Gait Phase Detection Research Articles

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

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.

Generation of Gait Events with a FSR Based Cane Handle.

Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited to obtain information about the gait. This is interesting especially in the case of unilateral assistance, for instance in the case of one side lower limb exoskeletons. If the cane is instrumented, it can hold sensors that otherwise should be attached to the body of the impaired user. This can ease the use of the assistive system in daily life as well as its acceptance. Moreover, Force Sensing Resistors (FSRs) are common in gait phase detection systems, and force sensors are also common in user intention detection. Therefore, a cane that incorporates FSRs on the handle can take advantage from the direct interface with the human and provide valuable information to implement real-time control. This is done in this paper, and the results confirm that many events are detected from variables derived from the readings of the FSRs that provide rich information about gait. However, a large inter-subject variability points to the need of tailored control systems.

Gait phase detection by using a portable system and artificial neural network

Gait phases are important to evaluate the walking function and to identify the characteristics of pathological gaits. However, it is difficult to differentiate gait phases outside gait laboratories, thus, this study aimed to develop a method to detect 8 gait sub-phases using a wearable multiple sensor system and artificial neural network (ANN). Motion sensors were used to acquire the acceleration of lower limbs, and force sensitive resistors were used to detect contact state and force between the foot and the ground. Walking was recorded using a high-speed camera. Two feed forward back-propagation (BP) neural networks were developed. The resilient BP algorithm was used to train ANN. A total of 66 volunteers participated in this study. For the stance and swing phase detection, simulation of the training data showed an accuracy of 98.0 ​%. The data from the test set showed a recognition accuracy of 97.75 ​%. Because the ending point of the last phase ‘Terminal Swing’ is always 100 ​% GC, we only listed seven phases. The prediction accuracy of seven phases were: 35.9 ​%, 63.8 ​%, 93.6 ​%, 94.9 ​%, 94.8 ​%, 97.9 ​% and 98 ​% using the limb acceleration data only. The average accuracy for seven phases were 68 ​%, 91.3 ​%, 97.8 ​%, 98.9 ​%, 98.8 ​%, 99.1 ​%, and 99.5 ​% using the limb acceleration and foot pressure data for fast, normal, and slow gait speeds. This study provides a new method for eight gait sub-phases detection with high accuracy combining a wearable system and ANN, which may make gait phase analysis possible under free-living conditions.

Gait phase detection based on inertial measurement unit and force-sensitive resistors embedded in a shoe.

This study proposes a system to detect the phases of gait. It consists of an intelligent shoe equipped with an inertial measurement unit (IMU) and force-sensitive resistors (FSRs), and it uses a compound method to recognize gait. The continuous wavelet transform is applied according to accelerations obtained via the IMU to identify heel strike and toe-off events. These events are used to calculate the pressure threshold and proportional factor via the Lopez-Meyer (LM) method by using minimal leave-one-out for training and validation. The LM method can identify the entire sub-phase of the stance of the gait based on ground contact forces measured by using the FSRs and rules of gait event detection. The proposed system was tested on five healthy volunteers who used the intelligent shoe. The results show that it can detect all sub-phases of the gait with an overall accuracy (96%) higher than the LM method. The proportional factor was adaptable to variable body weights, and the reported average errors of competing systems in the literature significantly exceeded the average variation of the proposed system for all phases of gait. The range of errors in the swing phase and sub-phases of stance was also acceptable for application purposes. When the size of the subject's foot was close to that of the intelligent shoe, the error between normative data and phases of gait identified by the detection system was minimal. Furthermore, the proposed system detected abnormalities in the gait circle, and thus, it can be used to monitor the walking activity and measure the motor recovery.

Evaluation of Individualized Functional Electrical Stimulation-Induced Acute Changes during Walking: A Case Series in Children with Cerebral Palsy.

Functional electrical stimulation (FES) walking interventions have demonstrated improvements to gait parameters; however, studies were often confined to stimulation of one or two muscle groups. Increased options such as number of muscle groups targeted, timing of stimulation delivery, and level of stimulation are needed to address subject-specific gait deviations. We aimed to demonstrate the feasibility of using a FES system with increased stimulation options during walking in children with cerebral palsy (CP). Three physical therapists designed individualized stimulation programs for six children with CP to target participant-specific gait deviations. Stimulation settings (pulse duration and current) were tuned to each participant. Participants donned our custom FES system that utilized gait phase detection to control stimulation to lower extremity muscle groups and walked on a treadmill at a self-selected speed. Motion capture data were collected during walking with and without the individualized stimulation program. Eight gait metrics and associated timing were compared between walking conditions. The prescribed participant-specific stimulation programs induced significant change towards typical gait in at least one metric for each participant with one iteration of FES-walking. FES systems with increased stimulation options have the potential to allow the physical therapist to better target the individual’s gait deviations than a one size fits all device.

