Real-time Gait Phase Detection Research Articles

Detecting Toe-Off and Initial Contact in Real-Time With Self-Adapting Thresholds.

This research introduces an adaptive control algorithm designed to determine gait phase in real-time using an inertial measurement unit (IMU) affixed to the shank. Focusing on detecting specific gait events, primarily initial contact (IC) and toe-off (TO), the algorithm utilizes dynamic thresholds and ratios that facilitate accurate event determination adaptively across a range of walking speeds. Built-in safety checks further ensure precision and minimize false detections. We validated the algorithm with eight participants walking at varying speeds. The algorithm demonstrated promising results in detecting IC and TO events with mean lead of 8.95 ms and 4.42 ms and detection success rate of 100% and 99.72%, respectively. These results are consistent with benchmarks from established algorithms (Hanlon and Anderson, 2009, "Real-Time Gait Event Detection Using Wearable Sensors," Gait Posture, 30(4), pp. 523-527; Maqbool et al., 2017, "A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation," IEEE Trans. Neural Syst. Rehabil. Eng.: Publ. IEEE Eng. Med. Biol. Soc., 25(9), pp. 1500-1509). Moreover, the algorithm's self-adaptive nature ensures it can be used in scenarios of varying movement, offering a promising solution for real-time gait phase detection.

A Real-Time Gait Phase Detection Method Based on BiLSTM-Attention Model.

Real-time gait phase detection is essential to achieve accurate and stable walking assistance in intelligent rehabilitation training for patients with motor disorders. This study proposed an efficient real-time detection method to detect three gait phases (loading response, stance, and swing) based on a bidirectional long short-term memory network with an attention layer (BiLSTM-Attention). We validated our method on a public dataset where eight healthy subjects' data during treadmill walking were employed. A single inertial measurement unit (IMU) was attached to the shank to measure the sagittal plane acceleration of the lower leg and the angular velocity around the central lateral axis. These data were transposed and segmented into data sequences based on labels using a sliding window method. The data from 8 participants were divided into the training, validation, and test sets (5:1:2). Results showed the average recognition accuracy of the proposed model on new subjects was 97.40% with an average time delay of 15.7±10.1ms, showing the method's potential to be applied for practice use.

Real-Time Gait Phase Detection on Wearable Devices for Real-World Free-Living Gait.

Detecting gait phases with wearables unobtrusively and reliably in real-time is important for clinical gait rehabilitation and early diagnosis of neurological diseases. Due to hardware limitations of microcontrollers in wearable devices (e.g., memory and computation power), reliable real-time gait phase detection on the microcontrollers remains a challenge, especially for long-term real-world free-living gait. In this work, a novel algorithm based on a reduced support vector machine (RSVM) and a finite state machine (FSM) is developed to address this. The RSVM is developed by exploiting the cascaded K-means clustering to reduce the model size and computation time of a standard SVM by 88% and a factor of 36, with only minor degradation in gait phase prediction accuracy of around 4%. For each gait phase prediction from the RSVM, the FSM is designed to validate the prediction and correct misclassifications. The developed algorithm is implemented on a microcontroller of a wearable device and its real-time (on the fly) classification performance is evaluated by twenty healthy subjects walking along a predefined real-world route with uncontrolled free-living gait. It shows a promising real-time performance with an accuracy of 91.51%, a sensitivity of 91.70%, and a specificity of 95.77%. The algorithm also demonstrates its robustness with varying walking conditions.

An adaptive reflexive control strategy for walking assistance system based on functional electrical stimulation.

Functional electrical stimulation (FES) neuroprostheses have been regarded as an effective approach for gait rehabilitation and assisting patients with stroke or spinal cord injuries. A multiple-channel FES system was developed to improve the assistance and restoration of lower limbs. However, most neuroprostheses need to be manually adjusted and cannot adapt to individual needs. This study aimed to integrate the purely reflexive FES controller with an iterative learning algorithm while a multiple-channel FES walking assistance system based on an adaptive reflexive control strategy has been established. A real-time gait phase detection system was developed for accurate gait phase detection and stimulation feedback. The reflexive controller generated stimulation sequences induced by the gait events. These stimulation sequences were updated for the next gait cycle through the difference between the current and previous five gait cycles. Ten healthy young adults were enrolled to validate the multiple-channel FES system by comparing participants' gait performance to those with no FES controller and purely reflexive controller. The results showed that the proposed adaptive FES controller enabled the adaption to generate fitted stimulation sequences for each participant during various treadmill walking speeds. The maximum, minimum, and range of motion (ROM) of the hip, knee, and ankle joints were furtherly improved for most participants, especially for the hip and knee flexion and ankle dorsiflexion compared with the purely reflexive FES control strategy. The presented system has the potential to enhance motor relearning and promote neural plasticity.

Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm.

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer.

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.

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.

Curve Similarity Model for Real-Time Gait Phase Detection Based on Ground Contact Forces.

This paper proposed a new novel method to adaptively detect gait patterns in real time through the ground contact forces (GCFs) measured by load cell. The curve similarity model (CSM) is used to identify the division of off-ground and on-ground statuses, and differentiate gait patterns based on the detection rules. Traditionally, published threshold-based methods detect gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. In this paper, the curve is composed of a series of continuous or discrete ordered GCF data points, and the CSM is built offline to obtain a training template. Then, the testing curve is compared with the training template to figure out the degree of similarity. If the computed degree of similarity is less than a given threshold, they are considered to be similar, which would lead to the division of off-ground and on-ground statuses. Finally, gait patterns could be differentiated according to the status division based on the detection rules. In order to test the detection error rate of the proposed method, a method in the literature is introduced as the reference method to obtain comparative results. The experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively, and obtain a low error rate compared with the reference method.

Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs.

This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez–Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.

A new fall-inducing technology platform: Development and assessment of a programmable split-belt treadmill.

Typical technologies for fall reduction/prevention training incorporate mechanical obstacles or cables/pulleys to induce trip or slip perturbations. This paper proposes a technology platform that uses a split-belt treadmill equipped with one force plate underneath each belt and a real-time gait phase detection algorithm. A proof-of-concept study validates the method for inducing trip perturbations in healthy young adults (n=10) by using kinematic measures from a full body motion capture system to characterize the effects of the perturbations. Preliminary results show that the proposed method successfully induces a trip and its congruent postural responses. The major findings have implications for designing intervention programs to reduce or prevent falls by individuals with a high risk of falls.

Development of real-time gait phase detection system for a lower extremity exoskeleton robot

In this paper, we develop an insole sensor system that can determine various dynamic models of a lower extremity exoskeleton. The study analyzed the kinematic model of exoskeleton robot for lower limb that changes according to the gait phase detection of a human. Based on the ground reaction force (GRF) that is generated when walking, the sensing type, location, and the number of sensors were selected to proceed on insole sensor development. Using the COP, an algorithm was developed that is capable of detecting gait phase with small number of sensors. An experiment was conducted to evaluate the developed insole sensor system and the gait phase detection algorithm.

Real-time data-driven gait phase detection using ground contact force measurements: Algorithms, platform design and performance

Abstract The world is experiencing an unprecedented, enduring, and pervasive aging process. With more people who need walking assistance, the demand for gait rehabilitation has increased rapidly over the years. Effective gait rehabilitation requires a comprehensive gait analysis, in which gait phase detection plays an important role. Although many specialized sensing systems have been developed for gait monitoring, most existing gait phase detection algorithms rely on significant input from medical professionals, which are subjective, manual and inaccurate. To address these problems, this paper presents a data-driven approach for real-time gait phase detection. The approach combines an infinite Gaussian mixture model (IGMM) to classify different gait phases based on the ground contact force (GCF) measurement, and a parallel particle filter to estimate and update the model parameters. Effective particle sharing mechanisms are further designed to distribute particles among different working nodes judiciously and thus strike a good balance between computational overhead and estimation accuracy. The proposed algorithm is implemented in our gait monitoring and analysis platform developed on a VMware cluster. Its estimation accuracy and computational efficiency are examined through extensive experiments using the data traces collected from both healthy human subjects and patients with neurological disorders.

RehaMovePro: A Versatile Mobile Stimulation System for Transcutaneous FES Applications.

Functional Electrical Stimulation is a commonly used method in clinical rehabilitation and research to trigger useful muscle contractions by electrical stimuli. In this work, we present a stimulation system for transcutaneous electrical stimulation that gives extensive control over the stimulation waveform and the stimulation timing. The system supports electrode arrays, which have been suggested to achieve better selectivity and to simplify electrode placement. Electromyography (EMG) measurements are obtained from the active stimulation electrodes (between the stimulation pulses) or from separate surface EMG electrodes. The modular design enables the implementation of sophisticated stimulation control systems including external triggers or wireless sensors. This is demonstrated by the standalone implementation of a feedback-controlled drop foot neuroprosthesis, which uses a wireless inertial sensor for realtime gait phase detection and foot orientation measurement.

Realtime EMG analysis for transcutaneous electrical stimulation assisted gait training in stroke patients

Abstract: This contribution describes a method for realtime analysis of muscle activity during application of Functional Electrical Stimulation (FES) to the assessed muscles. Inertial sensors at the foot are used for realtime gait phase detection in order to synchronize the stimulation with the gait. After detecting and muting stimulation artifacts and after extraction of Inputer-Pulse Intervals (IPIs), a non-causal high-pass filter is applied to a section of the IPI to extract the voluntary EMG activity. The filter suppresses FES-evoked EMG activity (M-wave) and electrode discharging artifacts. The initial filter states are chosen by an optimization procedure to minimize undesired filter transients. The obtained filtered EMG signal is then rectified and averaged to produce a scalar measure of the volitional EMG activity over the last IPI. The volitional EMG activity during four different detected gait phases is calculated after every completed step and displayed to the stroke patients for biofeedback or to the therapist in order to adjust the FES. The system has been initially evaluated with healthy subjects walking on a treadmill. It was demonstrated that different walking styles of an individual can be distinguished by the EMG analysis also during active FES support.

Adaptive method for real-time gait phase detection based on ground contact forces

A novel method is presented to detect real-time gait phases based on ground contact forces (GCFs) measured by force sensitive resistors (FSRs). The traditional threshold method (TM) sets a threshold to divide the GCFs into on-ground and off-ground statuses. However, TM is neither an adaptive nor real-time method. The threshold setting is based on body weight or the maximum and minimum GCFs in the gait cycles, resulting in different thresholds needed for different walking conditions. Additionally, the maximum and minimum GCFs are only obtainable after data processing. Therefore, this paper proposes a proportion method (PM) that calculates the sums and proportions of GCFs wherein the GCFs are obtained from FSRs. A gait analysis is then implemented by the proposed gait phase detection algorithm (GPDA). Finally, the PM reliability is determined by comparing the detection results between PM and TM. Experimental results demonstrate that the proposed PM is highly reliable in all walking conditions. In addition, PM could be utilized to analyze gait phases in real time. Finally, PM exhibits strong adaptability to different walking conditions.