scholarly journals Powered Knee and Ankle Prosthesis with Adaptive Control Enables Climbing Stairs with Different Stair Heights, Cadences, and Gait Patterns

2021 ◽  
Author(s):  
Sarah Hood ◽  
Lukas Gabert ◽  
Tommaso Lenzi
Keyword(s):  
Real World ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Ankle Prosthesis ◽  
Stair Ascent ◽  
Knee Position ◽  
Step Step ◽  
Step Over ◽  
Climbing Stairs ◽  
Torque Angle

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user’s thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

scholarly journals Powered Knee and Ankle Prosthesis with Adaptive Control Enables Climbing Stairs with Different Stair Heights, Cadences, and Gait Patterns

2020 ◽  
Author(s):  
Sarah Hood ◽  
Lukas Gabert ◽  
Tommaso Lenzi
Keyword(s):  
Real World ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Ankle Prosthesis ◽  
Stair Ascent ◽  
Knee Position ◽  
Step Step ◽  
Step Over ◽  
Climbing Stairs ◽  
Torque Angle

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user’s thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-step, similar to able-bodied individuals. We anticipate the proposed control strategy will improve the robustness of powered prostheses to environmental and human variance without the need for expert tuning, machine learning, or direct subject intervention, which may enable powered prostheses to more easily move from the lab to the real-world.

scholarly journals Powered Knee and Ankle Prosthesis with Adaptive Control Enables Climbing Stairs with Different Stair Heights, Cadences, and Gait Patterns

2020 ◽  
Author(s):  
Sarah Hood ◽  
Lukas Gabert ◽  
Tommaso Lenzi
Keyword(s):  
Real World ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Ankle Prosthesis ◽  
Stair Ascent ◽  
Knee Position ◽  
Step Step ◽  
Step Over ◽  
Climbing Stairs ◽  
Torque Angle

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user’s thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-step, similar to able-bodied individuals. We anticipate the proposed control strategy will improve the robustness of powered prostheses to environmental and human variance without the need for expert tuning, machine learning, or direct subject intervention, which may enable powered prostheses to more easily move from the lab to the real-world.

scholarly journals Powered Knee and Ankle Prosthesis with Adaptive Control Enables Climbing Stairs with Different Stair Heights, Cadences, and Gait Patterns

2021 ◽  
Author(s):  
Sarah Hood ◽  
Lukas Gabert ◽  
Tommaso Lenzi
Keyword(s):  
Real World ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Ankle Prosthesis ◽  
Stair Ascent ◽  
Knee Position ◽  
Step Step ◽  
Step Over ◽  
Climbing Stairs ◽  
Torque Angle

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user’s thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

Dynamic and Reactive Walking for Humanoid Robots Based on Foot Placement Control

2016 ◽  
Vol 13 (02) ◽  
pp. 1550041 ◽  
Author(s):  
Juan Alejandro Castano ◽  
Zhibin Li ◽  
Chengxu Zhou ◽  
Nikos Tsagarakis ◽  
Darwin Caldwell
Keyword(s):  
Center Of Mass ◽  
Humanoid Robots ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Foot Placement ◽  
Inverted Pendulum Model ◽  
Gait Parameters ◽  
Pendulum Model ◽  
Single Mass ◽  
Walking Velocity

This paper presents a novel online walking control that replans the gait pattern based on our proposed foot placement control using the actual center of mass (COM) state feedback. The analytic solution of foot placement is formulated based on the linear inverted pendulum model (LIPM) to recover the walking velocity and to reject external disturbances. The foot placement control predicts where and when to place the foothold in order to modulate the gait given the desired gait parameters. The zero moment point (ZMP) references and foot trajectories are replanned online according to the updated foothold prediction. Hence, only desired gait parameters are required instead of predefined or fixed gait patterns. Given the new ZMP references, the extended prediction self-adaptive control (EPSAC) approach to model predictive control (MPC) is used to minimize the ZMP response errors considering the acceleration constraints. Furthermore, to ensure smooth gait transitions, the conditions for the gait initiation and termination are also presented. The effectiveness of the presented gait control is validated by extensive disturbance rejection studies ranging from single mass simulation to a full body humanoid robot COMAN in a physics based simulator. The versatility is demonstrated by the control of reactive gaits as well as reactive stepping from standing posture. We present the data of the applied disturbances, the prediction of sagittal/lateral foot placements, the replanning of the foot/ZMP trajectories, and the COM responses.

