽

Do So

Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. The aim of this work was to employ Artificial Intelligence (AI), specifically a long short-term memory (LSTM) algorithm, to overcome these weaknesses. Three supervised LSTM-based models were designed to estimate the expected gait phases, including foot-off (FO), mid-swing (MidS) and foot-contact (FC). For collecting gait data two tri-axial inertial sensors were located above each ankle. The angular velocity magnitude, rotation matrix magnitude and free acceleration magnitude were captured for data labeling and turning detection and to strengthen the model, respectively. To do so, a train dataset based on a novel movement protocol was acquired. A validation dataset similar to a train dataset was generated as well. Five test datasets from already existing data were also created to independently evaluate the models. After testing the models on validation and test datasets, all three models demonstrated promising performance in estimating desired gait phases. The proposed approach proves the possibility of employing AI-based algorithms to predict labeled gait phases from a time series of gait data.

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Gait Phase Detection of Exoskeleton Robot Based on the Joints Angle of Lower Limb

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3425067 ◽

2020 ◽

Author(s):

Wang Jiang ◽

Jianbin Zheng ◽

Liping Huang

Keyword(s):

Lower Limb ◽

Phase Detection ◽

Exoskeleton Robot ◽

Gait Phase

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Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽

10.3390/s21030789 ◽

2021 ◽

Vol 21 (3) ◽

pp. 789

Author(s):

David Kreuzer ◽

Michael Munz

Keyword(s):

Gait Analysis ◽

Time Series Data ◽

Low Cost ◽

Analysis Data ◽

Gait Disorders ◽

Human Gait ◽

Series Data ◽

Ageing Society ◽

Time Motion ◽

Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

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Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽

10.3390/s21041081 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1081

Author(s):

Tamon Miyake ◽

Shintaro Yamamoto ◽

Satoshi Hosono ◽

Satoshi Funabashi ◽

Zhengxue Cheng ◽

...

Keyword(s):

Learning Algorithm ◽

Detection System ◽

Calibration Method ◽

Detection Algorithm ◽

Video Data ◽

Detection Accuracy ◽

Phase Detection ◽

Gait Phase ◽

Logistic Regression Algorithm ◽

Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

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