Estimation of 3D Ground Reaction Force Using Nanocomposite Piezo-Responsive Foam Sensors During Walking

Rosquist, Parker; Collins, Gavin; Merrell, A. Jake; Tuttle, Noelle J.; Tracy, James B.; Bird, Evan T.; Seeley, Matthew K.; Fullwood, David T.; Christensen, William F.; Bowden, Anton E.

doi:10.1007/s10439-017-1852-2

Cited by 32 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, the MAE increases if the personalized model was not adopted. [ 161 ] All these works of obtaining the gait parameters through direct and indirect calculation are summarized in Table 4 .…”

Section: The Algorithm Of Plantar Pressure Distributionmentioning

confidence: 99%

Plantar Pressure‐Based Insole Gait Monitoring Techniques for Diseases Monitoring and Analysis: A Review

Chen

Dai

Grimaldi

et al. 2021

Adv Materials Technologies

View full text Add to dashboard Cite

Among diverse wearable techniques, insole‐based plantar pressure monitoring systems have surged as a leading technology to monitor patient's chronic disease progression. Such technological feat has been made possible due to the strong correlation between gait and disease status. Hence, insole‐based plantar pressure monitoring techniques are growing rapidly worldwide; with several research institutions and enterprises showing an increased interest in the field. This review intends to first explain the working principles of mainstream insole plantar sensing techniques and design considerations such as sensing material selection and electronics design requirements, and then the state‐of‐the‐art algorithms for plantar pressure distribution reconstruction. Following, this article will discuss disease monitoring applications and the extraction of disease features. Finally, insight regarding common challenges and their potential solutions within the field would be elucidated.

show abstract

Section: The Algorithm Of Plantar Pressure Distributionmentioning

confidence: 99%

Plantar Pressure‐Based Insole Gait Monitoring Techniques for Diseases Monitoring and Analysis: A Review

Chen

Dai

Grimaldi

et al. 2021

Adv Materials Technologies

View full text Add to dashboard Cite

show abstract

“…However, due to partial GRF collection, both methods result in significant weight estimation errors, especially when the subject is moving. One promising approach is that of [32] where a nanocomposite piezo-responsive material is proposed as a sensing medium integrated under the shoe insole for 3D GRF measurement during walking. While the accuracy results were comparable to other total GRF measurement systems such as [28], system-level issues such as sensor synchronization were challenging to overcome.…”

Section: Review Of Prior Artmentioning

confidence: 99%

Shoe-Integrated, Force Sensor Design for Continuous Body Weight Monitoring

Muzaffar

Elfadel

2020

Sensors

View full text Add to dashboard Cite

Traditional pedobarography methods use direct force sensor placement in the shoe insole to record pressure patterns. One problem with such methods is that they tap only a few points on the flat sole under the foot and, therefore, do not account for the total ground reaction force. As a result, body weight tends to be under-estimated. This disadvantage has made it more difficult for pedobarography to be used to monitor many diseases, especially when their symptoms include body weight changes. In this paper, the problem of pedobarographic body weight measurement is addressed using a novel ergonomic shoe-integrated sensor array architecture based on concentrating the applied force via three-layered structures that we call Sandwiched Sensor Force Consolidators (SSFC). A shoe prototype is designed with the proposed sensors and shown to accurately measure body weight with an achievable relative accuracy greater than 99%, even in the presence of motion. The achieved relative accuracy is at least 4X better than the existing state of the art. The SSFC shoe prototype is built using readily available soccer shoes and piezoresistive FlexiForce sensors. To improve the wearability and comfort of the instrumented shoe, a semi-computational sensor design methodology is developed based on an equivalent-area concept that can accurately account for SSFC’s with arbitrary shapes. The search space of the optimal SSFC design is shown to be combinatorial, and a high-performance computing (HPC) framework based on OpenMP parallel programming is proposed to accelerate the design optimization process. An optimal sensor design speedup of up to 22X is shown to be achievable using the HPC implementation.

show abstract

“…However, the exoskeletal application of this method is limited due to the fact that this type of sensors are highly noisy and inaccurate. [50]. Also, there are other studies that only estimated vertical GRF which may not be enough information for closed-loop control system design of many exoskeletons.…”

Section: Ground Reaction Forces and Moments Estimationmentioning

confidence: 99%

Motion Generation of a Wearable Hip Exoskeleton Robot Using Machine Learning-Based Estimation of Ground Reaction Forces and Moments

Mahdavian

Arzanpour

Park

2019

2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

View full text Add to dashboard Cite

Statistical data acquired from US citizens in 2013 showed that the overall percentage of all disabilities for all ages in this country was around 12.6%, in which the "ambulatory disabilities" had the highest prevalence rate (7.1 %) [1]. This amount is estimated around 7.2% for all Canadian adults, which corresponds to more than 2.5 million people [2]. In order to improve the quality of life of those with ambulatory disabilities (e.g., paraplegic people), wearable robotic exoskeleton is being developed in our lab.In this project, Ground Reaction Forces and Moments (GRF/M), which are important data for closed-loop control of an exoskeleton, is estimated based on lower limb motion of a wearable hip exoskeleton user. This method can reduce manufacturing cost and design complications of these types of robots. In order to model GRF/M, Neural Network, Random Forest and Support Vector Machine algorithms are utilized. Afterward, the achieved results from the three algorithms are compared with each other and some of the most recent similar studies. In the next step, the trained models are employed in an online control loop for assisting a healthy exoskeleton user to walk easier. The device applies forces on the user's upper thigh, which reduces the required torque of the hip flexion-extension joint for the user. Finally, the exoskeleton's performance is compared experimentally with the cases when the device is not powered or it is simply following the user's motion based on the inverse kinematics. The results showed the presented algorithm can help the exoskeleton user to walk easier.

show abstract

Estimation of 3D Ground Reaction Force Using Nanocomposite Piezo-Responsive Foam Sensors During Walking

Cited by 32 publications

References 31 publications

Plantar Pressure‐Based Insole Gait Monitoring Techniques for Diseases Monitoring and Analysis: A Review

Plantar Pressure‐Based Insole Gait Monitoring Techniques for Diseases Monitoring and Analysis: A Review

Shoe-Integrated, Force Sensor Design for Continuous Body Weight Monitoring

Motion Generation of a Wearable Hip Exoskeleton Robot Using Machine Learning-Based Estimation of Ground Reaction Forces and Moments

Contact Info

Product

Resources

About