scholarly journals Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

2021 ◽  
Vol 2 ◽  
Author(s):  
Anderson Antonio Carvalho Alves ◽  
Lucas Tassoni Andrietta ◽  
Rafael Zinni Lopes ◽  
Fernando Oliveira Bussiman ◽  
Fabyano Fonseca e Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Deep Learning ◽  
Audio Signal ◽  
Gait Pattern ◽  
Classification Performance ◽  
Audio Signal Processing ◽  
Gait Patterns ◽  
Audio Features ◽  
Gaited Horses

This study focused on assessing the usefulness of using audio signal processing in the gaited horse industry. A total of 196 short-time audio files (4 s) were collected from video recordings of Brazilian gaited horses. These files were converted into waveform signals (196 samples by 80,000 columns) and divided into training (N = 164) and validation (N = 32) datasets. Twelve single-valued audio features were initially extracted to summarize the training data according to the gait patterns (Marcha Batida—MB and Marcha Picada—MP). After preliminary analyses, high-dimensional arrays of the Mel Frequency Cepstral Coefficients (MFCC), Onset Strength (OS), and Tempogram (TEMP) were extracted and used as input information in the classification algorithms. A principal component analysis (PCA) was performed using the 12 single-valued features set and each audio-feature dataset—AFD (MFCC, OS, and TEMP) for prior data visualization. Machine learning (random forest, RF; support vector machine, SVM) and deep learning (multilayer perceptron neural networks, MLP; convolution neural networks, CNN) algorithms were used to classify the gait types. A five-fold cross-validation scheme with 10 repetitions was employed for assessing the models' predictive performance. The classification performance across models and AFD was also validated with independent observations. The models and AFD were compared based on the classification accuracy (ACC), specificity (SPEC), sensitivity (SEN), and area under the curve (AUC). In the logistic regression analysis, five out of the 12 audio features extracted were significant (p < 0.05) between the gait types. ACC averages ranged from 0.806 to 0.932 for MFCC, from 0.758 to 0.948 for OS and, from 0.936 to 0.968 for TEMP. Overall, the TEMP dataset provided the best classification accuracies for all models. The most suitable method for audio-based horse gait pattern classification was CNN. Both cross and independent validation schemes confirmed that high values of ACC, SPEC, SEN, and AUC are expected for yet-to-be-observed labels, except for MFCC-based models, in which clear overfitting was observed. Using audio-generated data for describing gait phenotypes in Brazilian horses is a promising approach, as the two gait patterns were correctly distinguished. The highest classification performance was achieved by combining CNN and the rhythmic-descriptive AFD.

Texting While Walking Induces Gait Pattern Alterations in Healthy Older Adults

2018 ◽  
Vol 62 (1) ◽  
pp. 1908-1912 ◽  
Author(s):  
Massimiliano Pau ◽  
Micaela Porta ◽  
Giuseppina Pilloni ◽  
Federica Corona ◽  
Maria Chiara Fastame ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Complexity ◽  
Gait Pattern ◽  
Middle Aged ◽  
Similar Reduction ◽  
Gait Patterns ◽  
Healthy Older Adults ◽  
Double Support ◽  
Temporal Parameters ◽  
Spatio Temporal

The use of a mobile phone for texting purposes results in distracted walking which may lead to injuries. In particular, texting while walking has been shown to induce significant alterations in gait patterns. This study aimed to assess whether changes in the main spatio-temporal parameters of gait when simultaneously engaged in texting on a smartphone and walking are different in older adults relative to young and middle- aged individuals. A total of 57 participants divided in three groups (19 older adults aged over 65, 19 young aged 20-40 and 19 middle-aged aged 41-64) were tested in two conditions: walking, and walking while texting on a smartphone. Spatio-temporal parameters of gait were assessed using a wearable accelerometer located on the lower back. The results show that texting induced similar reduction of gait speed, stride length and cadence in all groups. Slight (although significant) alterations of stance, swing and double support phases duration were found only for middle-aged participants. Such findings suggest that modifications of gait patterns due to texting seem unaffected by age, probably due to different perceptions of the cognitive complexity of the task and differential prioritization of its motor and cognitive aspects.

