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

Adaptive Functional Electrical Stimulation Delivers Stimulation Amplitudes Based on Real-Time Gait Biomechanics

Functional electrical stimulation (FES) is often used in poststroke gait rehabilitation to decrease foot drop and increase forward propulsion. However, not all stroke survivors experience clinically meaningful improvements in gait function following training with FES. The purpose of this work was to develop and validate a novel adaptive FES (AFES) system to improve dorsiflexor (DF) and plantarflexor (PF) stimulation timing and iteratively adjust the stimulation amplitude at each stride based on measured gait biomechanics. Stimulation timing was determined by a series of bilateral footswitches. Stimulation amplitude was calculated based on measured dorsiflexion angle and peak propulsive force, where increased foot drop and decreased paretic propulsion resulted in increased stimulation amplitudes. Ten individuals with chronic poststroke hemiparesis walked on an adaptive treadmill with adaptive FES for three 2-min trials. Stimulation was delivered at the correct time to the dorsiflexor muscles during 95% of strides while stimulation was delivered to the plantarflexor muscles at the correct time during 84% of strides. Stimulation amplitudes were correctly calculated and delivered for all except two strides out of nearly 3000. The adaptive FES system responds to real-time gait biomechanics as intended, and further individualization to subject-specific impairments and rehabilitation goals may lead to improved rehabilitation outcomes.

Commercially available pressure sensors for sport and health applications: A comparative review

Pressure measurement systems have numerous applications in healthcare and sport. The purpose of this review is to: (a) describe the brief history of the development of pressure sensors for clinical and sport applications, (b) discuss the design requirements for pressure measurement systems for different applications, (c) critique the suitability, reliability, and validity of commercial pressure measurement systems, and (d) suggest future directions for the development of pressure measurements systems in this area. Commercial pressure measurement systems generally use capacitive or resistive sensors, and typically capacitive sensors have been reported to be more valid and reliable than resistive sensors for prolonged use. It is important to acknowledge, however, that the selection of sensors is contingent upon the specific application requirements. Recent improvements in sensor and wireless technology and computational power have resulted in systems that have higher sensor density and sampling frequency with improved usability - thinner, lighter platforms, some of which are wireless, and reduced the obtrusiveness of in-shoe systems due to wireless data transmission and smaller data-logger and control units. Future developments of pressure sensors should focus on the design of systems that can measure or accurately predict shear stresses in conjunction with pressure, as it is thought the combination of both contributes to the development of pressure ulcers and diabetic plantar ulcers. The focus for the development of in-shoe pressure measurement systems is to minimise any potential interference to the patient or athlete, and to reduce power consumption of the wireless systems to improve the battery life, so these systems can be used to monitor daily activity. A potential solution to reduce the obtrusiveness of in-shoe systems include thin flexible pressure sensors which can be incorporated into socks. Although some experimental systems are available further work is needed to improve their validity and reliability.

Wearable Nanocomposite Sensor System for Motion Phenotyping Chronic Low Back Pain: A BACPAC Technology Research Site

Chronic low back pain (cLBP) is a prevalent and multifactorial ailment. No single treatment has been shown to dramatically improve outcomes for all cLBP patients, and current techniques of linking a patient with their most effective treatment lack validation. It has long been recognized that spinal pathology alters motion. Therefore, one potential method to identify optimal treatments is to evaluate patient movement patterns (ie, motion-based phenotypes). Biomechanists, physical therapists, and surgeons each utilize a variety of tools and techniques to qualitatively assess movement as a critical element in their treatment paradigms. However, objectively characterizing and communicating this information is challenging due to the lack of economical, objective, and accurate clinical tools. In response to that need, we have developed a wearable array of nanocomposite stretch sensors that accurately capture the lumbar spinal kinematics, the SPINE Sense System. Data collected from this device are used to identify movement-based phenotypes and analyze correlations between spinal kinematics and patient-reported outcomes. The purpose of this paper is twofold: first, to describe the design and validity of the SPINE Sense System; and second, to describe the protocol and data analysis toward the application of this equipment to enhance understanding of the relationship between spinal movement patterns and patient metrics, which will facilitate the identification of optimal treatment paradigms for cLBP.

