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

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.

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

10.21203/rs.3.rs-40472/v1 ◽

2020 ◽

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.

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

10.21203/rs.3.rs-40472/v2 ◽

2020 ◽

Author(s):

Jie Kai Er ◽

Cyril John William Donnelly ◽

Seng Kwee Wee ◽

Wei Tech Ang

Keyword(s):

Real World ◽

Muscle Activation ◽

Repeated Measure ◽

Future Research ◽

Joint Forces ◽

In Series ◽

Gait Phase ◽

Movable Platform ◽

Movement System ◽

Fall Recovery

Abstract Background: The study of falls and fall prevention/intervention devices requires the recording of true fallsincidence. However, true falls are rare, random, and difficult to collect in real world settings. A system capableof producing falls in an ecologically valid manner will be very helpful in collecting the data necessary toadvance 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 toinduce a trip or slip. The ankle was arrested posteriorly with an electromagnetic brake and acceleratedanteriorly with a motor. A power spring was connected in series between the ankle and the brake/motor toallow freedom of movement (system transparency) when a fall is not being induced. A gait phase detectionalgorithm was also created to enable precise activation of the fall inducing mechanisms. Statistical ParametricMapping (SPM1D) and one-way repeated measure ANOVA were used to evaluate the ability of the FIMP toinduce a trip or slip.Results: During FIMP induced trips, the brake activates at the terminal swing or mid swing gait phase toinduce the lowering or skipping strategies, respectively. For the lowering strategy, the characteristic leg loweringand subsequent contralateral leg swing was seen in all subjects. Likewise, for the skipping strategy, all subjectsskipped forward on the perturbed leg.Slip was induced by FIMP by using a motor to impart unwanted forward acceleration to the ankle with thehelp of friction-reducing ground sliding sheets. Joint stiffening was observed during the slips, and subjectsuniversally adopted the surfing strategy after the initial slip.Conclusion: The results indicate that FIMP can induce ecologically valid falls under controlled laboratoryconditions. The use of SPM1D in conjunction with FIMP allows for the time varying statistical quanticationof trip and slip reactive kinematics events. With future research, fall recovery anomalies in subjects can nowalso be systematically evaluated through the assessment of other neuromuscular variables such as joint forces,muscle activation and muscle forces.

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

10.21203/rs.2.21449/v1 ◽

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

Sensors ◽

10.3390/s21041081 ◽

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.

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IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking With Cognitive Tasks

IEEE Access ◽

10.1109/access.2019.2915290 ◽

2019 ◽

Vol 7 ◽

pp. 68240-68249 ◽

Cited By ~ 5

Author(s):

Fang Peng ◽

Wei Peng ◽

Cheng Zhang ◽

Debao Zhong

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Detection Algorithm ◽

Cognitive Tasks ◽

Phase Detection ◽

Linear Discriminant ◽

Gait Phase

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A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Sensors ◽

10.3390/s20143972 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3972

Author(s):

Huong Thi Thu Vu ◽

Dianbiao Dong ◽

Hoang-Long Cao ◽

Tom Verstraten ◽

Dirk Lefeber ◽

...

Keyword(s):

Lower Limb ◽

Detection Algorithm ◽

Detection Methods ◽

Phase Detection ◽

Prosthetic Devices ◽

Detection Algorithms ◽

Gait Phase ◽

Robotic Devices ◽

Functional Gait ◽

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.

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

2009 International Conference of Soft Computing and Pattern Recognition ◽

10.1109/socpar.2009.116 ◽

2009 ◽

Cited By ~ 2

Author(s):

C. Senanayake ◽

S. M. N. Arosha Senanayake

Keyword(s):

Fuzzy Logic ◽

Real Time ◽

Detection Algorithm ◽

Phase Detection ◽

Gait Phase ◽

Kinematical Parameters

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

Sensors ◽

10.3390/s19112471 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2471 ◽

Cited By ~ 7

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.

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