scholarly journals Real-Time Identification of Knee Joint Walking Gait as Preliminary Signal for Developing Lower Limb Exoskeleton

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2117
Author(s):  
Susanto Susanto ◽  
Ipensius Tua Simorangkir ◽  
Riska Analia ◽  
Daniel Sutopo Pamungkas ◽  
Hendawan Soebhakti ◽  
...  
Keyword(s):  
Knee Joint ◽  
Real Time ◽  
Gait Cycle ◽  
Initial Step ◽  
Knee Joints ◽  
Walking Gait ◽  
Lower Limb Exoskeleton ◽  
Walking Rehabilitation ◽  
Research Experiment ◽  
Real Time Identification

An exoskeleton is a device used for walking rehabilitation. In order to develop a proper rehabilitation exoskeleton, a user’s walking intention needs to be captured as the initial step of work. Moreover, every human has a unique walking gait style. This work introduced a wearable sensor, which aimed to recognize the walking gait phase, as the fundamental step before applying it into the rehabilitation exoskeleton. The sensor used in this work was the IMU sensor, used to recognize the pitch angle generated from the knee joint while the user walks, as information about the walking gait cycle, before doing the investigation on how to identify the walking gait cycle. In order to identify the walking gait cycle, Neural Network has been proposed as a method. The gait cycle identification was generated to recognize the gait cycle on the knee joint. To verify the performance of the proposed method, experiments have been done in real-time application. The experiments were carried out with different processes such as walking on a flat floor, climbing up, and walking down stairs. Five subjects were trained and tested using the system. The experiments showed that the proposed method was able to recognize each gait cycle for all users as they wore the sensor on their knee joints. This study has the potential to be applied on an exoskeleton rehabilitation robot as a further research experiment.

Design and Evaluation of a Knee Actuator for a Dynamic Knee-Ankle-Foot Orthosis

2014 ◽  
Author(s):  
Feng Tian ◽  
Mohammad Elahinia ◽  
Mohamed Samir Hefzy
Keyword(s):  
Knee Joint ◽  
Gait Cycle ◽  
Gait Pattern ◽  
Walking Ability ◽  
Hydraulic Systems ◽  
Walking Gait ◽  
Normal Knee ◽  
Actuation System ◽  
Normal Gait ◽  
In Series

Dynamic KAFOs are developed to recover the normal walking ability during both stance and swing phases. Three types of dynamic KAFOs have been reported in the literature. Various actuation mechanisms including spring, pneumatic and hydraulic systems have been used. These devices can improve walking disability and compensate lower leg muscle deficiency. However, they are bulky, in some cases need complex control systems and do not recreate the normal gait pattern. These shortcomings have limited the application of dynamic KAFOs in daily life. The purpose of this paper is to develop a novel knee actuator for a dynamic KAFO that is actuated easily by employing shape memory materials. Such an actuation system makes the KAFO lightweight and has a greater potential to restore the normal gait. Torsional superelastic alloys are used in this actuator in order to match the stiffness of the knee joint of the KAFO with that of a normal knee joint during the walking gait cycle. There are two distinct parts in the knee actuator, acting independently to mimic the two phases of the gait cycle. One engages only in the stance phase and the other works in the swing phase. Each part is developed by combining a superelastic rod and a stiff rotary spring, in series. According to numerical simulation, such combination reproduces the varying knee stiffness during the whole walking gait. Also mechanical experiments have been conducted to further verify the conceptual design. The simulation and experimental results show that the actuator is able to reproduce the stiffness of the normal knee joint during the gait cycle.

A Biologically Inspired Knee Actuator for a KAFO

2016 ◽  
Vol 10 (4) ◽  
Author(s):  
Feng Tian ◽  
Mohamed Samir Hefzy ◽  
Mohammad Elahinia
Keyword(s):  
Knee Joint ◽  
Knee Flexion ◽  
Gait Cycle ◽  
Knee Extension ◽  
Lateral Side ◽  
Walking Gait ◽  
Normal Knee ◽  
Knee Stiffness ◽  
Control Knee ◽  
Stiffness Profile

Knee–ankle–foot orthoses (KAFOs) are prescribed to improve abnormal ambulation caused by quadriceps weakness. There are three major types of KAFOs: passive KAFOs, semidynamic KAFOs, and dynamic KAFOs. Dynamic KAFOs are the only type that enables to control knee motions throughout the entire walking gait cycle. However, those available in the market are heavy, bulky, and have limited functionality. The UT dynamic KAFO is developed to allow knee flexion and assist knee extension over the gait cycle by using a superelastic nitinol actuator, which has the potential to reduce volume and weight and reproduce normal knee behavior. In order to match the normal knee stiffness profile, the dynamic actuator consists of two actuating parts that work in the stance and swing phases, respectively. Each actuating part combines a superelastic torsional rod and a torsional spring in parallel. Geometries of the two superelastic rods were determined by matlab-based numerical simulations. The simulation response of the dynamic actuator was compared with the normal knee stiffness, verifying that the proposed design is able to mimic the normal knee performance. The surrounding parts of the dynamic knee joint have then been designed and modeled to house the two actuating parts. The dynamic knee joint was fabricated and mounted on a conventional passive KAFO, replacing its original knee joint on the lateral side. Motion analysis tests were conducted on a healthy subject to evaluate the feasibility of the UT dynamic KAFO. The results indicate that the UT dynamic KAFO allows knee flexion during the swing phase of gait and provides knee motion close to normal.