Dual-Sensing Piezoresponsive Foam for Dynamic and Static Loading

Polymeric foams, embedded with nano-scale conductive particles, have previously been shown to display quasi-piezoelectric (QPE) properties; i.e., they produce a voltage in response to rapid deformation. This behavior has been utilized to sense impact and vibration in foam components, such as in sports padding and vibration-isolating pads. However, a detailed characterization of the sensing behavior has not been undertaken. Furthermore, the potential for sensing quasi-static deformation in the same material has not been explored. This paper provides new insights into these self-sensing foams by characterizing voltage response vs frequency of deformation. The correlation between temperature and voltage response is also quantified. Furthermore, a new sensing functionality is observed, in the form of a piezoresistive response to quasi-static deformation. The piezoresistive characteristics are quantified for both in-plane and through-thickness resistance configurations. The new functionality greatly enhances the potential applications for the foam, for example, as insoles that can characterize ground reaction force and pressure during dynamic and/or quasi-static circumstances, or as seat cushioning that can sense pressure and impact.

A Low-Cost Three-Axis Force Sensor for Wearable Gait Analysis Systems

This paper presents the design, analysis, and fabrication of a capacitive-based three-axis force sensor as the building block of a wearable sensing system to directly measure all the components of three-dimensional (3D) ground reaction forces (3D GRFs) during walking. The proposed sensor is low-cost and easy to fabricate with high accuracy, which promotes its accessibility and usability for gait analysis in clinical and research settings. The sensor is comprised of only three parallel capacitors that enable three-axial force measurement while significantly reducing the complexity of fabrication and maintenance prevalent in three-axis force sensors. Comprehensive experiments were conducted to rigorously quantify different aspects of the sensor's performance. The static and dynamic errors along the three axes are less than 2.28%, which is well within the acceptable range for the intended application. The force sensor can decouple three-axial forces with a cross-sensitivity of less than 2%. The developed sensor also demonstrates desirable repeatability and hysteresis behaviors with almost no drift over long periods of usage.

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

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.

Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models

Wearable sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments. However, the moderate accuracy of these systems currently limits their widespread use. In this paper, we show that support vector regression (SVR) models can be used to extract accurate estimates of fundamental gait parameters (i.e., stride length, velocity, and foot clearance), from custom-engineered instrumented insoles (SportSole) during walking and running tasks. Additionally, these learning-based models are robust to inter-subject variability, thereby making it unnecessary to collect subject-specific training data. Gait analysis was performed in N=14 healthy subjects during two separate sessions, each including 6-minute bouts of treadmill walking and running at different speeds (i.e., 85% and 115% of each subject's preferred speed). Gait metrics were simultaneously measured with the instrumented insoles and with reference laboratory equipment. SVR models yielded excellent intraclass correlation coefficients (ICC) in all the gait parameters analyzed. Percentage mean absolute errors (MAE%) in stride length, velocity, and foot clearance obtained with SVR models were 1.37%±0.49%, 1.23%±0.27%, and 2.08%±0.72% for walking, 2.59%±0.64%, 2.91%±0.85%, and 5.13%±1.52% for running, respectively. These findings provide evidence that machine learning regression is a promising new approach to improve the accuracy of wearable sensors for gait analysis.