Wearable energy-efficient fitness tracking system for sports person health monitoring application

The high potential of wearable physiological sensors in regenerative medicine and continuous monitoring of human health is currently of great interest. In measuring in real-time and non-invasively highly heterogeneous constituents, have a great deal of work and therefore been pushed into creating several sports monitoring sensors. The advanced engineering research and technology lead to the design of a wearable energy-efficient fitness tracking (WE2FT) system for sports person health monitoring application. Instantaneous accelerations are measured against pulses, and specific walking motions can be tracked by this system using a deep learning-based integrated approach of an intelligent algorithm for gait phase detection for the proposed system (WE2FT). The algorithm’s effects are addressed, and the performance has been evaluated. In this study, the algorithm uses a smartphone application to track steps using the Internet of Things (IoT) technology. For this initiative, the central node’s optimal location is measured with the antenna reflectance coefficient and CM3A path loss model (IEEE 802.15.6) among the sensor nodes for energy-efficient communication. The simulation experiment results in the highest performance in terms of energy efficiency and path loss.

Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units

Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units

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

PurposeThe 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/approachGait 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.FindingsThe 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 implicationsThis 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/valueThe 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.

Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Then, ensemble learning method that combines the outputs of multiple classifiers into a single fusion-produced output was implemented. The results obtained from data collected from four human participants revealed that the light gradient boosting machine (LightGBM) algorithm has an average accuracy of 93.1%, a macro-F1 score of 0.929, and a calculation time of prediction of 15 ms in discriminating 12 different gait phases on three terrains. This was better than traditional voting-based multiple classifier fusion methods. LightGBM is a perfect choice for gait phase detection on different terrains in daily life.

A Gait Phase Detection Method in Complex Environment Based on DTW-Mean Templates

Gait phase detection is essential for the wearable powered exoskeletons. This paper proposes an online gait phase detection method based on dynamic time warping mean (DTW-MEAN) templates using ground contact forces (GCFs). There are two important methods of gait phase detection, which are the template matching and statistical method. Template matching methods such as DTW are robust and have been widely used in the field of gait phase detection. However, the template matching is a kind of method based on distance measure, and the gait phase detection will ignore the coupling connections between the gait sequences; on the contrary, the statistical method can fuse these connections well. In this paper, a statistical mean method based on DTW is proposed to solve the problem of recognizing phases between gait sequences that are not correctly detected by single template methods. We tested our approach in complex environment (such as different terrains, different payloads, and different speeds) and gained over 95% accuracy and time difference below 25ms. Our proposed approach obtained better detection results with the advantage of no need to retrain templates.

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model.

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

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

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.

Real-Time Gait Phase Recognition Based on Time Domain Features of Multi-MEMS Inertial Sensors

Gait phase analysis is widely used in disease diagnosis, rehabilitation training, and other fields by studying the characteristics of human gait. It systematically evaluates human body’s skeletal muscles and nerves with combined disciplines. Microelectromechanical system (MEMS) inertial sensors are extensively used in attitude detection because of its high-precision, portability, and good real-time performance in time-domain analysis. In this article, we presented a multi-degree-of-freedom MEMS gait detection method, which resolved the problems of single sensor and limited gait phase. We designed a sensor-based gait signal acquisition system, in which gait data acquisition program and feature analysis algorithm were compiled to verify the feasibility of the proposed method. We performed coordinate transformation and corrected position information to eliminate the gait phase detection error caused by random noise interference. Acceleration and angular velocity information were collected from 20 experimenters. We applied an adaptive threshold gait phase detection algorithm to classify the gait information collected by single sensor. To improve the results of gait phase classification, we used multisensor redundant measurement to analyze characteristics of five gait phases. The acceleration and angular velocity information collected by the three sensors placed at instep, ankle, and thigh were input into support vector machine (SVM). The classification results of the five gait phases are approximately 90%. Lastly, we built a human body structure model to simulate human motion in real time, realizing the real-time gait phase detection, which proves effectiveness of the proposed algorithm.

Identification of Gait Events in Healthy and Parkinson’s Disease Subjects Using Inertial Sensors: A Supervised Learning Approach

Automatic detection of gait phases through supervised learning is a feasible approach that takes advantage of the consistency of the gait cycle among healthy subjects. However, gait patterns among subjects with impairments are not as consistent and most of the existing algorithms have limited performance detecting phases during impaired gait. In this paper, we proposed one algorithm that used linear classifiers to detect in real-time the transition between consecutive gait phases. Our approach is a generalization of the rule- and threshold-based algorithms for event detection. Linear classifiers are parametric models that require appropriate values in order to perform correct classification of the gait phases. We introduced a modified Support Vector Machine (SVM) to compute such sub-optimal combinations of those values, and a further optimization with a hybrid meta-heuristic approach that integrates a Genetic and a Simulated Annealing Algorithm. We tested our approach on data collected by a single-IMU foot-mounted wearable device during overground and treadmill walking for two groups: one with healthy and one with Parkinson’s Disease subjects. The ${F}_1$ -scores were 0.987 and 0.953 for the two groups, which were comparable with our previously developed threshold-based method, which obtained 0.988 and 0.974, respectively. Our proposed approach achieved similar performance as the threshold-based scheme, with the advantage of not relying on any prior knowledge of specific features for any particular inertial signal.