scholarly journals An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy

2020 ◽  
Vol 9 (5) ◽  
pp. 1432
Author(s):  
Julie Choisne ◽  
Nicolas Fourrier ◽  
Geoffrey Handsfield ◽  
Nada Signal ◽  
Denise Taylor ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Gait Pattern ◽  
Data Driven ◽  
Self Organizing Map ◽  
Gait Patterns ◽  
Gait Biomechanics ◽  
Children With Cerebral Palsy ◽  
Profile Score ◽  
Data Driven Modeling ◽  
Gait Variable

Ankle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children’s 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children’s gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription.

scholarly journals Powered knee and ankle prosthesis with indirect volitional swing control enables level-ground walking and crossing over obstacles

2020 ◽  
Vol 5 (44) ◽  
pp. eaba6635 ◽  
Author(s):  
Joel Mendez ◽  
Sarah Hood ◽  
Andy Gunnel ◽  
Tommaso Lenzi
Keyword(s):  
Real World ◽  
Gait Pattern ◽  
Crossing Over ◽  
Volitional Control ◽  
Foot Placement ◽  
Control Approach ◽  
Minimum Jerk ◽  
Foot Clearance ◽  
High Level

Powered prostheses aim to mimic the missing biological limb with controllers that are finely tuned to replicate the nominal gait pattern of non-amputee individuals. Unfortunately, this control approach poses a problem with real-world ambulation, which includes tasks such as crossing over obstacles, where the prosthesis trajectory must be modified to provide adequate foot clearance and ensure timely foot placement. Here, we show an indirect volitional control approach that enables prosthesis users to walk at different speeds while smoothly and continuously crossing over obstacles of different sizes without explicit classification of the environment. At the high level, the proposed controller relies on a heuristic algorithm to continuously change the maximum knee flexion angle and the swing duration in harmony with the user’s residual limb. At the low level, minimum-jerk planning is used to continuously adapt the swing trajectory while maximizing smoothness. Experiments with three individuals with above-knee amputation show that the proposed control approach allows for volitional control of foot clearance, which is necessary to negotiate environmental barriers. Our study suggests that a powered prosthesis controller with intrinsic, volitional adaptability may provide prosthesis users with functionality that is not currently available, facilitating real-world ambulation.

scholarly journals Individuals Poststroke Do Not Perceive Their Spatiotemporal Gait Asymmetries as Abnormal

2015 ◽  
Vol 95 (9) ◽  
pp. 1244-1253 ◽  
Author(s):  
Clinton J. Wutzke ◽  
Richard A. Faldowski ◽  
Michael D. Lewek
Keyword(s):  
Step Length ◽  
Gait Pattern ◽  
Conscious Awareness ◽  
Gait Patterns ◽  
Temporal Asymmetry ◽  
Gait Asymmetry ◽  
Stance Time ◽  
The Asymmetry ◽  
Asymmetric Gait

Background Following stroke, spatiotemporal gait asymmetries persist into the chronic phases, despite the neuromuscular capacity to produce symmetric walking patterns. This persistence of gait asymmetry may be due to deficits in perception, as the newly established asymmetric gait pattern is perceived as normal. Objective The purpose of this study was to determine the effect of usual overground gait asymmetry on the ability to consciously and unconsciously perceive the presence of gait asymmetry in people poststroke. Design An observational study was conducted. Methods Thirty people poststroke walked overground and on a split-belt treadmill with the belts moving at different speeds (0%–70% difference) to impose varied step length and stance time asymmetries. Conscious awareness and subconscious detection of imposed gait patterns were determined for each participant, and the asymmetry magnitudes at those points were compared with overground gait. Results For both spatial and temporal asymmetry variables, the asymmetry magnitude at the threshold of awareness was significantly greater than the asymmetry present at the threshold of detection or during overground gait. Participants appeared to identify belt speed differences using the type of gait asymmetry they typically exhibited (ie, step length or stance time asymmetries during overground gait). Limitations Very few individuals with severe spatiotemporal asymmetry were tested, and participants were instructed to identify asymmetric belt speeds rather than interlimb movements. Conclusions The data suggest that asymmetry magnitudes need to exceed usual overground levels to reach conscious awareness. Therefore, it is proposed that the spatiotemporal asymmetry that is specific to each participant may need to be augmented beyond what he or she usually has during walking in order to promote awareness of asymmetric gait patterns for long-term correction and learning.

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