Estimation of Vertical Ground Reaction Force Using Low-Cost Insole With Force Plate-Free Learning From Single Leg Stance and Walking

For the evaluation of pathological gait, a machine learning-based estimation of the vertical ground reaction force (vGRF) using a low-cost insole is proposed as an alternative to costly force plates. However, learning a model for estimation still relies on the use of force plates, which is not accessible in small clinics and individuals. Therefore, this paper presents a force plate-free learning from a single leg stance (SLS) and natural walking measured only by the insoles. This method used a linear least squares regression that fits insole measurements during SLS to body weight in order to learn a model to estimate vGRF during walking. Constraints were added to the regression so that vGRF estimates during walking were of proper magnitude, and the constraint bounds were newly defined as a linear function of stance duration. Moreover, a lower bound for the estimated vGRF in mid-stance was added to the constraints to enhance estimation accuracy. The vGRF estimated by the proposed method was compared with force platforms for 4 healthy young adults and 13 elderly adults including patients with mild osteoarthritis, knee pain, and valgus hallux. Through the experiments, the proposed learning method had a normalized root mean squared error under 10% for healthy young and elderly adults with stance durations within a certain range (600-800 ms). From these results, the validity of the proposed learning method was verified for various users requiring assessment in the field of medicine and healthcare.

Insole-Based Estimation of Vertical Ground Reaction Force Using One-Step Learning With Probabilistic Regression and Data Augmentation

An insole-based estimation of the vertical ground reaction force (vGRF) is proposed as an alternative to costly force plates for the evaluation of pathological gait. However, machine learning techniques for estimation still rely on the use of force plates. Moreover, measuring plural walking steps in order to prevent overfitting induces fall risks and physically taxes the patients. Therefore, this paper presents an accessible and efficient learning scheme for the insole-based estimation of vGRF. In this system, we employ a low-cost scale as an alternative to force plates. Then, we use Gaussian process regression (GPR) to learn a model in order to estimate vGRF without overfitting of small-sized data sets corrupted by measurement errors and noise of the devices. In addition, we propose a "one-step learning" scheme based on a probabilistic data augmentation. This approach augments actual measurements of a minimum (just one) walking step to a virtual data set for plural steps by considering their typical variability between steps. In experiments, the GPR models learned from two walking steps estimated vGRF with mean errors of 8% or under for entire/local magnitudes. Moreover, the learning from one step with probabilistic augmentation enhanced the estimation accuracy.

Motion-Based Prediction of Hands and Feet Contact Efforts During Asymmetric Handling Tasks

This paper proposes a method to predict the external efforts exerted on a subject during handling tasks, only with a measure of his motion. These efforts are the contacts forces and moments on the ground and on the load carried by the subject. The method is based on a contact model initially developed to predict the ground reaction forces and moments. Discrete contact points are defined on the biomechanical model at the feet and the hands. An optimization technique computes the minimal forces at each of these points, satisfying the dynamic equations of the biomechanical model and the load. The method was tested on a set of asymmetric handling tasks performed by 13 subjects and validated using force platforms and an instrumented load. For each task, predictions of the vertical forces obtained an RMSE of about 0.25 N/kg for the feet contacts and below 1 N/kg for the hand contacts. L5/S1 joint moments were then computed using the predicted and the measured data. RMSE of 18 Nm and rRMSE below 10% were obtained for the flexion/extension component. In conclusion, this method enables to quantitatively assess asymmetric handling tasks on the basis of kinetics variables without additional instrumentation, such as force sensors, and thus improve the ecological aspect of the studied tasks. This method has a great potential to be applied in work tasks analyses in ergonomics studies or sports gestures analyses involving hand contacts in exercise science.

Functional Data Analyses of Gait Data Measured Using In-Shoe Sensors

In studies of gait, continuous measurement of force exerted by the ground on a body, or ground reaction force (GRF), provides valuable insights into biomechanics, locomotion, and the possible presence of pathology. However, gold-standard measurement of GRF requires a costly in-lab observation obtained with sophisticated equipment and computer systems. Recently, in-shoe sensors have been pursued as a relatively inexpensive alternative to in-lab measurement. In this study, we explore the properties of continuous in-shoe sensor recordings using a functional data analysis approach. Our case study is based on measurements of three healthy subjects, with more than 300 stances (defined as the period between the foot striking and lifting from the ground) per subject. The sensor data show both phase and amplitude variabilities; we separate these sources via curve registration. We examine the correlation of phase shifts across sensors within a stance to evaluate the pattern of phase variability shared across sensors. Using the registered curves, we explore possible associations between in-shoe sensor recordings and GRF measurements to evaluate the in-shoe sensor recordings as a possible surrogate for in-lab GRF measurements.

