Inertial sensing algorithms for long-term foot angle monitoring for assessment of idiopathic toe-walking

Measuring highly accurate foot position and angle trajectories with foot-mounted IMUs in clinical practice

BACKGROUND

Gait analysis using foot-mounted IMUs is a promising method to acquire gait parameters outside of laboratory settings and in everyday clinical practice. However, the need for precise sensor attachment or calibration, the requirement of environments with a homogeneous magnetic field, and the limited applicability to pathological gait patterns still pose challenges. Furthermore, in previously published work, the measurement accuracy of such systems is often only validated for specific points in time or in a single plane.

RESEARCH QUESTION

This study investigates the measurement accuracy of a gait analysis method based on foot-mounted IMUs in the acquisition of the foot motion, i.e., position and angle trajectories of the foot in the sagittal, frontal, and transversal plane over the entire gait cycle.

RESULTS

A comparison of the proposed method with an optical motion capture system showed an average RMSE of 0.67° for pitch, 0.63° for roll and 1.17° for yaw. For position trajectories, an average RMSE of 0.51 cm for vertical lift and 0.34 cm for lateral shift was found. The measurement error of the IMU-based method is found to be much smaller than the deviations caused by the shoes.

SIGNIFICANCE

The proposed method is found to be sufficiently accurate for clinical practice. It does not require precise mounting, special calibration movements, or magnetometer data, and shows no difference in measurement accuracy between normal and pathological gait. Therefore, it provides an easy-to-use alternative to optical motion capture and facilitates gait analysis independent of laboratory settings.

Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait

The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.

Early identification of toe walking gait in preschool children - Development and application of a quasi-automated video screening procedure

PURPOSE

To develop and test the application of a quasi-automated screening procedure identifying probable toe walking in a large population of preschool children.

METHODS

The proposed screening procedure was designed to identify children exhibiting signs of toe walking in a previously recruited cohort of preschool children (MiPS cohort). The procedure combines parent observation (step 1), objective parameters of foot contact during gait by an automated screening of 3-D video recordings (step 2), and clinical video screening of the children identified in step 1 and/or 2 (step 3).

FINDINGS

From 879 children, gait trials were obtained from 87% (n = 766). Step 1 (parent observation) identified 34 children with potential toe walking, step 2 (automated screening) 122. Fourteen were identified in both step 1 and 2. Thus, 142 children were selected for step 3 (clinical video screening), from which 41 children were classified as showing symmetric signs of toe walking, and five children were identified with asymmetrical signs of toe walking. Of the 41, five had been identified by step 1 only, 32 by step 2 only and four by both steps.

INTERPRETATION

Application of a quasi-automated screening algorithm was feasible and may assist in early detection of toe walking. Disagreements found between parent reported toe walking and video screening, indicate added value in quasi-automated video screening. However, thresholds of heel lift and clinical criteria of toe walking in the algorithm and video screening need to be addressed and validated to confidently identify toe walking gait.

PyTHang: an open-source wearable sensor system for real-time monitoring of head-torso angle for ambulatory applications

This article presents the realization of a low-cost wearable sensor system and its Python-based software that can measure and record relative head-torso angle, especially in sagittal plane. The system is mainly developed to track head-torso angle during walk in a clinical study. The open-hardware part of the system is composed of a pair of triaxial digital accelerometers, a microprocessor, a Bluetooth module and a rechargeable battery unit. The reception of the transmitted acceleration data, visualization, interactive sensor alignment, angle estimation and data-logging are realized by the developed open-source graphical user interface. The system is tested on a tripod for verification and on a subject for practical demonstration. Developed system can be constructed and used for ambulatory monitoring and analysis of relative head-torso angle. Open-source user interface can be downloaded and developed for further (different) algorithms and device hardware.

