scholarly journals Evaluation of Gait Phase Detection Delay Compensation Strategies to Control a Gyroscope-Controlled Functional Electrical Stimulation System During Walking

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2471 ◽  
Author(s):  
Nicole Zahradka ◽  
Ahad Behboodi ◽  
Henry Wright ◽  
Barry Bodt ◽  
Samuel Lee
Keyword(s):  
Electrical Stimulation ◽  
Motion Capture ◽  
Functional Electrical Stimulation ◽  
Gait Cycle ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Phase Detection ◽  
System Delay ◽  
Finite State ◽  
Gait Phase

Functional electrical stimulation systems are used as neuroprosthetic devices in rehabilitative interventions such as gait training. Stimulator triggers, implemented to control stimulation delivery, range from open- to closed-loop controllers. Finite-state controllers trigger stimulators when specific conditions are met and utilize preset sequences of stimulation. Wearable sensors provide the necessary input to differentiate gait phases during walking and trigger stimulation. However, gait phase detection is associated with inherent system delays. In this study, five stimulator triggers designed to compensate for gait phase detection delays were tested to determine which trigger most accurately delivered stimulation at the desired times of the gait cycle. Motion capture data were collected on seven typically-developing children while walking on an instrumented treadmill. Participants wore one inertial measurement unit on each ankle and gyroscope data were streamed into the gait phase detection algorithm. Five triggers, based on gait phase detection, were used to simulate stimulation to five muscle groups, bilaterally. For each condition, stimulation signals were collected in the motion capture software via analog channels and compared to the desired timing determined by kinematic and kinetic data. Results illustrate that gait phase detection is a viable finite-state control, and appropriate system delay compensations, on average, reduce stimulation delivery delays by 6.7% of the gait cycle.

scholarly journals ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2389 ◽  
Author(s):  
Huong Vu ◽  
Felipe Gomez ◽  
Pierre Cherelle ◽  
Dirk Lefeber ◽  
Ann Nowé ◽  
...  
Keyword(s):  
Gait Cycle ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Deep Learning Algorithm ◽  
Gait Phase ◽  
Validation Set ◽  
Fully Connected

Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection is considered to be of great importance, as achieving high detection accuracy will produce a more precise, stable, and safe rehabilitation device. In this paper, we propose a novel gait percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects’ signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications.

Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction

2013 ◽  
Vol 34 (5) ◽  
pp. 541-565 ◽  
Author(s):  
Jun-Uk Chu ◽  
Kang-Il Song ◽  
Sungmin Han ◽  
Soo Hyun Lee ◽  
Ji Yoon Kang ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Sciatic Nerve ◽  
Functional Electrical Stimulation ◽  
Foot Drop ◽  
Phase Detection ◽  
Gait Phase ◽  
Nerve Recordings

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

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4452
Author(s):  
Nicole Zahradka ◽  
Ahad Behboodi ◽  
Ashwini Sansare ◽  
Samuel C. K. Lee
Keyword(s):  
Cerebral Palsy ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Case Series ◽  
Phase Detection ◽  
Lower Extremity Muscle ◽  
Children With Cerebral Palsy ◽  
Muscle Groups

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.

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.

Tracking ankle joint movements during gait cycle via control of functional electrical stimulation

2021 ◽  
pp. 095441192110523
Author(s):  
Seyyed Arash Haghpanah ◽  
Morteza Farrokhnia ◽  
Sajjad Taghvaei ◽  
Mohammad Eghtesad ◽  
Esmaeal Ghavanloo
Keyword(s):  
Electrical Stimulation ◽  
Ankle Joint ◽  
Functional Electrical Stimulation ◽  
Gait Cycle ◽  
Sliding Mode ◽  
Musculoskeletal Model ◽  
Pd Controller ◽  
Model Errors ◽  
Nonlinear Method ◽  
Cycle Simulation

Functional electrical stimulation (FES) is an effective method to induce muscle contraction and to improve movements in individuals with injured central nervous system. In order to develop the FES systems for an individual with gait impairment, an appropriate control strategy must be designed to accurate tracking performance. The goal of this study is to present a method for designing proportional-derivative (PD) and sliding mode controllers (SMC) for the FES applied to the musculoskeletal model of an ankle joint to track the desired movements obtained by experiments on two healthy individuals during the gait cycle. Simulation results of the developed controller on musculoskeletal model of the ankle joint illustrated that the SMC is able to track the desired movements more accurately than the PD controller and prevents oscillating patterns around the experimentally measured data. Therefore, the sliding mode as the nonlinear method is more robust in face to unmodeled dynamics and model errors and track the desired path smoothly. Also, the required control effort is smoother in SMC with respect to the PD controller because of the nonlinearity.

IMU-Based Gait Phase Recognition for Stroke Survivors

Robotica ◽  
2019 ◽  
Vol 37 (12) ◽  
pp. 2195-2208 ◽  
Author(s):  
Yu Lou ◽  
Rongli Wang ◽  
Jingeng Mai ◽  
Ninghua Wang ◽  
Qining Wang
Keyword(s):  
Inertial Measurement Unit ◽  
Detection System ◽  
Effective Means ◽  
Human Motion ◽  
Measurement Unit ◽  
Stroke Survivors ◽  
Phase Detection ◽  
Wearable Robots ◽  
Gait Phase ◽  
Phase Recognition

SummaryUsing wearable robots is an effective means of rehabilitation for stroke survivors, and effective recognition of human motion intentions is a key premise in controlling wearable robots. In this paper, we propose an inertial measurement unit (IMU)-based gait phase detection system. The system consists of two IMUs that are tied on the thigh and on the shank, respectively, for collecting acceleration and angular velocity. Features were extracted using a sliding window of 150 ms in length, which was then fed into a quadratic discriminant analysis (QDA) classifier for classification. We recruited five stroke survivors to test our system. They walked at their own preferred speed on the level ground. Experimental results show that our proposed system has the ability of recognizing the gait phase of stroke survivors. All recognition accuracy results are above 96.5%, and detections are about 5–15 ms in advance of time. In addition, using only one IMU can also give reliable recognition results. This paper proposes an idea about the further research on human–computer interaction for the control of wearable robots.

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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