scholarly journals Use of the Human Walking Gait Cycle for Assistive Torque Generation for the Hip Joint Exoskeleton

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Riska Analia ◽  
Jan Hong ◽  
Joshua Mangkey ◽  
Susanto ◽  
Daniel Pamungkas ◽  
...  
Keyword(s):  
Hip Joint ◽  
Initial Phase ◽  
Gait Cycle ◽  
Reference Signal ◽  
Human Gait ◽  
Human Beings ◽  
Walking Gait ◽  
Lower Limb Exoskeleton ◽  
Inertial Moment ◽  
Decision Tree Method

The development of an assistive robot to assist human beings in walking normally is a difficult task. One of the main challenges lies in understanding the intention to walk, as an initial phase before walking commences. In this work, we classify the human gait cycle based on data from an inertial moment unit sensor and information on the angle of the hip joint and use the results as initial signals to produce a suitable assistive torque for a lower limb exoskeleton. A neural network module is used as a prediction module to identify the intention to walk based on the gait cycle. A decision tree method is implemented in our system to generate the assistive torque, and a prediction of the human gait cycle is used as a reference signal. Real-time experiments are carried out to verify the performance of the proposed method, which can differentiate between various types of walking. The results show that the proposed method is able to predict the intention to walk as an initial phase and is also able to provide an assistive torque based on the information predicted for this phase.

Development of a Dynamic Knee Actuator for a KAFO Using Superelastic Alloys

2014 ◽  
Author(s):  
Feng Tian ◽  
Mohamed Samir Hefzy ◽  
Mohammad Elahinia
Keyword(s):  
Knee Joint ◽  
Gait Cycle ◽  
Swing Phase ◽  
Walking Ability ◽  
Knee Motion ◽  
Walking Gait ◽  
Ankle Foot Orthosis ◽  
Stance Control ◽  
In Series ◽  
Foot Orthosis

A knee-ankle-foot orthosis (KAFO), which covers the knee, ankle and foot, can mitigate abnormal walking pattern caused by weak quadriceps. Several types of KAFOs are currently available in the market: passive KAFOs, stance-control KAFOs and dynamic KAFOs. In passive KAFOs, the knee joint keeps being locked during standing and walking, and can be unlocked manually to allow free rotation for sitting. Stance-control KAFOs (SCKAFOs) allow free knee motion during swing phase when the braced leg is unloaded. Dynamic KAFOs are able to reproduce normal walking ability throughout whole gait cycle. This research is directed at using superelastic alloys to develop a dynamic knee actuator that can be mounted on a traditional passive KAFO. The actuator stiffness can match that of a normal knee joint during the walking gait cycle. This proposed knee actuator utilizes a storing-releasing energy method to apply functional compensation to the knee joint, controlling the knee joint during both stance and swing phases. Fundamentally, the knee actuator is composed of two distinct parts which are connected with the thigh and shank segments, respectively. There are two superelastic actuators that are housed within these two parts and activated independently. Each actuator is developed by combining a superelastic rod and a rotary spring in series. When neither actuator is engaged, the knee joint is allowed to rotate freely. The stance actuator works only in the stance phase and the swing actuator is active for the swing phase. The conceptual design of the knee actuator is verified using numerical simulation and a prototype is developed through additive manufacturing for confirming the concept.

Powering a Lower Limb Exoskeleton Using Pneumatic Artificial Muscles

2016 ◽  
Author(s):  
Jonathan M. Chambers ◽  
Craig R. Carignan ◽  
Norman M. Wereley
Keyword(s):  
Knee Joint ◽  
Lower Limb ◽  
Kinematic Model ◽  
Knee Joints ◽  
Transmission Model ◽  
Artificial Muscles ◽  
Lower Limb Exoskeleton ◽  
Actuation System ◽  
Pneumatic Artificial Muscles ◽  
Assembly Tasks

Passive leg exoskeletons are currently being investigated for offsetting the weight of tools and other loads from workers performing maintenance and assembly tasks. By providing power-assist to the knee joints with pneumatic artificial muscles (PAMs), a wider range of stances could be used by maintenance workers without drawing significant power. A simplified kinematic model of the exoskeleton is developed, and the array of potential user stance configurations is then bounded. A static analysis is performed to define the torque required for actuation of the knee joint to support the tool loads carried by the exoskeleton. Finally, an exemplary transmission model is used to verify that it is feasible for a PAM to provide the range of motion and forces required for knee joint actuation. Upon demonstration of the viability of PAM actuation, development of an exoskeleton leg prototype is underway to provide validation of the proposed scheme. The knee actuation system will be retrofit to the FORTIS exoskeleton, and tests on its effectiveness will be conducted.