Finite Class Bayesian Inference System for Circle and Linear Walking Gait Event Recognition Using Inertial Measurement Units

Accurate and fast human motion pattern recognition is the key to ensuring lower limb assistive devices' appropriate assistance. The research on human motion pattern recognition of lower limb assistive devices mainly focuses on sagittal gait. The motion pattern such as circular walking (CW) is asymmetric about the sagittal plane of the body. CW is common in daily living. However, the recognition algorithm of CW is rarely reported. Since lower limb assistive devices interact with humans, lacking the capability of recognizing CW is dangerous. Thus, to realize the accurate and fast recognition of CW, this article proposed a finite class Bayesian interference system (FC-BesIS). FC-BesIS is designed to recognize walking activities (linear walking and CW) and gait events (heel contact, load response, mid stance, terminal stance, pre-swing, initial swing, mid swing, and terminal swing). A finite class method which reduces the number of potential classes according to elimination rules before decision-making is introduced. Elimination rules are designed based on likelihood estimation and sensor information. The experiments show that walking activities and gait events can be accurately and fastly recognized by FC-BesIS. The experiments also show that the performance of FC-BesIS in mean recognition accuracy (MRA) and mean decision time (MDT) is improved compared with BesIS. The MRA of walking activities and gait events are 100% and 97.38%, respectively. The MDT of walking activities and gait events are 28.19 ms and 33.94 ms, respectively. Overall, FC-BesIS has been proved to be an accurate and fast recognition algorithm for human motion patterns using wearable sensors.

Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis

BACKGROUND

Inertial measurement units (IMUs) offer the ability to measure walking gait through a variety of biomechanical outcomes (e.g., spatiotemporal, kinematics, other). Although many studies have assessed their validity and reliability, there remains no quantitive summary of this vast body of literature. Therefore, we aimed to conduct a systematic review and meta-analysis to determine the i) concurrent validity and ii) test-retest reliability of IMUs for measuring biomechanical gait outcomes during level walking in healthy adults.

METHODS

Five electronic databases were searched for journal articles assessing the validity or reliability of IMUs during healthy adult walking. Two reviewers screened titles, abstracts, and full texts for studies to be included, before two reviewers examined the methodological quality of all included studies. When sufficient data were present for a given biomechanical outcome, data were meta-analyzed on Pearson correlation coefficients (r) or intraclass correlation coefficients (ICC) for validity and reliability, respectively. Alternatively, qualitative summaries of outcomes were conducted on those that could not be meta-analyzed.

RESULTS

A total of 82 articles, assessing the validity or reliability of over 100 outcomes, were included in this review. Seventeen biomechanical outcomes, primarily spatiotemporal parameters, were meta-analyzed. The validity and reliability of step and stride times were found to be excellent. Similarly, the validity and reliability of step and stride length, as well as swing and stance time, were found to be good to excellent. Alternatively, spatiotemporal parameter variability and symmetry displayed poor to moderate validity and reliability. IMUs were also found to display moderate reliability for the assessment of local dynamic stability during walking. The remaining biomechanical outcomes were qualitatively summarized to provide a variety of recommendations for future IMU research.

CONCLUSIONS

The findings of this review demonstrate the excellent validity and reliability of IMUs for mean spatiotemporal parameters during walking, but caution the use of spatiotemporal variability and symmetry metrics without strict protocol. Further, this work tentatively supports the use of IMUs for joint angle measurement and other biomechanical outcomes such as stability, regularity, and segmental accelerations. Unfortunately, the strength of these recommendations are limited based on the lack of high-quality studies for each outcome, with underpowered and/or unjustified sample sizes (sample size median 12; range: 2-95) being the primary limitation.

Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion

Aiming at the requirement of rapid recognition of the wearer's gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.

Inertial measurement unit compared to an optical motion capturing system in post-stroke individuals with foot-drop syndrome

BACKGROUND

Functional electrical stimulation (FES) can be used for compensation of foot-drop for post-stroke individuals by pre-programmed fixed stimulation; however, this stimulation seems no more effective than mechanical ankle foot orthoses.

OBJECTIVE

We evaluated the metrological quality of inertial sensors for movement reconstruction as compared with the gold-standard motion capturing system, to couple FES with inertial sensors to improve dorsiflexion on the paretic side, by using an adaptive stimulation taking into account individuals' performance post-stroke.