Lesser lower extremity mechanical loading associates with a greater increase in serum cartilage oligomeric matrix protein following walking in individuals with anterior cruciate ligament reconstruction

BACKGROUND

Aberrant mechanical loading during gait is hypothesized to contribute to the development of posttraumatic osteoarthritis following anterior cruciate ligament reconstruction. Our purpose was to determine if peak vertical ground reaction force and instantaneous vertical ground reaction force loading rate associate with the acute change in serum cartilage oligomeric matrix protein following a 20-minute bout of walking.

METHODS

We enrolled thirty individuals with a unilateral anterior cruciate ligament reconstruction. Peak vertical ground reaction force and instantaneous vertical ground reaction force loading rate were extracted from the first 50% of the stance phase of gait during a 60-second trial. Blood samples were collected immediately before and after 20 min of treadmill walking at self-selected speed. The change in serum cartilage oligomeric matrix protein from pre- to post-walking was calculated. Stepwise linear regression models were used to determine the association between each outcome of loading and the change in serum cartilage oligomeric matrix protein after accounting for sex, gait speed, time since anterior cruciate ligament reconstruction, graft type, and history of concomitant meniscal procedure (ΔR²).

FINDINGS

Lesser peak vertical ground reaction force (ΔR² = 0.208; β = -0.561; P = 0.019) and instantaneous vertical ground reaction force loading rate (ΔR² = 0.168; β = -0.519; P = 0.037) on the anterior cruciate ligament reconstructed limb associated with a greater increase in serum cartilage oligomeric matrix protein following 20 min of walking.

INTERPRETATION

Mechanical loading may be a future therapeutic target for altering the acute biochemical response to walking in individuals with an anterior cruciate ligament reconstruction.

Walking gait asymmetries 6 months following anterior cruciate ligament reconstruction predict 12-month patient-reported outcomes

The study sought to determine the association between gait biomechanics (vertical ground reaction force [vGRF], vGRF loading rate [vGRF-LR]) collected 6 months following anterior cruciate ligament reconstruction (ACLR) with patient-reported outcomes at 12 months following ACLR. Walking gait biomechanics and all subsections of the Knee Injury and Osteoarthritis Outcomes Score (KOOS) were collected at 6 and 12 months following ACLR, respectively, in 25 individuals with a unilateral ACLR. Peak vGRF and peak instantaneous vGRF-LR were extracted from the first 50% of the stance phase. Limb symmetry indices (LSI) were used to normalize outcomes in the ACLR limb to that of the uninjured limb (ACLR/uninjured). Linear regression analyses were used to determine associations between biomechanical outcomes and KOOS while accounting for walking speed. Receiver operator characteristic curves were used to determine the accuracy of 6-month biomechanical outcomes for identifying individuals with acceptable patient-reported outcomes, using previously defined KOOS cut-off scores, 12 months post-ACLR. Individuals with lower peak vGRF LSI 6 months post-ACLR demonstrated worse patient-reported outcomes (KOOS Pain, Activities of Daily life, Sport and Recreation, Quality of Life) at the 12-month exam. A peak vGRF LSI ≥0.99 6 months following ACLR associated with 13.33× higher odds of reporting acceptable patient-reported outcomes 12 months post-ACLR. Lesser peak vGRF LSI during walking at 6-months post-ACLR may be a critical indicator of worse future patient-reported outcomes. Clinical significance achieving early symmetrical lower extremity loading and minimizing under-loading of the ACLR limb during walking may be a potential therapeutic target for improving patient-reported outcomes post-ACLR. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2932-2940, 2018.