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

BackgroundThe 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.MethodsA 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.ResultsDuring 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.ConclusionThe 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.

Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition

Human gait phase detection is a significance technology for robotics exoskeletons control and exercise rehabilitation therapy. Inertial Measurement Units (IMUs) with accelerometer and gyroscope are convenient and inexpensive to collect gait data, which are often used to analyze gait dynamics for personal daily applications. However, current deep-learning methods that extract spatial and the isolated temporal features can easily ignore the correlation that may exist in the high-dimensional space, which limits the recognition effect of a single model. In this study, an effective hybrid deep-learning framework based on Gaussian probability fusion of multiple spatiotemporal networks (GFM-Net) is proposed to detect different gait phases from multisource IMU signals. Furthermore, it first employs the gait information acquisition system to collect IMU data fixed on lower limb. With the data preprocessing, the framework constructs a spatial feature extractor with AutoEncoder and CNN modules and a multistream temporal feature extractor with three collateral modules combining RNN, LSTM, and GRU modules. Finally, the novel Gaussian probability fusion module optimized by the Expectation-Maximum (EM) algorithm is developed to integrate the different feature maps output by the three submodels and continues to realize gait recognition. The framework proposed in this paper implements the inner loop that also contains the EM algorithm in the outer loop and optimizes the reverse gradient in the entire network. Experiments show that this method has better performance in gait classification with accuracy reaching more than 96.7%.

Accurate gait modelling based on waveform scaling before DFT

Gait modelling is essential for many applications including animation, activity recognition, medical diagnosis, and robotics. Many researchers have worked on mathematically express the movement of human bodies. At the current stage, the reconstructed waveforms from the mathematical expressions either represent smoothened waveforms, noisy, or require a high number of computations. In this study, the thigh and shank angle waveforms are time and amplitude scaled before performing a discrete Fourier transform (DFT). By doing so, the correlation coefficient between the original and reconstructed waveforms can be improved without increasing the number of harmonics. The shank's angular velocity is also recalculated from the reconstructed shank's angle waveform for gait phase detection, and shows accurate results in heel and toe strikes estimation when compared to the original shank's angular velocity. Additionally, the harmonic components of the waveforms are used for gait recognition. Experimental results show that it is useful to time and amplitude-scale the angle waveforms to ‘enlarge’ the distinctive regions of the angle waveforms for better classification accuracy.

Adaptive body movement system for wearable IoT instruments based on matrix vector parameter estimation

Presently, the explosive growth of the portable Internet of Things (IoT) products presents a major challenge to the safety of wearable IoT devices by collecting significant amounts of sensitive information. In the area of protection and access control, the gyro sensor-based shift recognition is seen as a new technology that is emerging and achieved excellent performance at certain speeds. Therefore, a survey suggested that both illness and behaviour can impact gait habits, the gait criteria for clinical applications cannot be used by clinicians without awareness of the activities. Hence in this paper, the acceleration- gait cycle adaptive optimization technique (AGAO) hybridized with a non-linear model of gait recognition based on matrix–vector parameter estimation (MVP) has been proposed to modify the approach for producing a corresponding threshold that can effectively reduce cognitive-related problems. The detection of gait phases is taken into account in more accurate and difficult situations where the subject goes through mental tasks. Compared with adaptive gait cycle extraction (AGC) and linear discriminant analysis (LDA), AGAO -MVP reduces the duration of the algorithm by incorporating data from surface EMG sensors into the IoT system. Experimental results indicate that AGAO -MVP is more accurate in terms of performance accuracy and shows that cognitive task limits between the pose phase and the swing cycle become more dynamic.

Gait event detection based on inter-joint coordination using only angular information

The detection of gait events with wearable sensors is necessary for a robotic system interacting with walking people. Conventional gait phase detection methods are based on machine learning. However, this method cannot detect a gait event every gait cycle because it is difficult to extract characteristic points. Additionally, using only angular information for detection is beneficial because angular information is needed for the control and evaluation of the robots. This paper proposes a novel algorithm for the detection of heel contact and toe-off using the inter-joint coordination of the hip, knee, and ankle joints that has a lower-dimensional structure. The proposed algorithm derives the four planes in the angular space and finds the switching points of the planes. Seven participants walked on force plates that measured the force of the foot against the floor. The error was less than 0.035 s when the gait events were detected after calculating planes using the first gait datum. The change in the patterns of the inter-joint coordination reflected the change in gait phases. Although the data were calculated offline, the results show that the heel contact and toe-off could be detected as soon as the angles were sensed once the planes were derived.

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses.

Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.