METHODS

Adults with ischemic or hemorrhagic stroke presenting foot-drop and able to walk 10m, were included from May 2016 to June 2017. Those with passive ankle dorsiflexion<0° with the knee stretched were excluded. Synchronous gait was analyzed with the VICON© system as the gold standard and inertial measurement units (IMUs) worn by participants. The main outcome was the dorsiflexion angle at the heel strike and mid-swing phase obtained from IMUs and the VICON system. Secondary outcomes were: stride length, walking speed, maximal ankle dorsiflexion velocity and fatigue detection.

RESULTS

We included 26 participants [18 males; mean age 58 (range 45-84) years]. During heel strike, the dorsiflexion angle measurements demonstrated a root mean square error (RMSE) of 5.5°; a mean average error (MAE) of 3.9°; Bland-Altman bias of -0.1° with limits of agreement -10.9° to+10.7° and good intra-class correlation coefficient (ICC) at 0.87 between the 2 techniques. During the mid-swing phase, the RMSE was 5.6; MAE 3.7°; Bland-Altman bias -0.9° with limits of agreement -11.7° to+9.8° and ICC 0.88. Good agreement was demonstrated for secondary outcomes and fatigue detection.

CONCLUSIONS

IMU-based reconstruction algorithms were effective in measuring ankle dorsiflexion with small biases and good ICCs in adults with ischemic or hemorrhagic stroke presenting foot-drop. The precision obtained is sufficient to observe the fatigue influence on the dorsiflexion and therefore to use IMUs to adapt FES.

The utility of normative foot floor angle data in assessing toe-walking

Initial heel contact is an important attribute of gait, and failure to complete the heel rocker reduces gait stability. One common goal in treating toe-walking is to restore heel strike and prevent or reduce early heel rise. Foot floor angle (FFA) is a measure of toe-walking that is valuable for quantifying foot orientation at initial contact when using ankle dorsiflexion angle alone is misleading. However, no age-standardized FFA norms exist for clinical evaluation. Our objectives were to: (1) obtain normative FFA in typically developing children; and (2) examine its utility in the example of toe-walking secondary to unilateral cerebral palsy. Gait kinematics were acquired and FFA trajectories computed for 80 typically developing children (4-18 years). They were also obtained retrospectively from 11 children with toe-walking secondary to unilateral cerebral palsy (4-10 years), before and after operative intervention, and compared to 40 age-matched, typically developing children. FFA at initial contact was significantly different (P<.001) between pre-surgery toe-walking (-14.7±9.7°; mean±standard deviation) and typical gait (18.7±2.8°). Following operative lengthening of the gastrocnemius-soleus complex on the affected side, FFA at initial contact (-0.9±5.3°) was significantly improved (P<.001). Furthermore, several cases were identified for which the sole use of ankle dorsiflexion angle to capture toe-walking is misleading. The assessment of FFA is a simple method for providing valuable quantitative information to clinicians regarding foot orientation during gait. The demonstrated limitations of using ankle dorsiflexion angle alone to estimate foot orientation further emphasize the utility of FFA in assessing toe-walking.

Online Wavelet Complementary velocity Estimator

In this paper, we have proposed a new online Wavelet Complementary velocity Estimator (WCE) over position and acceleration data gathered from an electro hydraulic servo shaking table. This is a batch estimator type that is based on the wavelet filter banks which extract the high and low resolution of data. The proposed complementary estimator combines these two resolutions of velocities which acquired from numerical differentiation and integration of the position and acceleration sensors by considering a fixed moving horizon window as input to wavelet filter. Because of using wavelet filters, it can be implemented in a parallel procedure. By this method the numerical velocity is estimated without having high noise of differentiators, integration drifting bias and with less delay which is suitable for active vibration control in high precision Mechatronics systems by Direct Velocity Feedback (DVF) methods. This method allows us to make velocity sensors with less mechanically moving parts which makes it suitable for fast miniature structures. We have compared this method with Kalman and Butterworth filters over stability, delay and benchmarked them by their long time velocity integration for getting back the initial position data.

A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.