Fuzzy Logic Based Implementation of a Real-Time Gait Phase Detection Algorithm Using Kinematical Parameters for Walking

2009 ◽  
Author(s):  
C. Senanayake ◽  
S. M. N. Arosha Senanayake
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Detection Algorithm ◽  
Phase Detection ◽  
Gait Phase ◽  
Kinematical Parameters

A Standalone Real-Time Gait Phase Detection Using Fuzzy-Logic Implementation in Arduino Nano

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sachin Negi ◽  
Kartik Garg ◽  
Milind Prajapat ◽  
Neeraj Sharma
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Phase Detection ◽  
Gait Phase

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

Sensors ◽  
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.

FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Negi ◽  
Shiru Sharma ◽  
Neeraj Sharma
Keyword(s):  
Gait Analysis ◽  
Real Time ◽  
Medial Gastrocnemius ◽  
Human Gait ◽  
Phase Detection ◽  
Zero Crossing ◽  
Content Type ◽  
Open Environment ◽  
Emg Signal ◽  
Gait Phase

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.

scholarly journals Fall inducing movable platform (FIMP) for overground trips and slips

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Jie Kai Er ◽  
Cyril John William Donnelly ◽  
Seng Kwee Wee ◽  
Wei Tech Ang
Keyword(s):  
Real World ◽  
Muscle Activation ◽  
Statistical Parametric Mapping ◽  
Detection Algorithm ◽  
Repeated Measure ◽  
Future Research ◽  
Phase Detection ◽  
In Series ◽  
Gait Phase ◽  
Movable Platform

Abstract Background The study of falls and fall prevention/intervention devices requires the recording of true falls incidence. However, true falls are rare, random, and difficult to collect in real world settings. A system capable of producing falls in an ecologically valid manner will be very helpful in collecting the data necessary to advance our understanding of the neuro and musculoskeletal mechanisms underpinning real-world falls events. Methods A fall inducing movable platform (FIMP) was designed to arrest or accelerate a subject’s ankle to induce a trip or slip. The ankle was arrested posteriorly with an electromagnetic brake and accelerated anteriorly with a motor. A power spring was connected in series between the ankle and the brake/motor to allow freedom of movement (system transparency) when a fall is not being induced. A gait phase detection algorithm was also created to enable precise activation of the fall inducing mechanisms. Statistical Parametric Mapping (SPM1D) and one-way repeated measure ANOVA were used to evaluate the ability of the FIMP to induce a trip or slip. Results During FIMP induced trips, the brake activates at the terminal swing or mid swing gait phase to induce the lowering or skipping strategies, respectively. For the lowering strategy, the characteristic leg lowering and subsequent contralateral leg swing was seen in all subjects. Likewise, for the skipping strategy, all subjects skipped forward on the perturbed leg. Slip was induced by FIMP by using a motor to impart unwanted forward acceleration to the ankle with the help of friction-reducing ground sliding sheets. Joint stiffening was observed during the slips, and subjects universally adopted the surfing strategy after the initial slip. Conclusion The results indicate that FIMP can induce ecologically valid falls under controlled laboratory conditions. The use of SPM1D in conjunction with FIMP allows for the time varying statistical quantification of trip and slip reactive kinematics events. With future research, fall recovery anomalies in subjects can now also be systematically evaluated through the assessment of other neuromuscular variables such as joint forces, muscle activation and muscle forces.

scholarly journals Fall Inducing Movable Platform (FIMP) for Overground Trips and Slips

2020 ◽  
Author(s):  
Jie Kai Er ◽  
Cyril John William Donnelly ◽  
Seng Kwee Wee ◽  
Wei Tech Ang
Keyword(s):  
Statistical Parametric Mapping ◽  
Detection Algorithm ◽  
Repeated Measure ◽  
Phase Detection ◽  
Experimental Conditions ◽  
Gait Phase ◽  
Movable Platform ◽  
Fall Recovery ◽  
Fall Mechanisms ◽  
Fall Prevention Intervention

Abstract The study of falls and any related fall prevention/intervention device requires the recording of true falls incidence. However, true falls are rare, random and difficult to collect. Therefore, a system that can perturb falls in an ecologically valid and repeatedly manner will greatly benefit the understanding of the neuromuscular mechanisms underpinning real-world falls events. A fall inducing movable platform (FIMP) was designed to arrest and accelerate the subject's ankle to induce trip via a brake and slip via a motor respectively. A gait phase detection algorithm was also created to allow the timely activation of the fall mechanisms to induce different recovery actions. Statistical Parametric Mapping (SPM1D) and two sample t-test were used to evaluate the transparency of the platform before it was used to induce falls. Thereafter, SPM1D and one-way repeated measure ANOVA were used assess the effectiveness of FIMP in inducing realistic falls. Walking with the FIMP's fall mechanisms attached on the ankle (SW) was found to be similar to normal walking (NW), except for a slight increase in ankle flexion during the swing phase. However, the magnitude of change would be considered negligible when compared to the changes in joint angles during the trips and slips of interest. During the FIMP induced trips, the brake activates at the terminal-swing and mid-swing gait phase to induce the lowering and skipping strategies respectively. The characteristic leg lowering and the subsequent contralateral leg swing was seen in all subjects for the lowering strategy. Likewise, for skipping strategy, all subjects skipped forward on the perturbed leg. On the other hand, slip was induced by FIMP using the motor to impart unwanted forward acceleration to the ankle with the help of friction-reducing ground sliding sheets. Joints stiffening was observed during slips, and subjects adopt the \textit{surfing} strategy after the initial slip. Results indicate that FIMP can induce reliable and ecologically valid falls repeatedly under simulated experimental conditions. The usage of SPM1D with FIMP allows the creation of the first ever quantifiable trip and slip reactive kinematics comparison. Effects of fall recovery anomalies can now be easily identified.

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