Real-Time Tool for Human Gait Detection from Lower Trunk Acceleration

2018 ◽  
pp. 9-18
Author(s):  
Helena R. Gonçalves ◽  
Rui Moreira ◽  
Ana Rodrigues ◽  
Graça Minas ◽  
Luís Paulo Reis ◽  
...  
Keyword(s):  
Real Time ◽  
Human Gait ◽  
Lower Trunk ◽  
Trunk Acceleration

scholarly journals Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4792
Author(s):  
Denisa Nohelova ◽  
Lucia Bizovska ◽  
Nicolas Vuillerme ◽  
Zdenek Svoboda
Keyword(s):  
Dual Task ◽  
Inertial Sensor ◽  
Cognitive Task ◽  
Real Life ◽  
Gait Pattern ◽  
Gait Variability ◽  
Outdoor Environment ◽  
Manual Task ◽  
Lower Trunk ◽  
Trunk Acceleration

Nowadays, gait assessment in the real life environment is gaining more attention. Therefore, it is desirable to know how some factors, such as surfaces (natural, artificial) or dual-tasking, influence real life gait pattern. The aim of this study was to assess gait variability and gait complexity during single and dual-task walking on different surfaces in an outdoor environment. Twenty-nine healthy young adults aged 23.31 ± 2.26 years (18 females, 11 males) walked at their preferred walking speed on three different surfaces (asphalt, cobbles, grass) in single-task and in two dual-task conditions (manual task—carrying a cup filled with water, cognitive task—subtracting the number 7). A triaxial inertial sensor attached to the lower trunk was used to record trunk acceleration during gait. From 15 strides, sample entropy (SampEn) as an indicator of gait complexity and root mean square (RMS) as an indicator of gait variability were computed. The findings demonstrate that in an outdoor environment, the surfaces significantly impacted only gait variability, not complexity, and that the tasks affected both gait variability and complexity in young healthy adults.

scholarly journals Lower Trunk Acceleration Signals Reflect Fall Risk During Walking

2016 ◽  
Vol E99.D (6) ◽  
pp. 1482-1484
Author(s):  
Yoshitaka OTANI ◽  
Osamu AOKI ◽  
Tomohiro HIROTA ◽  
Hiroshi ANDO
Keyword(s):  
Fall Risk ◽  
Lower Trunk ◽  
Trunk Acceleration ◽  
Acceleration Signals

Real-time gait event detection for normal subjects from lower trunk accelerations

2010 ◽  
Vol 31 (3) ◽  
pp. 322-325 ◽  
Author(s):  
Rafael C. González ◽  
Antonio M. López ◽  
Javier Rodriguez-Uría ◽  
Diego Álvarez ◽  
Juan C. Alvarez
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Normal Subjects ◽  
Lower Trunk

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 Differences in Gait Stability and Acceleration Characteristics Between Healthy Young and Older Females

2021 ◽  
Vol 2 ◽  
Author(s):  
Yuge Zhang ◽  
Xinglong Zhou ◽  
Mirjam Pijnappels ◽  
Sjoerd M. Bruijn
Keyword(s):  
Inertial Sensors ◽  
Gait Stability ◽  
Gait Performance ◽  
Young Females ◽  
Trunk Stability ◽  
Lower Trunk ◽  
Amplitude Maximum ◽  
Lower Back ◽  
Acceleration Amplitude ◽  
Trunk Acceleration

Our aim was to evaluate differences in gait acceleration intensity, variability, and stability of feet and trunk between older females (OF) and young females (YF) using inertial sensors. Twenty OF (mean age 68.4, SD 4.1 years) and 18 YF (mean age 22.3, SD 1.7 years) were asked to walk straight for 100 meters at their preferred speed, while wearing inertial sensors on their heels and lower back. We calculated spatiotemporal measures, foot and trunk acceleration characteristics, their variability, and trunk stability using the local divergence exponent (LDE). Two-way ANOVA (such as the factors foot and age), Student's t-test and Mann–Whitney U test were used to compare statistical differences of measures between groups. Cohen's d effects were calculated for each variable. Foot maximum vertical (VT) acceleration and amplitude, trunk-foot VT acceleration attenuation, and their variability were significantly smaller in OF than in YF. In contrast, trunk mediolateral (ML) acceleration amplitude, maximum VT acceleration, amplitude, and their variability were significantly larger in OF than in YF. Moreover, OF showed lower stability (i.e., higher LDE values) in ML acceleration, ML, and VT angular velocity of the trunk. Even though we measured healthy OF, these participants showed lower VT foot accelerations with higher VT trunk acceleration, lower trunk-foot VT acceleration attenuation, less gait stability, and more variability of the trunk, and hence, were more likely to fall. These findings suggest that instrumented gait measurements may help for early detection of changes or impairments in gait performance, even before this can be observed by clinical eye or gait speed.

scholarly journals Development of a Robust, Simple, and Affordable Human Gait Analysis System Using Bottom-Up Pose Estimation With a Smartphone Camera

2022 ◽  
Vol 12 ◽  
Author(s):  
Aditya Viswakumar ◽  
Venkateswaran Rajagopalan ◽  
Tathagata Ray ◽  
Pranitha Gottipati ◽  
Chandu Parimi
Keyword(s):  
Gait Analysis ◽  
Real Time ◽  
Spinal Cord Injuries ◽  
Video Camera ◽  
Medical Diagnostics ◽  
Human Gait ◽  
Bottom Up ◽  
Flexion Extension ◽  
Analysis System ◽  
Ms Kinect

Gait analysis is used in many fields such as Medical Diagnostics, Osteopathic medicine, Comparative and Sports-related biomechanics, etc. The most commonly used system for capturing gait is the advanced video camera-based passive marker system such as VICON. However, such systems are expensive, and reflective markers on subjects can be intrusive and time-consuming. Moreover, the setup of markers for certain rehabilitation patients, such as people with stroke or spinal cord injuries, could be difficult. Recently, some markerless systems were introduced to overcome the challenges of marker-based systems. However, current markerless systems have low accuracy and pose other challenges in gait analysis with people in long clothing, hiding the gait kinematics. The present work attempts to make an affordable, easy-to-use, accurate gait analysis system while addressing all the mentioned issues. The system in this study uses images from a video taken with a smartphone camera (800 × 600 pixels at an average rate of 30 frames per second). The system uses OpenPose, a 2D real-time multi-person keypoint detection technique. The system learns to associate body parts with individuals in the image using Convolutional Neural Networks (CNNs). This bottom-up system achieves high accuracy and real-time performance, regardless of the number of people in the image. The proposed system is called the “OpenPose based Markerless Gait Analysis System” (OMGait). Ankle, knee, and hip flexion/extension angle values were measured using OMGait in 16 healthy volunteers under different lighting and clothing conditions. The measured kinematic values were compared with a standard video camera based normative dataset and data from a markerless MS Kinect system. The mean absolute error value of the joint angles from the proposed system was less than 90 for different lighting conditions and less than 110 for different clothing conditions compared to the normative dataset. The proposed system is adequate in measuring the kinematic values of the ankle, knee, and hip. It also performs better than the markerless systems like MS Kinect that fail to measure the kinematics of ankle, knee, and hip joints under dark and bright light conditions and in subjects with long robe clothing.

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