Visualization of Cartilage from Knee Joint Magnetic Resonance Images and Quantitative Assessment to Study the Effect of Age, Gender and Body Mass Index (BMI) in Progressive Osteoarthritis (OA)

2019 ◽  
Vol 15 (6) ◽  
pp. 565-572
Author(s):  
Mallikarjunaswamy Shivagangadharaiah Matada ◽  
Mallikarjun Sayabanna Holi ◽  
Rajesh Raman ◽  
Sujana Theja Jayaramu Suvarna
Keyword(s):  
Body Mass Index ◽  
Magnetic Resonance ◽  
Knee Joint ◽  
Body Mass ◽  
3D Visualization ◽  
Cartilage Degradation ◽  
Magnetic Resonance Images ◽  
Cartilage Thickness ◽  
Knee Joints ◽  
Data Set

Background: Osteoarthritis (OA) is a degenerative disease of joint cartilage affecting the elderly people around the world. Visualization and quantification of cartilage is very much essential for the assessment of OA and rehabilitation of the affected people. Magnetic Resonance Imaging (MRI) is the most widely used imaging modality in the treatment of knee joint diseases. But there are many challenges in proper visualization and quantification of articular cartilage using MRI. Volume rendering and 3D visualization can provide an overview of anatomy and disease condition of knee joint. In this work, cartilage is segmented from knee joint MRI, visualized in 3D using Volume of Interest (VOI) approach. Methods: Visualization of cartilage helps in the assessment of cartilage degradation in diseased knee joints. Cartilage thickness and volume were quantified using image processing techniques in OA affected knee joints. Statistical analysis is carried out on processed data set consisting of 110 of knee joints which include male (56) and female (54) of normal (22) and different stages of OA (88). The differences in thickness and volume of cartilage were observed in cartilage in groups based on age, gender and BMI in normal and progressive OA knee joints. Results: The results show that size and volume of cartilage are found to be significantly low in OA as compared to normal knee joints. The cartilage thickness and volume is significantly low for people with age 50 years and above and Body Mass Index (BMI) equal and greater than 25. Cartilage volume correlates with the progression of the disease and can be used for the evaluation of the response to therapies. Conclusion: The developed methods can be used as helping tool in the assessment of cartilage degradation in OA affected knee joint patients and treatment planning.

scholarly journals Real-Time Identification of Patients Included in the CMS Bundled Payment Care Improvement (BPCI) Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s367-s368
Author(s):  
Michael Korvink ◽  
John Martin ◽  
Michael Long
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Care Providers ◽  
Meta Model ◽  
Bundled Payment ◽  
Clinical Cohort ◽  
Real Time Identification ◽  
Care Improvement

Background: The Bundled Payment Care Improvement Program is a CMS initiative designed to encourage greater collaboration across settings of care, especially as it relates to an initial set of targeted clinical episodes, which include sepsis and pneumonia. As with many CMS incentive programs, performance evaluation is retrospective in nature, resulting in after-the-fact changes in operational processes to improve both efficiency and quality. Although retrospective performance evaluation is informative, care providers would ideally identify a patient’s potential clinical cohort during the index stay and implement care management procedures as necessary to prevent or reduce the severity of the condition. The primary challenges for real-time identification of a patient’s clinical cohort are CMS-targeted cohorts are based on either MS-DRG (grouping of ICD-10 codes) or HCPCS coding—coding that occurs after discharge by clinical abstractors. Additionally, many informative data elements in the EHR lack standardization and no simple and reliable heuristic rules can be employed to meaningfully identify those cohorts without human review. Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay. Methods: The predictive model uses a combination of Bernoulli Naïve Bayes natural language processing (NLP) classifiers, to reduce text dimensionality into a single probability value, and an eXtreme Gradient Boosting (XGBoost) algorithm as a meta-model to collectively evaluate both standardized clinical elements alongside the NLP-based text probabilities. Results: Bernoulli Naïve Bayes classifiers have proven to perform well on short text strings and allow for highly explanatory unstructured or semistructured text fields (eg, reason for visit, culture results), to be used in a both comparative and generalizable way within the larger XGBoost model. Conclusions: The choice of XGBoost as the meta-model has the benefits of mitigating concerns of nonlinearity among clinical features, reducing potential of overfitting, while allowing missing values to exist within the data. Both the Bayesian classifier and meta-model were trained using a patient-level integrated dataset extracted from both a patient-billing and EHR data warehouse maintained by Premier. The data set, joined by patient admission-date, medical record number, date of birth, and hospital entity code, allows the presence of both the coded clinical cohort (derived from the MS-DRG) and the explanatory features in the EHR to exist within a single patient encounter record. The resulting model produced F1 performance scores of .65 for the sepsis population and .61 for the pneumonia population.Funding: NoneDisclosures: None

Sign in / Sign up

Export Citation Format

Share Document